http://2007.igem.org/wiki/index.php?title=Special:Contributions/LiNan&feed=atom&limit=50&target=LiNan&year=&month=2007.igem.org - User contributions [en]2024-03-29T09:44:50ZFrom 2007.igem.orgMediaWiki 1.16.5http://2007.igem.org/wiki/index.php/Nan_LiNan Li2008-05-23T15:34:31Z<p>LiNan: </p>
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<div><!--- --[[User:LiNan|LiNan]] 07:51, 24 October 2007 (EDT) ---><br />
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<h1><font face="Arial" size="4" color="#000099"><i>About Me:</i></font></h1><br />
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[[Image:Nan_Li.jpg|left|120px]]<br />
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<p>I grew up in China and got my Bachelor degree there. <br />
Right now I am doing my Master study in Biomedical Engineering at<br />
<a href="http://www.ethz.ch" title="Eidgenössische Technische Hochschule Zürich since 1854">ETH Zurich</a>. I remain my study interest in various topics, however, <IMG height=22 src="https://static.igem.org/mediawiki/2007/4/49/Educateth_Ecoli_b.png" width=150></a> currently ranks the first.<span lang="EN-US" style="font-family: Wingdings">J</span></p><br />
<p><i><span style="font-family: Arial">&quot;A painter paints pictures on canvas; but <br />
musicians paint their pictures on silence.</i>&quot; During my spare time, I enjoy the painting on silence<br />
a lot. Besides, I like hiking with friends and traveling around. The only means <br />
to keep my life refreshing is to experience new things. Moreover,<br />
seal engraving always fascinates <br />
me.</span></p><br />
<p>&nbsp;</p><br />
<h1><font face="Arial" size="4" color="#000099"><i>Contact <br />
Information:</i></font></h1><br />
<ul><br />
<li><b>Email:</b>&nbsp;&nbsp;leemnan AT gmail DOT com</li><br />
</ul><br />
<br />
<p>&nbsp;</p><br />
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*[http://leemnan.spaces.live.com Personal Blog] <br />
*[[ETHZ | ETH Zürich iGEM 2007]] <br />
*[https://2006.igem.org/wiki/index.php/ETH_Zurich_2006 ETH Zürich iGEM 2006]<br />
*[https://2006.igem.org/wiki/index.php/ETH_Zurich_2005 ETH Zürich iGEM 2005]<br />
|style="text-align: center;width:500px; valign:center"|[[Image:NJZY.png|right|151px]]<br />
|}</div>LiNanhttp://2007.igem.org/wiki/index.php/Nan_LiNan Li2008-02-21T15:11:04Z<p>LiNan: </p>
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<div><!--- --[[User:LiNan|LiNan]] 07:51, 24 October 2007 (EDT) ---><br />
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<h1><font face="Arial" size="4" color="#000099"><i>About Me:</i></font></h1><br />
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[[Image:Nan_Li.jpg|left|120px]]<br />
<html><br />
<p>I grew up in China and got my Bachelor degree there. <br />
Right now I am doing my Master study in Biomedical Engineering at<br />
<a href="http://www.ethz.ch" title="Eidgenössische Technische Hochschule Zürich since 1854">ETH Zurich</a>. I remain my study interest in various topics, however, <IMG height=22 src="https://static.igem.org/mediawiki/2007/4/49/Educateth_Ecoli_b.png" width=150></a> currently ranks the first.<span lang="EN-US" style="font-family: Wingdings">J</span></p><br />
<p><i><span style="font-family: Arial">&quot;A painter paints pictures on canvas; but <br />
musicians paint their pictures on silence.</i>&quot; During my spare time, I enjoy the painting on silence<br />
a lot. Besides, I like hiking with friends and traveling around. The only means <br />
to keep my life refreshing is to experience new things. Moreover,<br />
<a href="http://zh.wikipedia.org/wiki/篆刻" title="Fantastic stone and calligraphy art...">seal engraving</a> always fascinates <br />
me.</span></p><br />
<p>&nbsp;</p><br />
<h1><font face="Arial" size="4" color="#000099"><i>Contact <br />
Information:</i></font></h1><br />
<ul><br />
<li><b>Email:</b>&nbsp;&nbsp;leemnan AT gmail DOT com</li><br />
</ul><br />
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<p>&nbsp;</p><br />
<h1><font face="Arial" size="4" color="#000099"><i>Links:</i></font></h1><br />
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*[http://leemnan.spaces.live.com Personal Blog] <br />
*[[ETHZ | ETH Zürich iGEM 2007]] <br />
*[https://2006.igem.org/wiki/index.php/ETH_Zurich_2006 ETH Zürich iGEM 2006]<br />
*[https://2006.igem.org/wiki/index.php/ETH_Zurich_2005 ETH Zürich iGEM 2005]<br />
|style="text-align: center;width:500px; valign:center"|[[Image:NJZY.png|right|151px]]<br />
|}</div>LiNanhttp://2007.igem.org/wiki/index.php/Nan_LiNan Li2008-02-03T19:18:41Z<p>LiNan: </p>
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<div><!--- --[[User:LiNan|LiNan]] 07:51, 24 October 2007 (EDT) ---><br />
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[[Image:Nan_Li.jpg|left|120px]]<br />
<html><br />
<p>I grew up in China and got my Bachelor degree there. <br />
Right now I am doing my Master study in Biomedical Engineering at<br />
<a href="http://www.ethz.ch" title="Eidgenössische Technische Hochschule Zürich since 1854">ETH Zurich</a>. I remain my study interest in various topics, however, <IMG height=22 src="https://static.igem.org/mediawiki/2007/4/49/Educateth_Ecoli_b.png" width=150></a> currently ranks the first.<span lang="EN-US" style="font-family: Wingdings">J</span></p><br />
<p><i><span style="font-family: Arial">&quot;A painter paints pictures on canvas; but <br />
musicians paint their pictures on silence.</i>&quot; During my spare time, I enjoy the painting on silence<br />
a lot. Besides, I like hiking with friends and traveling around. The only means <br />
to keep my life refreshing is to experience new things. Moreover,<br />
<a href="http://zh.wikipedia.org/wiki/篆刻" title="Fantastic stone and calligraphy art...">seal engraving</a> always fascinates <br />
me.</span></p><br />
<p>&nbsp;</p><br />
<h1><font face="Arial" size="4" color="#000099"><i>Contact <br />
Information:</i></font></h1><br />
<ul><br />
<li><b>Email:</b>&nbsp;&nbsp;leemnan AT gmail DOT com</li><br />
</ul><br />
<br />
<p>&nbsp;</p><br />
<h1><font face="Arial" size="4" color="#000099"><i>Links:</i></font></h1><br />
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*[[ETHZ | ETH Zürich iGEM 2007]] <br />
*[https://2006.igem.org/wiki/index.php/ETH_Zurich_2006 ETH Zürich iGEM 2006]<br />
*[https://2006.igem.org/wiki/index.php/ETH_Zurich_2005 ETH Zürich iGEM 2005]<br />
|style="text-align: center;width:500px; valign:center"|[[Image:NJZY.png|right|151px]]<br />
|}</div>LiNanhttp://2007.igem.org/wiki/index.php/File:ETHZiGEM07.jpgFile:ETHZiGEM07.jpg2007-10-29T15:44:53Z<p>LiNan: ETHZ iGEM07 Team picture (655 X 222)</p>
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<div>ETHZ iGEM07 Team picture (655 X 222)</div>LiNanhttp://2007.igem.org/wiki/index.php/Nan_LiNan Li2007-10-26T20:20:52Z<p>LiNan: </p>
<hr />
<div><!--- --[[User:LiNan|LiNan]] 07:51, 24 October 2007 (EDT) ---><br />
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<body background="https://static.igem.org/mediawiki/2007/c/c0/Igem2007logo_2.png" style="background-attachment: fixed" ><br />
<p align="center"><font face="Arial"><br />
</font></p><br />
<h1><font face="Arial" size="4" color="#000099"><i>About Me:</i></font></h1><br />
<p>I grew up in China and got my Bachelor degree there. <br />
Right now I am doing my Master study in Biomedical Engineering at<br />
<a href="http://www.ethz.ch" title="Eidgenössische Technische Hochschule Zürich since 1854">ETH Zurich</a>. I remain my study interest in various topics, however, <IMG height=22 src="https://static.igem.org/mediawiki/2007/4/49/Educateth_Ecoli_b.png" width=150></a> currently ranks the first.<span lang="EN-US" style="font-family: Wingdings">J</span></p><br />
<p><i><span style="font-family: Arial">&quot;A painter paints pictures on canvas; but <br />
musicians paint their pictures on silence.</i>&quot; During my spare time, I enjoy the painting on silence<br />
a lot. Besides, I like hiking with friends and traveling around. The only means <br />
to keep my life refreshing is to experience new things. Moreover,<br />
<a href="http://zh.wikipedia.org/wiki/篆刻" title="Fantastic stone and calligraphy art...">seal engraving</a> always fascinates <br />
me.</span></p><br />
<p>&nbsp;</p><br />
<h1><font face="Arial" size="4" color="#000099"><i>Contact <br />
Information:</i></font></h1><br />
<ul><br />
<li><b>Email:</b>&nbsp;&nbsp;leemnan AT gmail DOT com</li><br />
</ul><br />
<br />
<p>&nbsp;</p><br />
<h1><font face="Arial" size="4" color="#000099"><i>Links:</i></font></h1><br />
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*[[ETHZ | ETH Zürich iGEM 2007]] <br />
*[https://2006.igem.org/wiki/index.php/ETH_Zurich_2006 ETH Zürich iGEM 2006]<br />
*[https://2006.igem.org/wiki/index.php/ETH_Zurich_2005 ETH Zürich iGEM 2005]<br />
|style="text-align: center;width:500px; valign:center"|[[Image:NJZY.png|right|151px]]<br />
|}</div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZETHZ2007-10-26T20:03:51Z<p>LiNan: /* Intelligent Biosensors and Self - Adaptation */</p>
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<p align="center"><font size="6"><b>ETH Zurich - educatETH <i>E.coli</i> System</b></font></p><br />
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=Introduction=<br />
<blockquote>"All <i>E.coli</i> 's are equal, but some <i>E.coli</i> 's are more equal than others..." ''(freely adapted from "Animal Farm" by George Orwell)''</blockquote> <br />
<br />
... this is what George Orwell would have written, were he a synthetic biologist. In the <i>E.coli</i> colonies on petri dishes, all bacteria are equal; except for some special ones. Our project is about designing such special <i>E.coli</i> that are "more equal" than the rest: they have the ability to be trained in order to memorize and recognize their environment in the future. Their story will be presented through this wiki ...<br />
<br />
==Motivation==<br />
<br />
[[Image:ethz_main_pic.jpg|right|thumb|<b>Fig. 1</b>: Artist's approach to the different stages of the development. We started by modeling and simulating the system. We continued by specifying the DNA strands for its implementation. Finally in the end, our system should report with different fluorescent proteins (image edited)|600px]]<br />
<br />
Our combined team of biologists and engineers is coping with the problem of implementing memory capabilities in bacterial colonies. First, ''E.coli'' are able to respond differently (with distinct fluorescent proteins) to two different inputs (we used chemicals). Second, they remember which input was presented to them. Finally, when confronted with a new input, they are able to recognize whether it is the one that they were trained with or not. <br />
In other words, in this project '''we are educating the <i>E.coli</i>'''!<br />
<br />
===Multipurpose Cell Lines===<br />
<br />
Our system has the ability to behave in different ways according to an internal toggle inside it switching states based on the chemical substances that the system is exposed to. The toggle states could generally be used to trigger events such as enzyme synthesis, transcriptional regulation, virion production, or even cell death. Therefore, one may view the bacterial cell line containing this system as a multipurpose cell line. By adding a certain chemical to a cell line, the latter may be trained to exhibit a desired behavior, and then it is not necessary any more to construct two independent cell lines. <br />
<br />
This means that one applies an “input engineering” instead of a “DNA engineering” approach. If one extends this idea to several inducible toggle switches being harbored in the same cell line, the number of possible phenotypes increases to 2<i><sup>n</sup></i>, where <i>n</i> equals the number of toggle switches. For example, if one would have 5 such toggle switches inside a cell line, 32 different behavior patterns would be possible. <br />
<br />
For the purpose of creating a toggle switch that is activated in a specific phase only and not always (a multi-inducible toggle switch), as is required for stable biological automatons, we introduced the concept of [https://2007.igem.org/ETHZ/Biology/parts#double_promoters double promoters] to the [http://partsregistry.org/Main_Page Registry of Standard Biological Parts], which can be helpful for engineering systems which exhibit a desired behavior only at specific times. As a concept, double promoters are expandable to multiple promoter sites - this, however, needs further investigation.<br />
<br />
===Link to Epigenetics===<br />
Epigenetics refers to features like chromatin or DNA modifications that do not involve changes in the underlying DNA sequence and are stable over many cell divisions [1],[2]. If one has a closer look at our proposed system, one can also view it as a model-system for epigenetics: Although the DNA sequence itself stays the same, two different subpopulations of cells with different phenotypes can develop from it. Put simply, depending in which state (subpopulation) the toggle switch is, the cells will produce different fluorescent proteins upon addition of two different inducer molecules. Therefore, the epigenetic feature here is the binding of specific repressor proteins whose production is dependent on the toggle switch state.<br />
<br />
===Intelligent Biosensors and Self-Adaptation===<br />
<br />
The system is capable of sensing different chemicals and producing different fluorescent proteins. Since the cells can be trained to produce one of several specific fluorescent protein types when a certain chemical is present, one can also view those cells as intelligent biosensors which recognize chemical substances according to a training phase. The intelligent biosensors are not limited to detect chemicals; temperature, pH, light, pressure could be detected with an appropriate system as well. Such an application could be especially of interest when the environment to be probed is harmful for humans, for example due to high toxicity.<br />
<br />
==Team Members==<br />
[[Image:ETHZ_Group_photo_6.png|right|thumb|The ETH Zurich iGEM2007 Team|270px]]<br />
<br />
The ETH Zurich team consists of a good mixture between biologists and engineering students. <br />
We are:<br />
<ul> <br />
<li><i>Undergraduate students</i>: <br><br />
[https://2007.igem.org/User:brutsche Martin Brutsche], [https://2007.igem.org/User:kdikaiou Katerina Dikaiou], <br>[https://2007.igem.org/User:Raphael Raphael Guebeli], [https://2007.igem.org/User:hoehnels Sylke Hoehnel], <br><br />
[https://2007.igem.org/Nan_Li Nan Li], <br />
[https://2007.igem.org/User:Stefan Stefan Luzi]<br />
<li><i>Graduate students</i>: <br><br />
[http://christos.bergeles.net Christos Bergeles], <br />
[http://www.tik.ee.ethz.ch/~sop/people/thohm/ Tim Hohm], <br><br />
[http://www.fussenegger.ethz.ch/people/kemmerc Christian Kemmer], <br />
[https://2007.igem.org/User:JoeKnight Joseph Knight], <br><br />
[http://www.ricomoeckel.de Rico Möckel], <br />
[http://csb.inf.ethz.ch/people/uhr.html Markus Uhr]<br />
<li><i>Project advisors</i>: <br><br />
[http://www.ipe.ethz.ch/laboratories/bpl/people/panke Sven Panke], <br><br />
[http://csb.inf.ethz.ch/people/stelling.html Joerg Stelling]<br />
</ul><br />
<br />
For more information about us, visit our [[ETHZ/Meet_the_team | Meet the Team]] page.<br />
<br />
==Acknowledgments==<br />
The idea for the project as well as its implementation was done solely by the ETH iGEM 2007 team. We would like to thank the people in [http://www.ipe.ethz.ch/laboratories/bpl/index Sven Panke's Lab], especially Andreas Meyer who was always there for us when we had a problem. Additionally, we would like to thank [http://www.facs.ethz.ch Alfredo Franco-Obregón's lab] and Oralea Büchi for the help with the flow cytometry.<br />
<br />
We would also like to acknowledge the financial support by [http://europa.eu EU], the [http://www.ethz.ch ETH Zurich], and [http://www.geneart.com GeneArt]:<br />
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==Site Map==<br />
<br />
In this wiki, we will present to you a detailed description of the proposed system: starting with the [[ETHZ/Model | modeling of the system]], we describe both, [[ETHZ/Simulation | simulations and theoretical considerations]] of the system, as well as the actual [[ETHZ/Biology | implementation using bio-bricks]] accompanied by our [[ETHZ/Biology/Lab | lab notes]]. Additionally, you will find further [[ETHZ/Meet_the_team | information on the team]], more details about [[ETHZ/Internal | ideas we developed]] before we came up with the system we finally implemented, and some [[ETHZ/Pictures | pictures]] documenting our work.<br />
<br />
The site map of our wiki is the following:<br />
<br />
{| class="wikitable" width="100%" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
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!width="22%"| ETHZ Team Pages<br />
!width="22%"| Links<br />
|-<br />
| [[ETHZ/Model | Modeling of the learning system]]<br />
| [[ETHZ/Biology | Biological implementation]]<br />
| [[ETHZ/Meet_the_team | Team page]]<br />
| [https://2006.igem.org/wiki/index.php/ETH_Zurich_2005 The ETH Zurich 2005 project]<br />
|-<br />
| [[ETHZ/FlipFlop | Representation using flip-flops]]<br />
| [[ETHZ/Biology/parts | Biobricks/parts]]<br />
| [[ETHZ/Pictures | Pictures]]<br />
| [https://2006.igem.org/wiki/index.php/ETH_Zurich_2006 The ETH Zurich 2006 project]<br />
|-<br />
| [[ETHZ/FSM | Representation using finite state machines]]<br />
| [[ETHZ/Biology/Lab | Lab notes]]<br />
| [[ETHZ/Internal | Brainstorming sessions]]<br />
| <br />
|-<br />
| [[ETHZ/Simulation | Model simulations and theoretical considerations]]<br />
| <br />
| <br />
| <br />
|-<br />
| [[ETHZ/Parameters | Parameters used in our simulations]]<br />
| <br />
| <br />
| <br />
|-<br />
|}<br />
<br />
== References ==<br />
<br />
[http://www.nature.com/nature/journal/v447/n7143/abs/nature05913.html;jsessionid=62903C604764B175945C03DB8639ECBD &#91;1&#93; Bird A] <i>"Perceptions of epigenetics"</i>, Nature 447:396-398, 2007 <br /><br />
[http://linkinghub.elsevier.com/retrieve/pii/S096098220701007X &#91;2&#93; Ptashne M] <i>"On the use of the word ‘epigenetic’"</i>, Current Biology 17(7):R233-R236, 2007 <br /><br />
<br />
<br />
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</body></html></div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZETHZ2007-10-26T20:03:30Z<p>LiNan: /* Intelligent Biosensors and Self-Adaptation */</p>
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<p align="center"><font size="6"><b>ETH Zurich - educatETH <i>E.coli</i> System</b></font></p><br />
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<br />
=Introduction=<br />
<blockquote>"All <i>E.coli</i> 's are equal, but some <i>E.coli</i> 's are more equal than others..." ''(freely adapted from "Animal Farm" by George Orwell)''</blockquote> <br />
<br />
... this is what George Orwell would have written, were he a synthetic biologist. In the <i>E.coli</i> colonies on petri dishes, all bacteria are equal; except for some special ones. Our project is about designing such special <i>E.coli</i> that are "more equal" than the rest: they have the ability to be trained in order to memorize and recognize their environment in the future. Their story will be presented through this wiki ...<br />
<br />
==Motivation==<br />
<br />
[[Image:ethz_main_pic.jpg|right|thumb|<b>Fig. 1</b>: Artist's approach to the different stages of the development. We started by modeling and simulating the system. We continued by specifying the DNA strands for its implementation. Finally in the end, our system should report with different fluorescent proteins (image edited)|600px]]<br />
<br />
Our combined team of biologists and engineers is coping with the problem of implementing memory capabilities in bacterial colonies. First, ''E.coli'' are able to respond differently (with distinct fluorescent proteins) to two different inputs (we used chemicals). Second, they remember which input was presented to them. Finally, when confronted with a new input, they are able to recognize whether it is the one that they were trained with or not. <br />
In other words, in this project '''we are educating the <i>E.coli</i>'''!<br />
<br />
===Multipurpose Cell Lines===<br />
<br />
Our system has the ability to behave in different ways according to an internal toggle inside it switching states based on the chemical substances that the system is exposed to. The toggle states could generally be used to trigger events such as enzyme synthesis, transcriptional regulation, virion production, or even cell death. Therefore, one may view the bacterial cell line containing this system as a multipurpose cell line. By adding a certain chemical to a cell line, the latter may be trained to exhibit a desired behavior, and then it is not necessary any more to construct two independent cell lines. <br />
<br />
This means that one applies an “input engineering” instead of a “DNA engineering” approach. If one extends this idea to several inducible toggle switches being harbored in the same cell line, the number of possible phenotypes increases to 2<i><sup>n</sup></i>, where <i>n</i> equals the number of toggle switches. For example, if one would have 5 such toggle switches inside a cell line, 32 different behavior patterns would be possible. <br />
<br />
For the purpose of creating a toggle switch that is activated in a specific phase only and not always (a multi-inducible toggle switch), as is required for stable biological automatons, we introduced the concept of [https://2007.igem.org/ETHZ/Biology/parts#double_promoters double promoters] to the [http://partsregistry.org/Main_Page Registry of Standard Biological Parts], which can be helpful for engineering systems which exhibit a desired behavior only at specific times. As a concept, double promoters are expandable to multiple promoter sites - this, however, needs further investigation.<br />
<br />
===Link to Epigenetics===<br />
Epigenetics refers to features like chromatin or DNA modifications that do not involve changes in the underlying DNA sequence and are stable over many cell divisions [1],[2]. If one has a closer look at our proposed system, one can also view it as a model-system for epigenetics: Although the DNA sequence itself stays the same, two different subpopulations of cells with different phenotypes can develop from it. Put simply, depending in which state (subpopulation) the toggle switch is, the cells will produce different fluorescent proteins upon addition of two different inducer molecules. Therefore, the epigenetic feature here is the binding of specific repressor proteins whose production is dependent on the toggle switch state.<br />
<br />
===Intelligent Biosensors and Self - Adaptation===<br />
<br />
The system is capable of sensing different chemicals and producing different fluorescent proteins. Since the cells can be trained to produce one of several specific fluorescent protein types when a certain chemical is present, one can also view those cells as intelligent biosensors which recognize chemical substances according to a training phase. The intelligent biosensors are not limited to detect chemicals; temperature, pH, light, pressure could be detected with an appropriate system as well. Such an application could be especially of interest when the environment to be probed is harmful for humans, for example due to high toxicity.<br />
<br />
==Team Members==<br />
[[Image:ETHZ_Group_photo_6.png|right|thumb|The ETH Zurich iGEM2007 Team|270px]]<br />
<br />
The ETH Zurich team consists of a good mixture between biologists and engineering students. <br />
We are:<br />
<ul> <br />
<li><i>Undergraduate students</i>: <br><br />
[https://2007.igem.org/User:brutsche Martin Brutsche], [https://2007.igem.org/User:kdikaiou Katerina Dikaiou], <br>[https://2007.igem.org/User:Raphael Raphael Guebeli], [https://2007.igem.org/User:hoehnels Sylke Hoehnel], <br><br />
[https://2007.igem.org/Nan_Li Nan Li], <br />
[https://2007.igem.org/User:Stefan Stefan Luzi]<br />
<li><i>Graduate students</i>: <br><br />
[http://christos.bergeles.net Christos Bergeles], <br />
[http://www.tik.ee.ethz.ch/~sop/people/thohm/ Tim Hohm], <br><br />
[http://www.fussenegger.ethz.ch/people/kemmerc Christian Kemmer], <br />
[https://2007.igem.org/User:JoeKnight Joseph Knight], <br><br />
[http://www.ricomoeckel.de Rico Möckel], <br />
[http://csb.inf.ethz.ch/people/uhr.html Markus Uhr]<br />
<li><i>Project advisors</i>: <br><br />
[http://www.ipe.ethz.ch/laboratories/bpl/people/panke Sven Panke], <br><br />
[http://csb.inf.ethz.ch/people/stelling.html Joerg Stelling]<br />
</ul><br />
<br />
For more information about us, visit our [[ETHZ/Meet_the_team | Meet the Team]] page.<br />
<br />
==Acknowledgments==<br />
The idea for the project as well as its implementation was done solely by the ETH iGEM 2007 team. We would like to thank the people in [http://www.ipe.ethz.ch/laboratories/bpl/index Sven Panke's Lab], especially Andreas Meyer who was always there for us when we had a problem. Additionally, we would like to thank [http://www.facs.ethz.ch Alfredo Franco-Obregón's lab] and Oralea Büchi for the help with the flow cytometry.<br />
<br />
We would also like to acknowledge the financial support by [http://europa.eu EU], the [http://www.ethz.ch ETH Zurich], and [http://www.geneart.com GeneArt]:<br />
<center><br />
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| [http://europa.eu http://www.tik.ee.ethz.ch/~thohm/EU.gif]<br />
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|}<br />
</center><br />
<br />
==Site Map==<br />
<br />
In this wiki, we will present to you a detailed description of the proposed system: starting with the [[ETHZ/Model | modeling of the system]], we describe both, [[ETHZ/Simulation | simulations and theoretical considerations]] of the system, as well as the actual [[ETHZ/Biology | implementation using bio-bricks]] accompanied by our [[ETHZ/Biology/Lab | lab notes]]. Additionally, you will find further [[ETHZ/Meet_the_team | information on the team]], more details about [[ETHZ/Internal | ideas we developed]] before we came up with the system we finally implemented, and some [[ETHZ/Pictures | pictures]] documenting our work.<br />
<br />
The site map of our wiki is the following:<br />
<br />
{| class="wikitable" width="100%" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
!width="34%"| Modeling Pages <br />
!width="22%"| Biology Pages<br />
!width="22%"| ETHZ Team Pages<br />
!width="22%"| Links<br />
|-<br />
| [[ETHZ/Model | Modeling of the learning system]]<br />
| [[ETHZ/Biology | Biological implementation]]<br />
| [[ETHZ/Meet_the_team | Team page]]<br />
| [https://2006.igem.org/wiki/index.php/ETH_Zurich_2005 The ETH Zurich 2005 project]<br />
|-<br />
| [[ETHZ/FlipFlop | Representation using flip-flops]]<br />
| [[ETHZ/Biology/parts | Biobricks/parts]]<br />
| [[ETHZ/Pictures | Pictures]]<br />
| [https://2006.igem.org/wiki/index.php/ETH_Zurich_2006 The ETH Zurich 2006 project]<br />
|-<br />
| [[ETHZ/FSM | Representation using finite state machines]]<br />
| [[ETHZ/Biology/Lab | Lab notes]]<br />
| [[ETHZ/Internal | Brainstorming sessions]]<br />
| <br />
|-<br />
| [[ETHZ/Simulation | Model simulations and theoretical considerations]]<br />
| <br />
| <br />
| <br />
|-<br />
| [[ETHZ/Parameters | Parameters used in our simulations]]<br />
| <br />
| <br />
| <br />
|-<br />
|}<br />
<br />
== References ==<br />
<br />
[http://www.nature.com/nature/journal/v447/n7143/abs/nature05913.html;jsessionid=62903C604764B175945C03DB8639ECBD &#91;1&#93; Bird A] <i>"Perceptions of epigenetics"</i>, Nature 447:396-398, 2007 <br /><br />
[http://linkinghub.elsevier.com/retrieve/pii/S096098220701007X &#91;2&#93; Ptashne M] <i>"On the use of the word ‘epigenetic’"</i>, Current Biology 17(7):R233-R236, 2007 <br /><br />
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</body></html></div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZETHZ2007-10-26T19:56:11Z<p>LiNan: /* Multipurpose Cell Lines */</p>
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<p align="center"><font size="6"><b>ETH Zurich - educatETH <i>E.coli</i> System</b></font></p><br />
</body><br />
</html><br />
<br />
=Introduction=<br />
<blockquote>"All <i>E.coli</i> 's are equal, but some <i>E.coli</i> 's are more equal than others..." ''(freely adapted from "Animal Farm" by George Orwell)''</blockquote> <br />
<br />
... this is what George Orwell would have written, were he a synthetic biologist. In the <i>E.coli</i> colonies on petri dishes, all bacteria are equal; except for some special ones. Our project is about designing such special <i>E.coli</i> that are "more equal" than the rest: they have the ability to be trained in order to memorize and recognize their environment in the future. Their story will be presented through this wiki ...<br />
<br />
==Motivation==<br />
<br />
[[Image:ethz_main_pic.jpg|right|thumb|<b>Fig. 1</b>: Artist's approach to the different stages of the development. We started by modeling and simulating the system. We continued by specifying the DNA strands for its implementation. Finally in the end, our system should report with different fluorescent proteins (image edited)|600px]]<br />
<br />
Our combined team of biologists and engineers is coping with the problem of implementing memory capabilities in bacterial colonies. First, ''E.coli'' are able to respond differently (with distinct fluorescent proteins) to two different inputs (we used chemicals). Second, they remember which input was presented to them. Finally, when confronted with a new input, they are able to recognize whether it is the one that they were trained with or not. <br />
In other words, in this project '''we are educating the <i>E.coli</i>'''!<br />
<br />
===Multipurpose Cell Lines===<br />
<br />
Our system has the ability to behave in different ways according to an internal toggle inside it switching states based on the chemical substances that the system is exposed to. The toggle states could generally be used to trigger events such as enzyme synthesis, transcriptional regulation, virion production, or even cell death. Therefore, one may view the bacterial cell line containing this system as a multipurpose cell line. By adding a certain chemical to a cell line, the latter may be trained to exhibit a desired behavior, and then it is not necessary any more to construct two independent cell lines. <br />
<br />
This means that one applies an “input engineering” instead of a “DNA engineering” approach. If one extends this idea to several inducible toggle switches being harbored in the same cell line, the number of possible phenotypes increases to 2<i><sup>n</sup></i>, where <i>n</i> equals the number of toggle switches. For example, if one would have 5 such toggle switches inside a cell line, 32 different behavior patterns would be possible. <br />
<br />
For the purpose of creating a toggle switch that is activated in a specific phase only and not always (a multi-inducible toggle switch), as is required for stable biological automatons, we introduced the concept of [https://2007.igem.org/ETHZ/Biology/parts#double_promoters double promoters] to the [http://partsregistry.org/Main_Page Registry of Standard Biological Parts], which can be helpful for engineering systems which exhibit a desired behavior only at specific times. As a concept, double promoters are expandable to multiple promoter sites - this, however, needs further investigation.<br />
<br />
===Link to Epigenetics===<br />
Epigenetics refers to features like chromatin or DNA modifications that do not involve changes in the underlying DNA sequence and are stable over many cell divisions [1],[2]. If one has a closer look at our proposed system, one can also view it as a model-system for epigenetics: Although the DNA sequence itself stays the same, two different subpopulations of cells with different phenotypes can develop from it. Put simply, depending in which state (subpopulation) the toggle switch is, the cells will produce different fluorescent proteins upon addition of two different inducer molecules. Therefore, the epigenetic feature here is the binding of specific repressor proteins whose production is dependent on the toggle switch state.<br />
<br />
===Intelligent Biosensors and Self-Adaptation===<br />
<br />
The system is capable of sensing different chemicals and producing different fluorescent proteins. Since the cells can be trained to produce one of several specific fluorescent protein types when a certain chemical is present, one can also view those cells as intelligent biosensors which recognize chemical substances according to a training phase. The intelligent biosensors are not limited to detect chemicals; temperature, pH, light, pressure could be detected with an appropriate system as well. Such an application could be especially of interest when the environment to be probed is harmful for humans, for example due to high toxicity.<br />
<br />
==Team Members==<br />
[[Image:ETHZ_Group_photo_6.png|right|thumb|The ETH Zurich iGEM2007 Team|270px]]<br />
<br />
The ETH Zurich team consists of a good mixture between biologists and engineering students. <br />
We are:<br />
<ul> <br />
<li><i>Undergraduate students</i>: <br><br />
[https://2007.igem.org/User:brutsche Martin Brutsche], [https://2007.igem.org/User:kdikaiou Katerina Dikaiou], <br>[https://2007.igem.org/User:Raphael Raphael Guebeli], [https://2007.igem.org/User:hoehnels Sylke Hoehnel], <br><br />
[https://2007.igem.org/Nan_Li Nan Li], <br />
[https://2007.igem.org/User:Stefan Stefan Luzi]<br />
<li><i>Graduate students</i>: <br><br />
[http://christos.bergeles.net Christos Bergeles], <br />
[http://www.tik.ee.ethz.ch/~sop/people/thohm/ Tim Hohm], <br><br />
[http://www.fussenegger.ethz.ch/people/kemmerc Christian Kemmer], <br />
[https://2007.igem.org/User:JoeKnight Joseph Knight], <br><br />
[http://www.ricomoeckel.de Rico Möckel], <br />
[http://csb.inf.ethz.ch/people/uhr.html Markus Uhr]<br />
<li><i>Project advisors</i>: <br><br />
[http://www.ipe.ethz.ch/laboratories/bpl/people/panke Sven Panke], <br><br />
[http://csb.inf.ethz.ch/people/stelling.html Joerg Stelling]<br />
</ul><br />
<br />
For more information about us, visit our [[ETHZ/Meet_the_team | Meet the Team]] page.<br />
<br />
==Acknowledgments==<br />
The idea for the project as well as its implementation was done solely by the ETH iGEM 2007 team. We would like to thank the people in [http://www.ipe.ethz.ch/laboratories/bpl/index Sven Panke's Lab], especially Andreas Meyer who was always there for us when we had a problem. Additionally, we would like to thank [http://www.facs.ethz.ch Alfredo Franco-Obregón's lab] and Oralea Büchi for the help with the flow cytometry.<br />
<br />
We would also like to acknowledge the financial support by [http://europa.eu EU], the [http://www.ethz.ch ETH Zurich], and [http://www.geneart.com GeneArt]:<br />
<center><br />
{| border="0"<br />
|-<br />
| [http://europa.eu http://www.tik.ee.ethz.ch/~thohm/EU.gif]<br />
| width="40" |<br />
| [http://www.ethz.ch http://www.tik.ee.ethz.ch/~thohm/ethlogo.jpg]<br />
| width="40" |<br />
| [http://www.geneart.com http://www.tik.ee.ethz.ch/~thohm/geneart.gif]<br />
|}<br />
</center><br />
<br />
==Site Map==<br />
<br />
In this wiki, we will present to you a detailed description of the proposed system: starting with the [[ETHZ/Model | modeling of the system]], we describe both, [[ETHZ/Simulation | simulations and theoretical considerations]] of the system, as well as the actual [[ETHZ/Biology | implementation using bio-bricks]] accompanied by our [[ETHZ/Biology/Lab | lab notes]]. Additionally, you will find further [[ETHZ/Meet_the_team | information on the team]], more details about [[ETHZ/Internal | ideas we developed]] before we came up with the system we finally implemented, and some [[ETHZ/Pictures | pictures]] documenting our work.<br />
<br />
The site map of our wiki is the following:<br />
<br />
{| class="wikitable" width="100%" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
!width="34%"| Modeling Pages <br />
!width="22%"| Biology Pages<br />
!width="22%"| ETHZ Team Pages<br />
!width="22%"| Links<br />
|-<br />
| [[ETHZ/Model | Modeling of the learning system]]<br />
| [[ETHZ/Biology | Biological implementation]]<br />
| [[ETHZ/Meet_the_team | Team page]]<br />
| [https://2006.igem.org/wiki/index.php/ETH_Zurich_2005 The ETH Zurich 2005 project]<br />
|-<br />
| [[ETHZ/FlipFlop | Representation using flip-flops]]<br />
| [[ETHZ/Biology/parts | Biobricks/parts]]<br />
| [[ETHZ/Pictures | Pictures]]<br />
| [https://2006.igem.org/wiki/index.php/ETH_Zurich_2006 The ETH Zurich 2006 project]<br />
|-<br />
| [[ETHZ/FSM | Representation using finite state machines]]<br />
| [[ETHZ/Biology/Lab | Lab notes]]<br />
| [[ETHZ/Internal | Brainstorming sessions]]<br />
| <br />
|-<br />
| [[ETHZ/Simulation | Model simulations and theoretical considerations]]<br />
| <br />
| <br />
| <br />
|-<br />
| [[ETHZ/Parameters | Parameters used in our simulations]]<br />
| <br />
| <br />
| <br />
|-<br />
|}<br />
<br />
== References ==<br />
<br />
[http://www.nature.com/nature/journal/v447/n7143/abs/nature05913.html;jsessionid=62903C604764B175945C03DB8639ECBD &#91;1&#93; Bird A] <i>"Perceptions of epigenetics"</i>, Nature 447:396-398, 2007 <br /><br />
[http://linkinghub.elsevier.com/retrieve/pii/S096098220701007X &#91;2&#93; Ptashne M] <i>"On the use of the word ‘epigenetic’"</i>, Current Biology 17(7):R233-R236, 2007 <br /><br />
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<p align="center"><font size="6"><b>ETH Zurich - educatETH <i>E.coli</i> System</b></font></p><br />
</body><br />
</html><br />
<br />
=Introduction=<br />
<blockquote>"All <i>E.coli</i> 's are equal, but some <i>E.coli</i> 's are more equal than others..." ''(freely adapted from "Animal Farm" by George Orwell)''</blockquote> <br />
<br />
... this is what George Orwell would have written, were he a synthetic biologist. In the <i>E.coli</i> colonies on petri dishes, all bacteria are equal; except for some special ones. Our project is about designing such special <i>E.coli</i> that are "more equal" than the rest: they have the ability to be trained in order to memorize and recognize their environment in the future. Their story will be presented through this wiki ...<br />
<br />
==Motivation==<br />
<br />
[[Image:ethz_main_pic.jpg|right|thumb|<b>Fig. 1</b>: Artist's approach to the different stages of the development. We started by modeling and simulating the system. We continued by specifying the DNA strands for its implementation. Finally in the end, our system should report with different fluorescent proteins (image edited)|600px]]<br />
<br />
Our combined team of biologists and engineers is coping with the problem of implementing memory capabilities in bacterial colonies. First, ''E.coli'' are able to respond differently (with distinct fluorescent proteins) to two different inputs (we used chemicals). Second, they remember which input was presented to them. Finally, when confronted with a new input, they are able to recognize whether it is the one that they were trained with or not. <br />
In other words, in this project '''we are educating the <i>E.coli</i>'''!<br />
<br />
===Multipurpose Cell Lines===<br />
<br />
Our system has the ability to behave in different ways according to an internal toggle inside it switching states based on the chemical substances that the system is exposed to. The toggle states could generally be used to trigger events such as enzyme synthesis, transcriptional regulation, virion production, or even cell death. Therefore, one may view the bacterial cell line containing this system as a multipurpose cell line. By adding a certain chemical to a cell line, the latter may be trained to exhibit a desired behavior, and then it is not necessary any more to construct two independent cell lines. <br />
<br />
This means that one applies an “input engineering” instead of a “DNA engineering” approach. If one extends this idea to several inducible toggle switches being harbored in the same cell line, the number of possible phenotypes increases to 2<sup>n</sup>, where n equals the number of toggle switches. For example, if one would have 5 such toggle switches inside a cell line, 32 different behavior patterns would be possible. <br />
<br />
For the purpose of creating a toggle switch that is activated in a specific phase only and not always (a multi-inducible toggle switch), as is required for stable biological automatons, we introduced the concept of [https://2007.igem.org/ETHZ/Biology/parts#double_promoters double promoters] to the [http://partsregistry.org/Main_Page Registry of Standard Biological Parts], which can be helpful for engineering systems which exhibit a desired behavior only at specific times. As a concept, double promoters are expandable to multiple promoter sites - this, however, needs further investigation. <br />
<br />
===Link to Epigenetics===<br />
Epigenetics refers to features like chromatin or DNA modifications that do not involve changes in the underlying DNA sequence and are stable over many cell divisions [1],[2]. If one has a closer look at our proposed system, one can also view it as a model-system for epigenetics: Although the DNA sequence itself stays the same, two different subpopulations of cells with different phenotypes can develop from it. Put simply, depending in which state (subpopulation) the toggle switch is, the cells will produce different fluorescent proteins upon addition of two different inducer molecules. Therefore, the epigenetic feature here is the binding of specific repressor proteins whose production is dependent on the toggle switch state.<br />
<br />
===Intelligent Biosensors and Self-Adaptation===<br />
<br />
The system is capable of sensing different chemicals and producing different fluorescent proteins. Since the cells can be trained to produce one of several specific fluorescent protein types when a certain chemical is present, one can also view those cells as intelligent biosensors which recognize chemical substances according to a training phase. The intelligent biosensors are not limited to detect chemicals; temperature, pH, light, pressure could be detected with an appropriate system as well. Such an application could be especially of interest when the environment to be probed is harmful for humans, for example due to high toxicity.<br />
<br />
==Team Members==<br />
[[Image:ETHZ_Group_photo_6.png|right|thumb|The ETH Zurich iGEM2007 Team|270px]]<br />
<br />
The ETH Zurich team consists of a good mixture between biologists and engineering students. <br />
We are:<br />
<ul> <br />
<li><i>Undergraduate students</i>: <br><br />
[https://2007.igem.org/User:brutsche Martin Brutsche], [https://2007.igem.org/User:kdikaiou Katerina Dikaiou], <br>[https://2007.igem.org/User:Raphael Raphael Guebeli], [https://2007.igem.org/User:hoehnels Sylke Hoehnel], <br><br />
[https://2007.igem.org/Nan_Li Nan Li], <br />
[https://2007.igem.org/User:Stefan Stefan Luzi]<br />
<li><i>Graduate students</i>: <br><br />
[http://christos.bergeles.net Christos Bergeles], <br />
[http://www.tik.ee.ethz.ch/~sop/people/thohm/ Tim Hohm], <br><br />
[http://www.fussenegger.ethz.ch/people/kemmerc Christian Kemmer], <br />
[https://2007.igem.org/User:JoeKnight Joseph Knight], <br><br />
[http://www.ricomoeckel.de Rico Möckel], <br />
[http://csb.inf.ethz.ch/people/uhr.html Markus Uhr]<br />
<li><i>Project advisors</i>: <br><br />
[http://www.ipe.ethz.ch/laboratories/bpl/people/panke Sven Panke], <br><br />
[http://csb.inf.ethz.ch/people/stelling.html Joerg Stelling]<br />
</ul><br />
<br />
For more information about us, visit our [[ETHZ/Meet_the_team | Meet the Team]] page.<br />
<br />
==Acknowledgments==<br />
The idea for the project as well as its implementation was done solely by the ETH iGEM 2007 team. We would like to thank the people in [http://www.ipe.ethz.ch/laboratories/bpl/index Sven Panke's Lab], especially Andreas Meyer who was always there for us when we had a problem. Additionally, we would like to thank [http://www.facs.ethz.ch Alfredo Franco-Obregón's lab] and Oralea Büchi for the help with the flow cytometry.<br />
<br />
We would also like to acknowledge the financial support by [http://europa.eu EU], the [http://www.ethz.ch ETH Zurich], and [http://www.geneart.com GeneArt]:<br />
<center><br />
{| border="0"<br />
|-<br />
| [http://europa.eu http://www.tik.ee.ethz.ch/~thohm/EU.gif]<br />
| width="40" |<br />
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<br />
==Site Map==<br />
<br />
In this wiki, we will present to you a detailed description of the proposed system: starting with the [[ETHZ/Model | modeling of the system]], we describe both, [[ETHZ/Simulation | simulations and theoretical considerations]] of the system, as well as the actual [[ETHZ/Biology | implementation using bio-bricks]] accompanied by our [[ETHZ/Biology/Lab | lab notes]]. Additionally, you will find further [[ETHZ/Meet_the_team | information on the team]], more details about [[ETHZ/Internal | ideas we developed]] before we came up with the system we finally implemented, and some [[ETHZ/Pictures | pictures]] documenting our work.<br />
<br />
The site map of our wiki is the following:<br />
<br />
{| class="wikitable" width="100%" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
!width="34%"| Modeling Pages <br />
!width="22%"| Biology Pages<br />
!width="22%"| ETHZ Team Pages<br />
!width="22%"| Links<br />
|-<br />
| [[ETHZ/Model | Modeling of the learning system]]<br />
| [[ETHZ/Biology | Biological implementation]]<br />
| [[ETHZ/Meet_the_team | Team page]]<br />
| [https://2006.igem.org/wiki/index.php/ETH_Zurich_2005 The ETH Zurich 2005 project]<br />
|-<br />
| [[ETHZ/FlipFlop | Representation using flip-flops]]<br />
| [[ETHZ/Biology/parts | Biobricks/parts]]<br />
| [[ETHZ/Pictures | Pictures]]<br />
| [https://2006.igem.org/wiki/index.php/ETH_Zurich_2006 The ETH Zurich 2006 project]<br />
|-<br />
| [[ETHZ/FSM | Representation using finite state machines]]<br />
| [[ETHZ/Biology/Lab | Lab notes]]<br />
| [[ETHZ/Internal | Brainstorming sessions]]<br />
| <br />
|-<br />
| [[ETHZ/Simulation | Model simulations and theoretical considerations]]<br />
| <br />
| <br />
| <br />
|-<br />
| [[ETHZ/Parameters | Parameters used in our simulations]]<br />
| <br />
| <br />
| <br />
|-<br />
|}<br />
<br />
== References ==<br />
<br />
[http://www.nature.com/nature/journal/v447/n7143/abs/nature05913.html;jsessionid=62903C604764B175945C03DB8639ECBD &#91;1&#93; Bird A] <i>"Perceptions of epigenetics"</i>, Nature 447:396-398, 2007 <br /><br />
[http://linkinghub.elsevier.com/retrieve/pii/S096098220701007X &#91;2&#93; Ptashne M] <i>"On the use of the word ‘epigenetic’"</i>, Current Biology 17(7):R233-R236, 2007 <br /><br />
<br />
<br />
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<p align="center"><font size="6"><b>ETH Zurich - educatETH <i>E.coli</i> System</b></font></p><br />
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<br />
=Introduction=<br />
<blockquote>"All <i>E.coli</i> 's are equal, but some <i>E.coli</i> 's are more equal than others..." ''(freely adapted from "Animal Farm" by George Orwell)''</blockquote> <br />
<br />
... this is what George Orwell would have written, were he a synthetic biologist. In the <i>E.coli</i> colonies on petri dishes, all bacteria are equal; except for some special ones. Our project is about designing such special <i>E.coli</i> that are "more equal" than the rest: they have the ability to be trained in order to memorize and recognize their environment in the future. Their story will be presented through this wiki ...<br />
<br />
==Motivation==<br />
<br />
[[Image:ethz_main_pic.jpg|right|thumb|<b>Fig. 1</b>: Artist's approach to the different stages of the development. We started by modeling and simulating the system. We continued by specifying the DNA strands for its implementation. Finally in the end, our system should report with different fluorescent proteins (image edited)|600px]]<br />
<br />
Our combined team of biologists and engineers is coping with the problem of implementing memory capabilities in bacterial colonies. First, ''E.coli'' are able to respond differently (with distinct fluorescent proteins) to two different inputs (we used chemicals). Second, they remember which input was presented to them. Finally, when confronted with a new input, they are able to recognize whether it is the one that they were trained with or not. <br />
In other words, in this project '''we are educating the <i>E. coli</i>'''!<br />
<br />
===Multipurpose Cell Lines===<br />
<br />
Our system has the ability to behave in different ways according to an internal toggle inside it switching states based on the chemical substances that the system is exposed to. The toggle states could generally be used to trigger events such as enzyme synthesis, transcriptional regulation, virion production, or even cell death. Therefore, one may view the bacterial cell line containing this system as a multipurpose cell line. By adding a certain chemical to a cell line, the latter may be trained to exhibit a desired behavior, and then it is not necessary any more to construct two independent cell lines. <br />
<br />
This means that one applies an “input engineering” instead of a “DNA engineering” approach. If one extends this idea to several inducible toggle switches being harbored in the same cell line, the number of possible phenotypes increases to 2<sup>n</sup>, where n equals the number of toggle switches. For example, if one would have 5 such toggle switches inside a cell line, 32 different behavior patterns would be possible. <br />
<br />
For the purpose of creating a toggle switch that is activated in a specific phase only and not always (a multi-inducible toggle switch), as is required for stable biological automatons, we introduced the concept of [https://2007.igem.org/ETHZ/Biology/parts#double_promoters double promoters] to the [http://partsregistry.org/Main_Page Registry of Standard Biological Parts], which can be helpful for engineering systems which exhibit a desired behavior only at specific times. As a concept, double promoters are expandable to multiple promoter sites - this, however, needs further investigation. <br />
<br />
===Link to Epigenetics===<br />
Epigenetics refers to features like chromatin or DNA modifications that do not involve changes in the underlying DNA sequence and are stable over many cell divisions [1],[2]. If one has a closer look at our proposed system, one can also view it as a model-system for epigenetics: Although the DNA sequence itself stays the same, two different subpopulations of cells with different phenotypes can develop from it. Put simply, depending in which state (subpopulation) the toggle switch is, the cells will produce different fluorescent proteins upon addition of two different inducer molecules. Therefore, the epigenetic feature here is the binding of specific repressor proteins whose production is dependent on the toggle switch state.<br />
<br />
===Intelligent Biosensors and Self-Adaptation===<br />
<br />
The system is capable of sensing different chemicals and producing different fluorescent proteins. Since the cells can be trained to produce one of several specific fluorescent protein types when a certain chemical is present, one can also view those cells as intelligent biosensors which recognize chemical substances according to a training phase. The intelligent biosensors are not limited to detect chemicals; temperature, pH, light, pressure could be detected with an appropriate system as well. Such an application could be especially of interest when the environment to be probed is harmful for humans, for example due to high toxicity.<br />
<br />
==Team Members==<br />
[[Image:ETHZ_Group_photo_6.png|right|thumb|The ETH Zurich iGEM2007 Team|260px]]<br />
<br />
The ETH Zurich team consists of a good mixture between biologists and engineering students. <br />
We are:<br />
<ul> <br />
<li><i>Undergraduate students</i>: <br><br />
[https://2007.igem.org/User:brutsche Martin Brutsche], [https://2007.igem.org/User:kdikaiou Katerina Dikaiou], <br>[https://2007.igem.org/User:Raphael Raphael Guebeli], [https://2007.igem.org/User:hoehnels Sylke Hoehnel], <br><br />
[https://2007.igem.org/Nan_Li Nan Li], <br />
[https://2007.igem.org/User:Stefan Stefan Luzi]<br />
<li><i>Graduate students</i>: <br><br />
[http://christos.bergeles.net Christos Bergeles], <br />
[http://www.tik.ee.ethz.ch/~sop/people/thohm/ Tim Hohm], <br><br />
[http://www.fussenegger.ethz.ch/people/kemmerc Christian Kemmer], <br />
[https://2007.igem.org/User:JoeKnight Joseph Knight], <br><br />
[http://www.ricomoeckel.de Rico Möckel], <br />
[http://csb.inf.ethz.ch/people/uhr.html Markus Uhr]<br />
<li><i>Project advisors</i>: <br><br />
[http://www.ipe.ethz.ch/laboratories/bpl/people/panke Sven Panke], <br><br />
[http://csb.inf.ethz.ch/people/stelling.html Joerg Stelling]<br />
</ul><br />
<br />
For more information about us, visit our [[ETHZ/Meet_the_team | Meet the Team]] page.<br />
<br />
==Acknowledgments==<br />
The idea for the project as well as its implementation was done solely by the ETH iGEM 2007 team. We would like to thank the people in [http://www.ipe.ethz.ch/laboratories/bpl/index Sven Panke's Lab], especially Andreas Meyer who was always there for us when we had a problem. Additionally, we would like to thank [http://www.facs.ethz.ch Alfredo Franco-Obregón's lab] and Oralea Büchi for the help with the flow cytometry.<br />
<br />
We would also like to acknowledge the financial support by [http://europa.eu EU], the [http://www.ethz.ch ETH Zurich], and [http://www.geneart.com GeneArt]:<br />
<center><br />
{| border="0"<br />
|-<br />
| [http://europa.eu http://www.tik.ee.ethz.ch/~thohm/EU.gif]<br />
| width="40" |<br />
| [http://www.ethz.ch http://www.tik.ee.ethz.ch/~thohm/ethlogo.jpg]<br />
| width="40" |<br />
| [http://www.geneart.com http://www.tik.ee.ethz.ch/~thohm/geneart.gif]<br />
|}<br />
</center><br />
<br />
==Site Map==<br />
<br />
In this wiki, we will present to you a detailed description of the proposed system: starting with the [[ETHZ/Model | modeling of the system]], we describe both, [[ETHZ/Simulation | simulations and theoretical considerations]] of the system, as well as the actual [[ETHZ/Biology | implementation using bio-bricks]] accompanied by our [[ETHZ/Biology/Lab | lab notes]]. Additionally, you will find further [[ETHZ/Meet_the_team | information on the team]], more details about [[ETHZ/Internal | ideas we developed]] before we came up with the system we finally implemented, and some [[ETHZ/Pictures | pictures]] documenting our work.<br />
<br />
The site map of our wiki is the following:<br />
<br />
{| class="wikitable" width="100%" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
!width="34%"| Modeling Pages <br />
!width="22%"| Biology Pages<br />
!width="22%"| ETHZ Team Pages<br />
!width="22%"| Links<br />
|-<br />
| [[ETHZ/Model | Modeling of the learning system]]<br />
| [[ETHZ/Biology | Biological implementation]]<br />
| [[ETHZ/Meet_the_team | Team page]]<br />
| [https://2006.igem.org/wiki/index.php/ETH_Zurich_2005 The ETH Zurich 2005 project]<br />
|-<br />
| [[ETHZ/FlipFlop | Representation using flip-flops]]<br />
| [[ETHZ/Biology/parts | Biobricks/parts]]<br />
| [[ETHZ/Pictures | Pictures]]<br />
| [https://2006.igem.org/wiki/index.php/ETH_Zurich_2006 The ETH Zurich 2006 project]<br />
|-<br />
| [[ETHZ/FSM | Representation using finite state machines]]<br />
| [[ETHZ/Biology/Lab | Lab notes]]<br />
| [[ETHZ/Internal | Brainstorming sessions]]<br />
| <br />
|-<br />
| [[ETHZ/Simulation | Model simulations and theoretical considerations]]<br />
| <br />
| <br />
| <br />
|-<br />
| [[ETHZ/Parameters | Parameters used in our simulations]]<br />
| <br />
| <br />
| <br />
|-<br />
|}<br />
<br />
== References ==<br />
<br />
[http://www.nature.com/nature/journal/v447/n7143/abs/nature05913.html;jsessionid=62903C604764B175945C03DB8639ECBD &#91;1&#93; Bird A] <i>"Perceptions of epigenetics"</i>, Nature 447:396-398, 2007 <br /><br />
[http://linkinghub.elsevier.com/retrieve/pii/S096098220701007X &#91;2&#93; Ptashne M] <i>"On the use of the word ‘epigenetic’"</i>, Current Biology 17(7):R233-R236, 2007 <br /><br />
<br />
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<hr />
<div><!--- --[[User:LiNan|LiNan]] 07:51, 24 October 2007 (EDT) ---><br />
<br />
<html><br />
<body><br />
<body background="https://static.igem.org/mediawiki/2007/c/c0/Igem2007logo_2.png" style="background-attachment: fixed" ><br />
<p align="center"><font face="Arial"><br />
</font></p><br />
<h1><font face="Arial" size="4" color="#000099"><i>About Me:</i></font></h1><br />
<p>I grew up in China and got my Bachelor degree there. <br />
Right now I am doing my Master study in Biomedical Engineering at<br />
<a href="http://www.ethz.ch" title="Eidgenössische Technische Hochschule Zürich since 1854">ETH Zurich</a>. I remain my study interest in various topics, however, <IMG height=22 src="https://static.igem.org/mediawiki/2007/4/49/Educateth_Ecoli_b.png" width=150></a> currently ranks the first.<span lang="EN-US" style="font-family: Wingdings">J</span></p><br />
<p><i><span style="font-family: Arial">&quot;A painter paints pictures on canvas; but <br />
musicians paint their pictures on silence.</i>&quot; During my spare time, I enjoy the painting on silence<br />
a lot. Besides, I like hiking with friends and traveling around. The only means <br />
to keep my life refreshing is to experience things new. Moreover,<br />
<a href="http://zh.wikipedia.org/wiki/篆刻" title="Fantastic stone and calligraphy art...">seal engraving</a> always fascinates <br />
me.</span></p><br />
<p>&nbsp;</p><br />
<h1><font face="Arial" size="4" color="#000099"><i>Contact <br />
Information:</i></font></h1><br />
<ul><br />
<li><b>Email:</b>&nbsp;&nbsp;leemnan AT gmail DOT com</li><br />
</ul><br />
<br />
<p>&nbsp;</p><br />
<h1><font face="Arial" size="4" color="#000099"><i>Links:</i></font></h1><br />
<br />
</body><br />
<br />
</html><br />
<br />
{| class="wikitable" style="width:500px; height:200px" border="0" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #ffffff; border: 0px #aaa solid; border-collapse: collapse;" <br />
|valign="top"|<br />
*[[ETHZ | ETH Zürich iGEM 2007]] <br />
*[https://2006.igem.org/wiki/index.php/ETH_Zurich_2006 ETH Zürich iGEM 2006]<br />
*[https://2006.igem.org/wiki/index.php/ETH_Zurich_2005 ETH Zürich iGEM 2005]<br />
|style="text-align: center;width:500px; valign:center"|[[Image:NJZY.png|right|151px]]<br />
|}</div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/Meet_the_teamETHZ/Meet the team2007-10-26T15:51:15Z<p>LiNan: /* Undergraduate Students */</p>
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<br />
=ETH Zurich 2007 iGEM Team=<br />
<br />
In this page, you can find more information on this year's team of ETH Zurich. If you also want to see photos taken during the preparation for this year's iGEM, don't forget to see [[ETHZ/Pictures | Pictures!]] as well.<br />
<br />
[[Image:ETHZ_Group_photo_6.png|center|thumb|The ETHZ iGEM2007 Team!|420px]]<br />
<br />
<br />
==Team Description==<br />
<br />
Our team is a combination of hard-working biologists and engineers! :)<br />
===Undergraduate Students===<br />
*[[Image:Martin_Brutsche.jpg|left|40px]] Hi, my name is '''Martin''', and I'm a Master Student in Biomedical Engineering at ETH Zurich. Before that, I did my Diploma studies in Mechanical Engineering, at the University of Applied Sciences in Constance, Germany. I love sailing, snooker and holidays in Denmark. I joined iGEM to learn more about synthetic biology and labwork ([https://2007.igem.org/User:brutsche more info]).<br />
<br />
*[[Image:Katerina_Dikaiou.jpg|left|40px]] Hi, I am '''Katerina'''! I'm one of the engineers in the group - I hold a Diploma in Electrical and Computer Engineering from the Aristotle University of Thessaloniki, Greece, and I am currently a Master student of Biomedical Engineering at ETH Zurich. I enjoy working at the interface between engineering and biology, and I also love music, literature and (very) long walks([https://2007.igem.org/User:kdikaiou more info]).<br />
<br />
*[[Image:Raphael_Gubeli.jpg|left|40px]] Hi, I'm '''Raphael''', Master Student of Biotechnology at ETH Zurich. So, that's why you can find me mainly in the lab (day and night!). Anyway, if there is time to see the daylight, you can find me on my bicycle in the city, or skiing down the mountains. I joined iGEM because I think that there is really a need to find international standards like the "biobricks". Of course, despite having a lot of work, it is a lot of pleasure and fun, working on this project ([https://2007.igem.org/User:Raphael more info]).<br />
<br />
*[[Image:sylke_hoehnel2.jpg|left|40px]] Hello! I'm '''Sylke''', a B.Sc.-Biotech Student at the ETH Zurich. I was born in Germany, and have lived in London for four years. So, as you can see, I like jetting around the world. I found iGEM as a great opportunity to be part of a team, working on a fun project that's also related to my studies. Apart from Bio-books, I like art, swimming and snowboarding ([https://2007.igem.org/User:hoehnels more info]).<br />
<br />
*[[Image:Nan_Li.jpg|left|40px]] Hi, I am '''Nan'''. Right now I'm doing my Master study in Biomedical Engineering at [http://www.ethz.ch ETH Zurich] and currently fascinated by educatETH ''E.coli''. During my spare time, I enjoy music - the painting on silence, also I like hiking with friends and traveling around. The only means to keep my life refreshing is to experience things new ([https://2007.igem.org/Nan_Li more info]).<br />
<br />
*[[Image:Stefan_Luzi.jpg|left|40px]] My name is '''Stefan''', M.Sc.-Biotechnology student at ETH Zurich. This means that I like cells, genes and bioreactors... especially bioreactors... the bigger the better, hell yeah! Although there are many people who question the success of synthetic biology, I believe in its potential and am proud to work with our team at the very front of it. During my spare time (yes, I have a life besides my studies) I like hiking, rowing or cycling, and can never reject a cold jug of beer ;-) ([https://2007.igem.org/User:Stefan more info]).<br />
<br />
===Graduate Students===<br />
*[[Image:Christos_Bergeles.jpg|left|40px]] Hoi zaeme, I am '''Christos'''. I graduated with a Diploma in Electrical and Computer Engineering, from the National Technical University of Athens, Greece, and currently I am a PhD Student at ETH Zurich, working with nice, little, autonomous, magnetic, wireless, reconfigurable, fluorescent, swallowable, biocompatible super geeky microrobots. I sleep in my lab currently, but all is good! ([http://christos.bergeles.net more info]).<br />
<br />
*[[Image:Tim_Hohm.jpg|left|40px]] Hi, I am '''Tim'''. I received a diploma in computer science in 2003, from the University of Dortmund, Germany. After staying for two years in the research institute caesar (in Bonn, Germany), 2006 I joined the [http://www.tik.ee.ethz.ch/~sop/ Systems Optimization Group] at ETH Zurich headed by Prof. Zitzler. My research focuses on the application of bio-inspired optimization techniques on systems biology problems ([http://www.tik.ee.ethz.ch/~sop/people/thohm/ more info]).<br />
<br />
<br />
<br />
*[[Image:Christian_Kemmer.jpg|left|40px]] Gruezi, I am '''Christian'''. I am the only graduated Biology-guy in our Team. I studied Electrical Engineering, Biology and Bioprocess Engineering in Germany and Finland. Beside my studies, I worked in serveral scientific Institutes, e.g., the European Neuroscience Institute Goettingen (Germany) and the Bioprocess Engineering Laboratory in Oulu (Finland). I graduated in 2005 with a diploma in Biology. Currently I am a PhD-student at the ETH. I am doing research in applied Biotechnology and synthetic Biology. ([http://www.fussenegger.ethz.ch/people/kemmerc more info]).<br />
<br />
*[[Image:Joe_Knight.jpg|left|40px]] '''Joe Knight''' here. I am a PhD candidate performing research in biomedical engineering at the ETH. Before coming to the ETH I was the 2005-06 Grube Fellow at Stanford University's Biodesign Innovation Program. I joined iGEM because I believe some of the greatest innovations in biotechnology of tomorrow will be made in synthetic biology, and I hope to be part of it all! ([https://2007.igem.org/User:JoeKnight more info]).<br />
<br />
*[[Image:Markus_Uhr.jpg|left|38px]] I'm '''Markus Uhr''', a PhD candidate in Computational Systems Biology, at ETH Zurich. I did my MSc in Computational Science and Engineering, so I'm the guy that likes big equations and solving them on a computer ;-). Besides the geeky stuff I do to make a living I also enjoy running and orienteering, or listening to good music. ([https://2007.igem.org/User:uhrm more info]).<br />
<br />
*[[Image:Rico_Mockel.jpg|left|40px]] Hi, I am '''Rico'''. I am a Ph.D student of the ETH Zurich in Switzerland where I am working on the design of aVLSI motion detection chips for microflyers at the Institute of Neuroinformatics. I am a engineer by heart who always loves to disassemble all kind of machines and wants to understand what is driving them and how things work. Because I think that building machines out of DNA instead of silicon is an extremly exiting new way I decided to participate in iGEM. If you would like to find out more about me have a look ([http://www.ricomoeckel.de here]).<br />
<br />
===Advisors===<br />
[[Image:Sven_Panke.jpg|left|thumb|Sven Panke|80px]]<br />
<br />
[[Image:Joerg_Stelling.gif|left|thumb|Joerg Stelling|80px]]</div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZETHZ2007-10-26T15:48:11Z<p>LiNan: /* Site Map */</p>
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<p align="center"><font size="6"><b>ETH Zurich - educatETH <i>E.coli</i> System</b></font></p><br />
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<br />
=Introduction=<br />
<blockquote>"All <i>E.coli</i> 's are equal, but some <i>E.coli</i> 's are more equal than others..." ''(freely adapted from "Animal Farm" by George Orwell)''</blockquote> <br />
<br />
... this is what George Orwell would have written, were he a synthetic biologist. In the <i>E.coli</i> colonies on petri dishes, all bacteria are equal; except for some special ones. Our project is about modeling and designing these special <i>E.coli</i> that are "more equal" than the rest: they have the ability to be trained, memorize, and recognize their environment, and their story will be presented through this wiki ...<br />
<br />
==Motivation==<br />
<br />
[[Image:ethz_main_pic.jpg|right|thumb|<b>Fig. 1</b>: Artist's approach to the different stages of the development. We started by modeling and simulating the system, we continued by specifying the DNA strands for its implementation, and in the end, our system should report with different fluorescent proteins (image edited)|600px]]<br />
<br />
Our combined team of biologists and engineers is working on the ''E.coli'' 's ability, first, to recognize two different inputs (here we use two different chemicals), second, to remember which input was presented to them, and third, when confronted with a new input, to recognize whether it is the one that it was trained with or not - in other words, '''we educated the <i>E. coli</i>'''.<br />
<br />
It is obvious thus, that we are coping with the problem of implementing memory capabilities in bacterial colonies. Our system is a memorizing system, and it also has the ability to understand its environment through a recognition phase. If we assume that the training chemicals are harmful for humans, we can use the developed system to understand whether a particular environment is dangerous for humans or not. In this sense, our system can have applications in the health/safety sector, as is described below:<br />
<br />
===Intelligent Biosensors and Self-Adaptation===<br />
<br />
We constructed a system capable of sensing different chemicals and producing different fluorescent proteins. Since the cells can be trained to produce one of several specific fluorescent protein types when a certain chemical is present, one can also view those cells as intelligent biosensors, able to change their properties in a training phase.<br />
It is also possible that the environment (and its chemicals) itself is the training phase, and hence, that the biosensors are adapting themselves to the existing environment. Eventually, the intelligent biosensors are not limited to detect chemicals. Temperature, pH, light, pressure etc. could be detected with an appropriate system as well. <br />
<br />
The main applications of our system however, lie in fully exploiting its memorizing potential, as can be understood from the following:<br />
<br />
===Multipurpose Cell Lines===<br />
<br />
Our system can be trained to behave in a specific way, by setting its inducible toggle switch to one of its two states. This specific states can trigger specific and different events such as enzyme synthesis, transcriptional regulation, virion production, or even cell death. In this case, one can view the bacterial cell line containing this system, as a multipurpose cell line. One can add a certain chemical to a cell line, and train it to the desired behavior, instead of constructing two independent cell lines. <br />
<br />
This means, one applies an “input engineering” instead of a “DNA engineering” approach. If one extends this idea to several inducible toggle switches being harbored in the same cell line, the number of possible phenotypes increases to 2<sup>n</sup>, where n equals the number of toggle switches. For example, if one would have 5 toggle switches inside a cell line, 32 different behavior patterns would be possible. <br />
<br />
For the purpose of creating a toggle switch that is activated in a specific phase (we call this a multi-inducible toggle switch), as is required for stable biological automatons, we introduced the concept of [https://2007.igem.org/ETHZ/Biology/parts#double_promoters double promoters] to the [http://partsregistry.org/Main_Page Registry of Standard Biological Parts], which can be helpful for future projects. As a concept, double promoters are expandable to handle multiple promoter sites, for the case of a greater number of toggle switches (stefan: this sentence is not so clear to me, can we formulate it differently?).<br />
<br />
==Link to Epigenetics==<br />
Epigenetics refers to features like chromatin or DNA modifications that do not involve changes in the underlying DNA sequence and are stable over many cell divisions [1],[2]. If one has a closer look at our proposed system, one can also view it as a model-system for epigenetics: Although the DNA sequence itself stays the same, two different subpopulations of cells with different phenotypes can develop from it. Put simply, depending in which state (subpopulation) the toggle switch is, the cells will produce different fluorescent proteins upon addition of inducer molecules (aTc or IPTG). For example, if aTc is added one subpopulation will be red while the other will be yellow although both carry exactly the same DNA information. Therefore, the epigenetic feature here is the binding of specific repressor proteins whose production is dependent on the toggle switch state.<br />
<br />
==Team Members==<br />
[[Image:ETHZ_Group_photo_6.png|right|thumb|ETHZ iGEM2007 Team|250px]]<br />
<br />
The ETH Zurich team consists of good mixture between biologists and engineering students. <br />
We are:<br />
<ul> <br />
<li><i>Undergraduate students</i>: <br><br />
[https://2007.igem.org/User:brutsche Martin Brutsche], [https://2007.igem.org/User:kdikaiou Katerina Dikaiou], <br>[https://2007.igem.org/User:Raphael Raphael Guebeli], [https://2007.igem.org/User:hoehnels Sylke Hoehnel], <br><br />
[https://2007.igem.org/Nan_Li Nan Li], <br />
[https://2007.igem.org/User:Stefan Stefan Luzi]<br />
<li><i>Graduate students</i>: <br><br />
[http://christos.bergeles.net Christos Bergeles], <br />
[http://www.tik.ee.ethz.ch/~sop/people/thohm/ Tim Hohm], <br><br />
[http://www.fussenegger.ethz.ch/people/kemmerc Christian Kemmer], <br />
[https://2007.igem.org/User:JoeKnight Joseph Knight], <br><br />
[http://www.ricomoeckel.de Rico Möckel], <br />
[http://csb.inf.ethz.ch/people/uhr.html Markus Uhr]<br />
<li><i>Project advisors</i>: <br><br />
[http://www.ipe.ethz.ch/laboratories/bpl/people/panke Sven Panke], <br><br />
[http://csb.inf.ethz.ch/people/stelling.html Joerg Stelling]<br />
</ul><br />
<br />
For more information about us, visit our [[ETHZ/Meet_the_team | Meet the Team]] page.<br />
<br />
==Acknowledgments==<br />
The idea for the project as well as its implementation was done by the ETH iGEM 2007 team. We would like to thank the people in [http://www.ipe.ethz.ch/laboratories/bpl/index Sven Panke's Lab], especially Andreas Meyer who was always there for us when we had a problem. Additionally, we would like to thank [http://www.facs.ethz.ch Alfredo Franco-Obregón's lab] and Oralea Büchi for the help with the flow cytometry.<br />
<br />
We would also like to acknowledge the financial support by [http://europa.eu EU], the [http://www.ethz.ch ETH Zurich], and [http://www.geneart.com GeneArt]:<br />
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|-<br />
| [http://europa.eu http://www.tik.ee.ethz.ch/~thohm/EU.gif]<br />
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| [http://www.ethz.ch http://www.tik.ee.ethz.ch/~thohm/ethlogo.jpg]<br />
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| [http://www.geneart.com http://www.tik.ee.ethz.ch/~thohm/geneart.gif]<br />
|}<br />
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<br />
==Site Map==<br />
<br />
In this wiki, we will present you a detailed description of the proposed system: starting with the [[ETHZ/Model | modeling of the system]], we describe both, [[ETHZ/Simulation | simulations and theoretical considerations]] of the system, as well as the actual [[ETHZ/Biology | implementation using bio-bricks]] accompanied by our [[ETHZ/Biology/Lab | lab notes]]. Additionally, you find some further [[ETHZ/Meet_the_team | information on the team]], some more details about [[ETHZ/Internal | ideas we developed]] before we came up with the system we finally implemented, and some [[ETHZ/Pictures | pictures]] documenting our work.<br />
<br />
The site map of our wiki is the following:<br />
<br />
{| class="wikitable" width="100%" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
!width="34%"| Modeling Pages <br />
!width="22%"| Biology Pages<br />
!width="22%"| ETHZ Team Pages<br />
!width="22%"| Links<br />
|-<br />
| [[ETHZ/Model | Modeling of the learning system]]<br />
| [[ETHZ/Biology | Biological implementation]]<br />
| [[ETHZ/Meet_the_team | Team page]]<br />
| [https://2006.igem.org/wiki/index.php/ETH_Zurich_2005 The ETH Zurich 2005 project]<br />
|-<br />
| [[ETHZ/FlipFlop | Representation using flip-flops]]<br />
| [[ETHZ/Biology/parts | Biobricks/parts]]<br />
| [[ETHZ/Pictures | Pictures]]<br />
| [https://2006.igem.org/wiki/index.php/ETH_Zurich_2006 The ETH Zurich 2006 project]<br />
|-<br />
| [[ETHZ/FSM | Representation using finite state machines]]<br />
| [[ETHZ/Biology/Lab | Lab notes]]<br />
| [[ETHZ/Internal | Brainstorming sessions]]<br />
| <br />
|-<br />
| [[ETHZ/Simulation | Model simulations and theoretical considerations]]<br />
| <br />
| <br />
| <br />
|-<br />
| [[ETHZ/Parameters | Parameters used in our simulations]]<br />
| <br />
| <br />
| <br />
|-<br />
|}<br />
<br />
== References ==<br />
<br />
[http://www.nature.com/nature/journal/v447/n7143/abs/nature05913.html;jsessionid=62903C604764B175945C03DB8639ECBD &#91;1&#93; Bird A] <i>"Perceptions of epigenetics"</i>, Nature 447:396-398, 2007 <br /><br />
[http://linkinghub.elsevier.com/retrieve/pii/S096098220701007X &#91;2&#93; Ptashne M] <i>"On the use of the word ‘epigenetic’"</i>, Current Biology 17(7):R233-R236, 2007 <br /><br />
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= Introduction =<br />
<br />
On this page, you can find an analysis of the function of our system, its biological design, and a list of the parts that make up the system. Under [https://2007.igem.org/ETHZ/Biology/Lab Lab Notes], you can find the ingredients and equipment we used, the electronic version of our lab notebook and a presentation of all the difficulties that we encountered.<br />
<br />
educatETH <i>E.coli</i> is a system which can distinguish between [http://openwetware.org/wiki/ATc anhydrotetracycline (aTc)] and [http://openwetware.org/wiki/IPTG Isopropyl-beta-D-thiogalactopyranoside (IPTG)] based on a previous learning phase conducted with the same chemicals and the help of [http://partsregistry.org/Acyl-HSLs acylhomoserine lactone (AHL)]. It is composed of three subsystems: the subsystem of constitutively produced proteins, the learning subsystem and the reporting subsystem. The constitutively produced proteins (LacI, TetR and LuxR) control the learning subsystem. At the core of the latter there exists an extended version of the original toggle switch found in [1]. That is, a multi-inducible toggle switch. The main difference is reflected in the use of double promoters, so that the toggle switch only changes its state when both, one of the two chemicals (aTc/IPTG), and AHL are present. As AHL is only present during the learning phase, the toggle maintains its state during testing/recognition, and thus can “memorize”. AHL can therefore be seen as a training- or learning substance. In the reporting subsystem, four reporters ([http://partsregistry.org/Featured_Parts:Fluorescent_proteins fluorescent proteins]) allow supervision of (1.) the chemical the system was trained with and (2.) if the system recognizes the chemical it is being exposed to in the recognition phase as one it has been previously trained with or not.<br />
<br />
== The Complete System ==<br />
<br />
<p>[[Image:Biol_system_stand24.10.png|thumb|left|350px|'''Fig. 1:''' Gene interaction network of educatETH ''E.coli'' ]] The biological design of educatETH <i>E.coli</i> is presented in Fig. 1 and below, we clarify the function of all depicted components. (Are you interested in how the complex system of Fig. 1 was modeled? Then visit the [[ETHZ/Model| System Modeling]]!)</p><br />
<br />
==== Constitutive Subsystem ====<br />
<br />
<p>The constitutively produced proteins of the system are LacI, TetR and LuxR. The LuxR part has a special function: when AHL is present, it forms a LuxR-AHL complex which acts on the learning subsystem (more on this later). For now, we will consider that AHL is absent and therefore LuxR cannot activate transcription. The TetR and LacI parts behave similarly: more specifically, the TetR protein in the absence of aTc inhibits the production of p22cII and LacI in the absence of IPTG inhibits the production of cI. When aTc is present, however, the p22cII production is no longer inhibited (and thus p22cII is produced). Correspondingly, cI is produced when IPTG is present.</p><br />
<br />
==== Learning Subsystem ====<br />
<br />
<p>The learning subsystem is a toggle switch with two operator sites. The upper part of the toggle (cI production) has operator sites for the LuxR-AHL complex and p22cII (whose production has in turn been induced by aTc). The LuxR-AHL complex induces cI production, whereas p22cII inhibits it. The lower part of the toggle (p22cII production) has operator sites for the LuxR-AHL complex and cI (which has been induced by IPTG). In analogy to the upper part, the LuxR-AHL complex induces production of p22cII and cI inhibits it. Therefore, the switch always requires the presence of the LuxR-AHL complex in order for it to operate. Its state depends on the presence of p22cII and cI in the system, which in turn was caused through the exposure of the system to aTc and IPTG.</p><br />
<br />
==== Reporting Subsystem ====<br />
<br />
<p>There are four reporters in the system. CFP (more precisely: enhanced CFP, that is ECFP) and YFP (more precisely: enhanced YFP, that is EYFP) are active during the learning phase of the system and show which chemical the system is exposed to during learning, whereas all four reporters (the latter and GFP and RFP) are active during the recognition phase and show if the system is exposed to the same chemical as in learning or not. <br />
More specifically, the YFP production is regulated with help of two operator sites controlled by cI and aTc (TetR inhibitor). cI inhibits the YFP production and aTc induces it. Therefore, YFP is synthesized when the system is exposed to only aTc and cI is not produced within the system (i.e. the system has not been previously exposed to IPTG). The production of the other fluorescent proteins is regulated in a similar manner. Overall, the production of the fluorescent proteins is regulated as follows:<br />
*YFP gets produced when the system is exposed to only aTc and no cI is produced (i.e. the system has ''not'' been previously exposed to IPTG).<br />
*CFP gets produced when the system is exposed to only IPTG and no p22cII is produced (i.e. the system has ''not'' been previously exposed to aTc).<br />
*GFP gets produced when the system is exposed to only IPTG and no cI is produced (i.e. the system has ''not'' been previously exposed to IPTG).<br />
*RFP gets produced when the system is exposed to only aTc and no p22cII is produced (i.e. the system has ''not'' been previously exposed to aTc).</p><br />
<br />
This behaviour is visualized in Fig. 2.<br />
[[Image:ETHzFlowdiagram2.png|center|thumb|350px|<b>Fig. 2</b>: Flow diagram. This figure shows the protocol with which the final system should be tested, as well as the test results in the form of the reported colors. There are three phases the system has to go through: (1) a training or learning phase in which the system learns an input and stores it in its memory, (2) a memory phase in which the system has to keep the content of its memory and, (3) a recognition phase where the output of the system depends on the content of its memory as well as on the current input. |500px]]<br />
<br />
== System Phases ==<br />
<br />
<p>The system operation is divided into three main phases: a learning phase, a memory phase and a recognition phase. During the learning phase, the system is first exposed to one of the two chemicals it is designed to detect (aTc or IPTG). During the memory phase, the specific chemical (aTc or IPTG) is removed and AHL is added to activate the systems internal toggle switch. This maintains the toggle switch to its acquired steady state, which is reported with YFP (if aTc was detected) or CFP (if IPTG was detected). During the recognition phase, the system is exposed to any of the two chemicals (aTc or IPTG), with AHL present. Lets compare the systems toggle switch state with the effect of the newly introduced chemical: the system shows a different response if it has previously been exposed to this certain chemical and reports with the same XFP as in the learning phase (YFP for aTc, CFP for IPTG) or if it recognizes a different chemical and reports with a different XFP (GFP for trained with aTc and recognizing IPTG, RFP for trained with IPTG and recognizing aTc). The following table represents all possible paths that may be taken by the system during all phases of operation according to external stimuli: </p><br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:center; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;" <br />
|+ '''System phases''' <br />
! <br />
!width="44" style="background:#446084; color:white"| aTc<br />
!width="44" style="background:#446084; color:white"| IPTG<br />
!width="44" style="background:#446084; color:white"| AHL<br />
!width="44" style="background:#446084; color:white"| p22cII<br />
!width="44" style="background:#446084; color:white"| cI<br />
! style="background:#446084; color:white"| Reporting <br />
|- <br />
|colspan="7" style="background:#96c9cf;" align="center"|'''Start''' <br />
|- <br />
| no input<br />
| no<br />
| no<br />
| no<br />
| no<br />
| no<br />
| non<br />
|- <br />
| colspan="7" style="background:#96c9cf;" align="center"| '''Learning'''<br />
|- <br />
| Trained with aTc<br />
| yes<br />
| no<br />
| no<br />
| yes<br />
| no<br />
| YFP<br />
|- <br />
| Trained with IPTG<br />
| no<br />
| yes<br />
| no<br />
| no<br />
| yes<br />
| CFP<br />
|- <br />
| colspan="7" style="background:#96c9cf;" align="center"| '''Memorizing'''<br />
|- <br />
| Trained with aTc<br />
| yes<br />
| no<br />
| yes<br />
| yes<br />
| no<br />
| YFP (fading)<br>finally no color<br />
|- <br />
| Trained with IPTG<br />
| no<br />
| yes<br />
| yes<br />
| no<br />
| yes<br />
| CFP (fading)<br>finally no color<br />
|- <br />
| colspan="7" style="background:#96c9cf;" align="center"| '''Recognition'''<br />
|-<br />
| Trained with aTc<br>Tested with aTc<br />
| yes<br />
| no<br />
| yes<br />
| yes<br />
| no<br />
| YFP<br />
|- <br />
| Trained with aTc<br>Tested with IPTG<br />
| no<br />
| yes<br />
| yes<br />
| yes<br />
| no<br />
| GFP<br />
|- <br />
| Trained with IPTG<br>Tested with IPTG<br />
| no<br />
| yes<br />
| yes<br />
| no<br />
| yes<br />
| CFP<br />
|- <br />
| Trained with IPTG<br>Tested with aTc<br />
| yes<br />
| no<br />
| yes<br />
| no<br />
| yes<br />
| RFP<br />
|- <br />
<br />
|}<br />
<br />
== System Parts ==<br />
<br />
<p>educatETH <i>E.coli</i> was implemented with 11 basic parts designed by the ETH Zurich team. [https://2007.igem.org/wiki/index.php?title=ETHZ/Biology/parts The list of all the parts, plasmids and strains used] is available. Because the part information is retrieved from the Registry, the page needs some time to load. <br>(Are you interested in this information because you want to implement educatETH <i>E.coli</i> in your lab? Then visit our [https://2007.igem.org/ETHZ/Biology/Lab In the Lab] page!)</p><br />
<br />
== Current Cloning Status ==<br />
<br />
Due to some major problems, we weren't able to assemble our complete system in the lab, yet. But we are working on it. We're sending 8 new parts and 3 new plasmids to the registry at the moment. As soon as the missing parts are available in their destined plasmids, we will forward them to the parts registry.<br />
We hope to be able to test parts of our system next week, using the FACS machine provided by [http://www.facs.ethz.ch Alfredo Franco-Obregón's lab].<br />
<br />
== References ==<br />
<br />
[http://www.nature.com/nature/journal/v403/n6767/abs/403339a0.html &#91;1&#93; Gardner TS, Cantor CR and Collins JJ] <i>"Construction of a genetic toggle switch in Escherichia coli"</i>, Nature 403:339–342, 2000<br /></div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZETHZ2007-10-26T13:54:11Z<p>LiNan: /* Multipurpose Cell Lines */</p>
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<p align="center"><font size="6"><b>ETH Zurich - educatETH <i>E.coli</i> System</b></font></p><br />
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=Introduction=<br />
<blockquote>"All <i>E.coli</i> 's are equal, but some <i>E.coli</i> 's are more equal than others..." ''(freely adapted from "Animal Farm" by George Orwell)''</blockquote> <br />
<br />
... this is what George Orwell would have written, were he a synthetic biologist. In the <i>E.coli</i> colonies on petri dishes, all bacteria are equal; except for some special ones. Our project is about modeling and designing these special <i>E.coli</i> that are "more equal" than the rest: they have the ability to be trained, memorize, and recognize their environment, and their story will be presented through this wiki ...<br />
<br />
==Motivation==<br />
<br />
[[Image:ethz_main_pic.jpg|right|thumb|<b>Fig. 1</b>: Artist's approach to the different stages of the development. We started by modeling and simulating the system, we continued by specifying the DNA strands for its implementation, and in the end, our system should report with different fluorescent proteins (image edited)|600px]]<br />
<br />
Our combined team of biologists and engineers is working on the ''E.coli'' 's ability, first, to recognize two different inputs (here we use two different chemicals), second, to remember which input was presented to them, and third, when confronted with a new input, to recognize whether it is the one that it was trained with or not - in other words, '''we educated the <i>E. coli</i>'''.<br />
<br />
It is obvious thus, that we are coping with the problem of implementing memory capabilities in bacterial colonies. Our system is a memorizing system, and it also has the ability to understand its environment through a recognition phase. If we assume that the training chemicals are harmful for humans, we can use the developed system to understand whether a particular environment is dangerous for humans or not. In this sense, our system can have applications in the health/safety sector, as is described below:<br />
<br />
===Intelligent Biosensors and Self-Adaptation===<br />
<br />
We constructed a system capable of sensing different chemicals and producing different fluorescent proteins. Since the cells can be trained to produce one of several specific fluorescent protein types when a certain chemical is present, one can also view those cells as intelligent biosensors, able to change their properties in a training phase.<br />
It is also possible that the environment (and its chemicals) itself is the training phase, and hence, that the biosensors are adapting themselves to the existing environment. Eventually, the intelligent biosensors are not limited to detect chemicals. Temperature, pH, light, pressure etc. could be detected with an appropriate system as well. <br />
<br />
The main applications of our system however, lie in fully exploiting its memorizing potential, as can be understood from the following:<br />
<br />
===Multipurpose Cell Lines===<br />
<br />
Our system can be trained to behave in a specific way, by setting its inducible toggle switch to one of its two states. This specific states can trigger specific and different events such as enzyme synthesis, transcriptional regulation, virion production, or even cell death. In this case, one can view the bacterial cell line containing this system, as a multipurpose cell line. One can add a certain chemical to a cell line, and train it to the desired behavior, instead of constructing two independent cell lines. <br />
<br />
This means, one applies an “input engineering” instead of a “DNA engineering” approach. If one extends this idea to several inducible toggle switches being harbored in the same cell line, the number of possible phenotypes increases to 2<sup>n</sup>, where n equals the number of toggle switches. For example, if one would have 5 toggle switches inside a cell line, 32 different behavior patterns would be possible. <br />
<br />
For the purpose of creating a toggle switch that is activated in a specific phase (we call this a multi-inducible toggle switch), as is required for stable biological automatons, we introduced the concept of [https://2007.igem.org/ETHZ/Biology/parts#double_promoters double promoters] to the [http://partsregistry.org/Main_Page Registry of Standard Biological Parts], which can be helpful for future projects. As a concept, double promoters are expandable to handle multiple promoter sites, for the case of a greater number of toggle switches (stefan: this sentence is not so clear to me, can we formulate it differently?).<br />
<br />
==Link to Epigenetics==<br />
Epigenetics refers to features like chromatin or DNA modifications that do not involve changes in the underlying DNA sequence and are stable over many cell divisions [1],[2]. If one has a closer look at our proposed system, one can also view it as a model-system for epigenetics: Although the DNA sequence itself stays the same, two different subpopulations of cells with different phenotypes can develop from it. Put simply, depending in which state (subpopulation) the toggle switch is, the cells will produce different fluorescent proteins upon addition of inducer molecules (aTc or IPTG). For example, if aTc is added one subpopulation will be red while the other will be yellow although both carry exactly the same DNA information. Therefore, the epigenetic feature here is the binding of specific repressor proteins whose production is dependent on the toggle switch state.<br />
<br />
==Team Members==<br />
[[Image:ETHZ_Group_photo_6.png|right|thumb|ETHZ iGEM2007 Team|250px]]<br />
<br />
The ETH Zurich team consists of good mixture between biologists and engineering students. <br />
We are:<br />
<ul> <br />
<li><i>Undergraduate students</i>: <br><br />
[https://2007.igem.org/User:brutsche Martin Brutsche], [https://2007.igem.org/User:kdikaiou Katerina Dikaiou], <br>[https://2007.igem.org/User:Raphael Raphael Guebeli], [https://2007.igem.org/User:hoehnels Sylke Hoehnel], <br><br />
[https://2007.igem.org/Nan_Li Nan Li], <br />
[https://2007.igem.org/User:Stefan Stefan Luzi]<br />
<li><i>Graduate students</i>: <br><br />
[http://christos.bergeles.net Christos Bergeles], <br />
[http://www.tik.ee.ethz.ch/~sop/people/thohm/ Tim Hohm], <br><br />
[http://www.fussenegger.ethz.ch/people/kemmerc Christian Kemmer], <br />
[https://2007.igem.org/User:JoeKnight Joseph Knight], <br><br />
[http://www.ricomoeckel.de Rico Möckel], <br />
[http://csb.inf.ethz.ch/people/uhr.html Markus Uhr]<br />
<li><i>Project advisors</i>: <br><br />
[http://www.ipe.ethz.ch/laboratories/bpl/people/panke Sven Panke], <br><br />
[http://csb.inf.ethz.ch/people/stelling.html Joerg Stelling]<br />
</ul><br />
<br />
For more information about us, visit our [[ETHZ/Meet_the_team | Meet the Team]] page.<br />
<br />
==Acknowledgments==<br />
The idea for the project as well as its implementation was done by the ETH iGEM 2007 team. We would like to thank the people in [http://www.ipe.ethz.ch/laboratories/bpl/index Sven Panke's Lab], especially Andreas Meyer who was always there for us when we had a problem. Additionally, we would like to thank [http://www.facs.ethz.ch Alfredo Franco-Obregón's lab] and Oralea Büchi for the help with the flow cytometry.<br />
<br />
We would also like to acknowledge the financial support by [http://europa.eu EU], the [http://www.ethz.ch ETH Zurich], and [http://www.geneart.com GeneArt]:<br />
<center><br />
{| border="0"<br />
|-<br />
| [http://europa.eu http://www.tik.ee.ethz.ch/~thohm/EU.gif]<br />
| width="40" |<br />
| [http://www.ethz.ch http://www.tik.ee.ethz.ch/~thohm/ethlogo.jpg]<br />
| width="40" |<br />
| [http://www.geneart.com http://www.tik.ee.ethz.ch/~thohm/geneart.gif]<br />
|}<br />
</center><br />
<br />
==Site Map==<br />
<br />
In this wiki, we will present you a detailed description of the proposed system: starting with the [[ETHZ/Model | modeling of the system]], we describe both, [[ETHZ/Simulation | simulations and theoretical considerations]] of the system, as well as the actual [[ETHZ/Biology | implementation using bio-bricks]] accompanied by our [[ETHZ/Biology/Lab | lab notes]]. Additionally, you find some further [[ETHZ/Meet_the_team | information on the team]], some more details about [[ETHZ/Internal | ideas we developed]] before we came up with the system we finally implemented, and some [[ETHZ/Pictures | pictures]] documenting our work.<br />
<br />
The site map of our wiki is the following:<br />
<br />
{| class="wikitable" width="100%" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
!width="25%"| Modeling Pages <br />
!width="25%"| Biology Pages<br />
!width="25%"| ETHZ Team Pages<br />
!width="25%"| Links<br />
|-<br />
| [[ETHZ/Model | Modeling of the learning system]]<br />
| [[ETHZ/Biology | Biological implementation]]<br />
| [[ETHZ/Meet_the_team | Team page]]<br />
| [https://2006.igem.org/wiki/index.php/ETH_Zurich_2005 The ETH Zurich 2005 project]<br />
|-<br />
| [[ETHZ/FlipFlop | Representation using flip-flops]]<br />
| [[ETHZ/Biology/parts | Biobricks/parts]]<br />
| [[ETHZ/Pictures | Pictures]]<br />
| [https://2006.igem.org/wiki/index.php/ETH_Zurich_2006 The ETH Zurich 2006 project]<br />
|-<br />
| [[ETHZ/FSM | Representation using finite state machines]]<br />
| [[ETHZ/Biology/Lab | Lab notes]]<br />
| [[ETHZ/Internal | Brainstorming sessions]]<br />
| <br />
|-<br />
| [[ETHZ/Simulation | Model simulations and theoretical considerations]]<br />
| <br />
| <br />
| <br />
|-<br />
| [[ETHZ/Parameters | Parameters used in our simulations]]<br />
| <br />
| <br />
| <br />
|-<br />
|}<br />
<br />
== References ==<br />
<br />
[http://www.nature.com/nature/journal/v447/n7143/abs/nature05913.html;jsessionid=62903C604764B175945C03DB8639ECBD &#91;1&#93; Bird A] <i>"Perceptions of epigenetics"</i>, Nature 447:396-398, 2007 <br /><br />
[http://linkinghub.elsevier.com/retrieve/pii/S096098220701007X &#91;2&#93; Ptashne M] <i>"On the use of the word ‘epigenetic’"</i>, Current Biology 17(7):R233-R236, 2007 <br /><br />
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=Introduction=<br />
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In this page, you can find pictures that we took during our working on the educatETH <i>E.coli</i> project. <br />
<br />
===The Meeting===<br />
<p><br />
[[Image:ETHZ_pic_02.jpg|left|thumb|300px|During a Monday night's meeting]] <br />
[[Image:ETHZ_pic_02.jpg|center|thumb|300px|test]]<br />
</p><br />
<br><br />
<br />
===Working in the lab===<br />
<p><br />
[[Image:ETHZ_pic_16.jpg|left|thumb|300px|The lab]] [[Image:Igem_bench.jpg|center|thumb|300px|Our iGEM bench!]]<br />
[[Image:Ecoli2.jpg|left|thumb|300px|more Agar plates]]<br />
</p></div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/ModelETHZ/Model2007-10-26T11:51:57Z<p>LiNan: /* Allosteric regulation */</p>
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<br />
=Introduction=<br />
<br />
As previously discussed in the main page, we are interested in designing a system that is able to adapt to its environment. Our ideas are based on discussions about neural networks, and how we can create a biological system that exhibits the behavior of learning without having to resort to evolutionary processes. <br />
<br />
[[Image:ETHzFlowdiagram2.png|thumb|<b>Fig. 1</b>: Flow diagram. This figure shows the protocol with which the final system should be tested as well as the test results in form of the reported colors. The protocol is divided into three phases: (1) a training or learning phase in which the system learns an input and stores it in its memory, (2) a memory phase in which the system keeps the content of its memory, and finally (3) a recognition phase where the output of the system depends on the content of its memory as well as the current input.|450px]]<br />
<br />
Learning can be considered as a switching of behavior, based on some external stimuli. Thus, it comes naturally to work on existing ideas of toggle switches and [[ETHZ/FSM | finite state machines]]. <br />
<br />
The proposed system is able to distinguish between two chemicals. It represents a minimal test system that is intended as a proof of concept. By introducing the ability to distinguish more than two chemical and thereby introducing new [[ETHZ/FSM | system states]], the power of the system or its "intelligence" can be scaled. A protocol depicting how the system should react according to an input is shown in Fig. 1.<br />
<br />
The idea behind this protocol is that:<br />
* The system will be able to learn one of two input signals - ATC or IPTG - during a learning phase, while a "learning signal" (AHL) is not yet present. Depending on the input it will report by producing either cyan or yellow florescence. <br />
* Once the system has learned, the inputs - ATC or IPTG - can be removed and the system goes into a memory state in the presence of AHL. In this state, no output color is reported. Memorizing is guaranteed by removing the input chemicals.<br />
* During the recognition phase, the inputs ATC or IPTG are (re-)inserted. The system reports by changing its color depending on the input and its current memory state. This is why the system has different florescence properties even in the presence of the same input. The recognition phase takes place in the presence of AHL, to keep the memory enabled and avoid another learning phase. Since we would like to separate four different end states, we use four different fluorescent proteins to encode them.<br />
<br />
==Model Overview==<br />
<br />
The model for the proposed system is developed using a top-down approach. We start with a black box approach as shown in Fig. 2. <br />
<br />
[[Image:ETHZBlackbox.png|thumb|<b>Fig. 2</b>: Black box |280px]]<br />
<br />
The system is sketched in Fig. 3. It can be summarized as follows:<br />
* There are two inputs to be learned/detected/adapted to.<br />
* There is one separate input to switch on the memory.<br />
* The system has to alternate between at least three states. Hence, we decided to use two state variables - CI and P22CII (when interpreted as binary variables, in principle allowing for up to four different states).<br />
* There are four different output signals (synthesis of four fluorescent proteins). One could also decide to take six output signals into account to further distinguish the learning phase from the recognition phase. However, we restricted ourselves to four outputs to reduce the number of genes that are needed to implement the signals.<br />
<br />
[[Image:ETHZFullsystemmodel.png|left|thumb|<b>Fig. 3</b>: System overview. AHL, IPTG and ATC pass the cell membrane where they build complexes with the sensor proteins LuxR, LacI and TetR. These sensor proteins and/or complexes are used to control the internal system state: the memory represented by the proteins CI and P22CII (mutually repressing their synthesis) and the sensed input (IPTG, ATC). CFP, RFP, GFP and YFP stand for yellow, red, cyan and green florescent protein, respectively.|420px]]<br />
<br />
However, we had to keep in mind that the proposed system should be implemented in DNA, and that it would be sensitive to noise. As a result, we took several actions to achieve better experimental results and easier DNA construction:<br />
* To be more robust against perturbations, we coupled the state variables CI and P22CII like it is known from toggle switches [1]. Based on this approach, one state variable is depressing the other one, and the system's internal toggle has the possibility of reaching two stable states.<br />
* Since - due to their size - proteins can only hardly pass the cell membrane (if they are not actively transported through the cell membrane), we decided to use the much smaller inducer molecules AHL, IPTG and ATC as inputs. However, since these inducers cannot directly act on the transcription of the DNA nor on the production of proteins, we need to produce the sensor proteins LuxR, LacI and TetR that build complexes with AHL, IPTG and ATC, respectively.<br />
* The sensor proteins and complexes are used to control the memory formation and the production of the florescent reporter proteins CFP, RFP, GFP and YFP.<br><br><br />
<br />
==Detailed Model==<br />
<br />
In order to test our ideas, we came up with a detailed model of all the interactions in the system. <br />
After defining the desired behavior of our system (as shown in the introduction) and a [[ETHZ/FSM | formalized desciption of the system]] we identified necessary biological components and their interactions. As we can observe in Fig. 3, our system is composed from three basic subparts:<br />
* sensors,<br />
* memory, and<br />
* reporters.<br />
<br />
===Sensors===<br />
<br />
The first part contains the sensors. Our sensors are the proteins LacI, luxR and TetR, which are constitutively produced. The sensing subsystem is shown in Fig. 4.<br />
<br />
[[Image:Model01b.png|center|thumb|<b>Fig. 4</b>: The proteins that act as sensors are constitutively produced.|140px]]<br />
<br />
===Memory===<br />
<br />
The second subsystem implements the memory. The memory control is based on the following underlying mechanisms:<br />
* The sensor proteins form complexes together with the inducers. These complexes are used to activate the transcription of the genes for the proteins CI and P22CII. <br />
* P22CII and CI repress the DNA transcription of each other, so that the closed loop system behaves as a toggle; a dynamic system with only two possible steady states (see Fig. 6).<br />
<br />
[[Image:ETHZModelLearning.png|center|thumb|<b>Fig. 5</b>: Learning system: Depending on the inputs IPTG or ATC the proteins CI and P22CII are produced.|300px]]<br />
<br />
* Fig. 5 shows the protein production system that is used during the learning phase. During the learning phase, there is still no CI or P22CII produced. They are produced, only if either IPTG or ATC is added, respectively. Since no AHL is present, the inner toggle switch (see Figure 6) is turned off.<br />
<br />
[[Image:ETHZModelMemory.png|center|thumb|<b>Fig. 6</b>: Memory system. If AHL is present the production of either CI or P22CII is continued.|420px]]<br />
<br />
* During the memory phase, AHL is added and the IPTG and ATC are removed. That is why only the inner toggle switch (see Fig. 6) is turned on while the protein production systems shown in Fig. 5 are deactivated. Depending on what was produced during the learning phase, the production of either CI or P22CII is continued. That is why the system can act as memory, effectively storing the information it is exposed to.<br />
<br />
Based on all the above, we present the final assembly of the memory subsystem in Fig. 7.<br />
<br />
[[Image:Model02b.png|center|thumb|<b>Fig. 7</b>: Final interaction of the learning and memory system. The memory content is represented by the concentrations of the proteins CI and P22CII.|560px]]<br />
<br />
===Reporters===<br />
<br />
Fig. 8 gives an overview of the reporter subsystem. Florescent reporter proteins are expressed depending on the inducer concentrations, and the concentrations of CI and P22CII. For example, the presence of either TetR or CI will repress the production of YFP. However, if the inducer ATC is present, ATC will bind to TetR which can no longer block the production of YFP. We are using four fluorescent proteins, to encode the steady states of our system at the final recognition stage. In this way, we are able to distinguish between all the different transition paths in the system.<br />
<br />
[[Image:Model03b.png|center|thumb|<b>Fig. 8</b>: The production of the florescent reporter proteins depends on the memory content (CI or P22CII) and the current input (ATC or IPTG).|600px]]<br />
<br />
==Final Design==<br />
<br />
So far, we have presented all parts needed to model and simulate the behavior of the proposed system with. By following the details presented in the previous section, we have all the necessary information to fully understand the interior of the black boxes that were presented in Fig. 2 and Fig. 3. Our overall system model is presented in Fig. 9.<br />
<br />
[[Image:ETHZFullsystem.png|center|thumb|<b>Fig. 9</b>: Final design of the educatETH <i>E.coli</i> system.|900px]]<br />
<br />
In contrast to this biological implementation, an alternative implementation using an engineering approach can be found on our [[ETHZ/FlipFlop | 'Engineer's View' page]].<br />
<br />
==Mathematical Model==<br />
<br />
Based on the modeling done so far, we can derive the equations that govern the behavior of our system. The model is given by sets of coupled [[ETHZ/Modeling_Basics | ordinary differential equations]] which are presented below. We use a simple notation for the different elements of the equations. Namely: <br />
* All concentrations are given in brackets (for example [CI]). <br />
* All decay constants are described by a variable d followed by the name of the protein they refer to. <br />
* The production of the proteins is described by a basic constant production level named 'a' that models the leak of the production system, and a factor of l and c<sub>max</sub> that describe the maximum production of a protein, given in [M/min]. <br />
* Depending on whether the DNA for a protein is implemented on a low or a high copy plasmid, we distinguish between l<sub>lo</sub> and l<sub>hi</sub>, respectively.<br />
* Dissociation constants are given by 'K' followed by the name of the protein they refer to.<br />
* The Hill cooperabilities are described by the constants 'n' followed by the name of the protein they refer to.<br />
<br />
For a more detailed introduction into how we transferred our model into equations, see the section [[ETHZ/Modeling_Basics|Modeling Basics]].<br><br />
<br />
===Allosteric regulation===<br />
<br />
These equations describe the formation of complexes between the inducers and sensor proteins. We do not use differential equations, but describe directly the concentrations of the complexes. This is a valid assumption, provided that we always wait a sufficient time, and the system reaches a steady state.<br />
We describe the total amount of proteins with the index 't', while we use the index '*' for proteins that build a complex with their respective inducer. For example: <br />
* [TetR]<sub>t</sub> describes the total concentration of TetR that is available. It is the sum of the free TetR proteins and the TetR proteins that form a complex with ATC. <br />
* [TetR<sub>*</sub>] describes the proteins that are available as a complex with ATC, and<br />
* [TetR] gives the concentration of free TetR proteins.<br />
<br />
[[Image:Eq04.png|238px]]<br />
<br />
===Constitutively produced proteins===<br />
<br />
The differential equations for the constitutively produced proteins are very simple, since there is no dependence on other proteins. They are designed so that the protein concentration reaches the value l<sub>hi</sub>*c<sub>max</sub>/d at steady state.<br />
<br />
[[Image:Constitutive_braced.png|330px]]<br />
<br />
===Learning and memory subsystem===<br />
<br />
The learning and memory subsystem is the core of the system that we are trying to model and implement. It is characterized by the feedback between its state variables/proteins CI and P22CII. Its behavior is further complicated by the variation of the production of the aforementioned proteins because of the inputs. The following equations describe the concentrations of the memory proteins as a system of coupled differential equations. The equations consist of two major production parts and a decay part. <br />
* The first production part models the production of either CI or P22CII during the learning phase, and corresponds to the model in Fig. 5.<br />
* The second production part describes the inner toggle switch that was shown in Fig. 6.<br />
<br />
[[Image:Toggle_braced.png|770px]]<br />
<br />
===Reporting subsystem===<br />
<br />
The equations for the reporting subsystem finally describe the production of the florescence proteins depending on the inputs and memory proteins as modeled in Figure 8. Note that both the free constitutively produced proteins and the memory proteins repress the production of the florescence proteins. So e.g. YFP is only produced when there is both no CI and all TetR is bound in a complex together with ATC.<br />
<br />
[[Image:Reporter_braced.png|778px]]<br />
<br />
The systems of equations presented above describe and predict the behavior of our system. We have simulated the behavior of our system at steady states, and the results can be seen in the section [[ETHZ/Simulation|Simulations]]. In order to increase the accuracy of our results, we conducted an extensive literature survey, in order to isolate and find the parameters of our system. Since this is a burden for every team undertaking a complicated project in synthetic biology, we are presenting our full table of parameters in the [[ETHZ/Parameters|Parameters]] page.<br />
<br />
== References ==<br />
<p><br />
[http://www.nature.com/nature/journal/v403/n6767/abs/403339a0.html &#91;1&#93; Gardner TS, Cantor CR and Collins JJ] <i>"Construction of a genetic toggle switch in Escherichia coli"</i>, Nature 403:339–342, 2000<br /></div>LiNanhttp://2007.igem.org/wiki/index.php/Nan_LiNan Li2007-10-26T09:48:35Z<p>LiNan: </p>
<hr />
<div><!--- --[[User:LiNan|LiNan]] 07:51, 24 October 2007 (EDT) ---><br />
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<h1><font face="Arial" size="4" color="#000099"><i>About Me:</i></font></h1><br />
<p>I grew up in China and got my Bachelor degree there. <br />
Right now I am doing my Master study in Biomedical Engineering at<br />
<a href="http://www.ethz.ch" title="Eidgenössische Technische Hochschule Zürich since 1854">ETH Zürich</a>. I remain my study interest in various topics, however, <IMG height=22 src="https://static.igem.org/mediawiki/2007/4/49/Educateth_Ecoli_b.png" width=150></a> currently ranks the first.<span lang="EN-US" style="font-family: Wingdings">J</span></p><br />
<p><i><span style="font-family: Arial">&quot;A painter paints pictures on canvas; but <br />
musicians paint their pictures on silence.</i>&quot; During my spare time, I enjoy the painting on silence<br />
a lot. Besides, I like hiking with friends and traveling around. The only means <br />
to keep my life refreshing is to experience things new. Moreover,<br />
<a href="http://zh.wikipedia.org/wiki/篆刻" title="Fantastic stone and calligraphy art...">seal engraving</a> always fascinates <br />
me.</span></p><br />
<p>&nbsp;</p><br />
<h1><font face="Arial" size="4" color="#000099"><i>Contact <br />
Information:</i></font></h1><br />
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<li><b>Email:</b>&nbsp;&nbsp;leemnan AT gmail DOT com</li><br />
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*[[ETHZ | ETH Zürich iGEM 2007]] <br />
*[https://2006.igem.org/wiki/index.php/ETH_Zurich_2006 ETH Zürich iGEM 2006]<br />
*[https://2006.igem.org/wiki/index.php/ETH_Zurich_2005 ETH Zürich iGEM 2005]<br />
|style="text-align: center;width:500px; valign:center"|[[Image:NJZY.png|right|151px]]<br />
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__NOTOC__<br />
<br />
=Introduction=<br />
<br />
As previously discussed in the main page, we are interested in designing a system that is able to adapt to its environment. Our ideas are based on discussions about neural networks, and how we can create a biological system that exhibits the behavior of learning without having to resort to evolutionary processes. <br />
<br />
[[Image:ETHzFlowdiagram2.png|thumb|<b>Fig. 1</b>: Flow diagram. This figure shows the protocol with which the final system should be tested as well as the test results in form of the reported colors. The protocol is divided into three phases: (1) a training or learning phase in which the system learns an input and stores it in its memory, (2) a memory phase in which the system keeps the content of its memory, and finally (3) a recognition phase where the output of the system depends on the content of its memory as well as the current input.|450px]]<br />
<br />
Learning can be considered as a switching of behavior, based on some external stimuli. Thus, it comes naturally to work on existing ideas of toggle switches and [[ETHZ/FSM | finite state machines]]. <br />
<br />
The proposed system is able to distinguish between two chemicals. It represents a minimal test system that is intended as a proof of concept. By introducing the ability to distinguish more than two chemical and thereby introducing new [[ETHZ/FSM | system states]], the power of the system or its "intelligence" can be scaled. A protocol depicting how the system should react according to an input is shown in Fig. 1.<br />
<br />
The idea behind this protocol is that:<br />
* The system will be able to learn one of two input signals - ATC or IPTG - during a learning phase, while a "learning signal" (AHL) is not yet present. Depending on the input it will report by producing either cyan or yellow florescence. <br />
* Once the system has learned, the inputs - ATC or IPTG - can be removed and the system goes into a memory state in the presence of AHL. In this state, no output color is reported. Memorizing is guaranteed by removing the input chemicals.<br />
* During the recognition phase, the inputs ATC or IPTG are (re-)inserted. The system reports by changing its color depending on the input and its current memory state. This is why the system has different florescence properties even in the presence of the same input. The recognition phase takes place in the presence of AHL, to keep the memory enabled and avoid another learning phase. Since we would like to separate four different end states, we use four different fluorescent proteins to encode them.<br />
<br />
==Model Overview==<br />
<br />
The model for the proposed system is developed using a top-down approach. We start with a black box approach as shown in Fig. 2. <br />
<br />
[[Image:ETHZBlackbox.png|thumb|<b>Fig. 2</b>: Black box |280px]]<br />
<br />
The system is sketched in Fig. 3. It can be summarized as follows:<br />
* There are two inputs to be learned/detected/adapted to.<br />
* There is one separate input to switch on the memory.<br />
* The system has to alternate between at least three states. Hence, we decided to use two state variables - CI and P22CII (when interpreted as binary variables, in principle allowing for up to four different states).<br />
* There are four different output signals (synthesis of four fluorescent proteins). One could also decide to take six output signals into account to further distinguish the learning phase from the recognition phase. However, we restricted ourselves to four outputs to reduce the number of genes that are needed to implement the signals.<br />
<br />
[[Image:ETHZFullsystemmodel.png|left|thumb|<b>Fig. 3</b>: System overview. AHL, IPTG and ATC pass the cell membrane where they build complexes with the sensor proteins LuxR, LacI and TetR. These sensor proteins and/or complexes are used to control the internal system state: the memory represented by the proteins CI and P22CII (mutually repressing their synthesis) and the sensed input (IPTG, ATC). CFP, RFP, GFP and YFP stand for yellow, red, cyan and green florescent protein, respectively.|420px]]<br />
<br />
However, we had to keep in mind that the proposed system should be implemented in DNA, and that it would be sensitive to noise. As a result, we took several actions to achieve better experimental results and easier DNA construction:<br />
* To be more robust against perturbations, we coupled the state variables CI and P22CII like it is known from toggle switches [1]. Based on this approach, one state variable is depressing the other one, and the system's internal toggle has the possibility of reaching two stable states.<br />
* Since - due to their size - proteins can only hardly pass the cell membrane (if they are not actively transported through the cell membrane), we decided to use the much smaller inducer molecules AHL, IPTG and ATC as inputs. However, since these inducers cannot directly act on the transcription of the DNA nor on the production of proteins, we need to produce the sensor proteins LuxR, LacI and TetR that build complexes with AHL, IPTG and ATC, respectively.<br />
* The sensor proteins and complexes are used to control the memory formation and the production of the florescent reporter proteins CFP, RFP, GFP and YFP.<br><br><br />
<br />
==Detailed Model==<br />
<br />
In order to test our ideas, we came up with a detailed model of all the interactions in the system. <br />
After defining the desired behavior of our system (as shown in the introduction) and a [[ETHZ/FSM | formalized desciption of the system]] we identified necessary biological components and their interactions. As we can observe in Fig. 3, our system is composed from three basic subparts:<br />
* sensors,<br />
* memory, and<br />
* reporters.<br />
<br />
===Sensors===<br />
<br />
The first part contains the sensors. Our sensors are the proteins LacI, luxR and TetR, which are constitutively produced. The sensing subsystem is shown in Fig. 4.<br />
<br />
[[Image:Model01b.png|center|thumb|<b>Fig. 4</b>: The proteins that act as sensors are constitutively produced.|140px]]<br />
<br />
===Memory===<br />
<br />
The second subsystem implements the memory. The memory control is based on the following underlying mechanisms:<br />
* The sensor proteins form complexes together with the inducers. These complexes are used to activate the transcription of the genes for the proteins CI and P22CII. <br />
* P22CII and CI repress the DNA transcription of each other, so that the closed loop system behaves as a toggle; a dynamic system with only two possible steady states (see Fig. 6).<br />
<br />
[[Image:ETHZModelLearning.png|center|thumb|<b>Fig. 5</b>: Learning system: Depending on the inputs IPTG or ATC the proteins CI and P22CII are produced.|300px]]<br />
<br />
* Fig. 5 shows the protein production system that is used during the learning phase. During the learning phase, there is still no CI or P22CII produced. They are produced, only if either IPTG or ATC is added, respectively. Since no AHL is present, the inner toggle switch (see Figure 6) is turned off.<br />
<br />
[[Image:ETHZModelMemory.png|center|thumb|<b>Fig. 6</b>: Memory system. If AHL is present the production of either CI or P22CII is continued.|420px]]<br />
<br />
* During the memory phase, AHL is added and the IPTG and ATC are removed. That is why only the inner toggle switch (see Fig. 6) is turned on while the protein production systems shown in Fig. 5 are deactivated. Depending on what was produced during the learning phase, the production of either CI or P22CII is continued. That is why the system can act as memory, effectively storing the information it is exposed to.<br />
<br />
Based on all the above, we present the final assembly of the memory subsystem in Fig. 7.<br />
<br />
[[Image:Model02b.png|center|thumb|<b>Fig. 7</b>: Final interaction of the learning and memory system. The memory content is represented by the concentrations of the proteins CI and P22CII.|560px]]<br />
<br />
===Reporters===<br />
<br />
Fig. 8 gives an overview of the reporter subsystem. Florescent reporter proteins are expressed depending on the inducer concentrations, and the concentrations of CI and P22CII. For example, the presence of either TetR or CI will repress the production of YFP. However, if the inducer ATC is present, ATC will bind to TetR which can no longer block the production of YFP. We are using four fluorescent proteins, to encode the steady states of our system at the final recognition stage. In this way, we are able to distinguish between all the different transition paths in the system.<br />
<br />
[[Image:Model03b.png|center|thumb|<b>Fig. 8</b>: The production of the florescent reporter proteins depends on the memory content (CI or P22CII) and the current input (ATC or IPTG).|600px]]<br />
<br />
==Final Design==<br />
<br />
So far, we have presented all parts needed to model and simulate the behavior of the proposed system with. By following the details presented in the previous section, we have all the necessary information to fully understand the interior of the black boxes that were presented in Fig. 2 and Fig. 3. Our overall system model is presented in Fig. 9.<br />
<br />
[[Image:ETHZFullsystem.png|center|thumb|<b>Fig. 9</b>: Final design of the educatETH <i>E.coli</i> system.|900px]]<br />
<br />
In contrast to this biological implementation, an alternative implementation using an engineering approach can be found on our [[ETHZ/FlipFlop | 'Engineer's View' page]].<br />
<br />
==Mathematical Model==<br />
<br />
Based on the modeling done so far, we can derive the equations that govern the behavior of our system. The model is given by sets of coupled [[ETHZ/Modeling_Basics | ordinary differential equations]] which are presented below. We use a simple notation for the different elements of the equations. Namely: <br />
* All concentrations are given in brackets (for example [CI]). <br />
* All decay constants are described by a variable d followed by the name of the protein they refer to. <br />
* The production of the proteins is described by a basic constant production level named 'a' that models the leak of the production system, and a factor of l and c<sub>max</sub> that describe the maximum production of a protein, given in [M/min]. <br />
* Depending on whether the DNA for a protein is implemented on a low or a high copy plasmid, we distinguish between l<sub>lo</sub> and l<sub>hi</sub>, respectively.<br />
* Dissociation constants are given by 'K' followed by the name of the protein they refer to.<br />
* The Hill cooperabilities are described by the constants 'n' followed by the name of the protein they refer to.<br />
<br />
For a more detailed introduction into how we transferred our model into equations, see the section [[ETHZ/Modeling_Basics|Modeling Basics]].<br><br />
<br />
===Allosteric regulation===<br />
<br />
These equations describe the formation of complexes between the inducers and sensor proteins. We do not use differential equations, but describe directly the concentrations of the complexes. This is a valid assumption, provided that we always wait a sufficient time, and the system reaches a steady state.<br />
We describe the total amount of proteins with the index 't', while we use the index '*' for proteins that build a complex with their respective inducer. For example: <br />
* [TetR<sub>t</sub>] describes the total concentration of TetR that is available. It is the sum of the free TetR proteins and the TetR proteins that form a complex with ATC. <br />
* [TetR<sub>*</sub>] describes the proteins that are available as a complex with ATC, and<br />
* [TetR] gives the concentration of free TetR proteins.<br />
<br />
[[Image:Eq04.png|238px]]<br />
<br />
===Constitutively produced proteins===<br />
<br />
The differential equations for the constitutively produced proteins are very simple, since there is no dependence on other proteins. They are designed so that the protein concentration reaches the value l<sub>hi</sub>*c<sub>max</sub>/d at steady state.<br />
<br />
[[Image:Constitutive_braced.png|330px]]<br />
<br />
===Learning and memory subsystem===<br />
<br />
The learning and memory subsystem is the core of the system that we are trying to model and implement. It is characterized by the feedback between its state variables/proteins CI and P22CII. Its behavior is further complicated by the variation of the production of the aforementioned proteins because of the inputs. The following equations describe the concentrations of the memory proteins as a system of coupled differential equations. The equations consist of two major production parts and a decay part. <br />
* The first production part models the production of either CI or P22CII during the learning phase, and corresponds to the model in Fig. 5.<br />
* The second production part describes the inner toggle switch that was shown in Fig. 6.<br />
<br />
[[Image:Toggle_braced.png|770px]]<br />
<br />
===Reporting subsystem===<br />
<br />
The equations for the reporting subsystem finally describe the production of the florescence proteins depending on the inputs and memory proteins as modeled in Figure 8. Note that both the free constitutively produced proteins and the memory proteins repress the production of the florescence proteins. So e.g. YFP is only produced when there is both no CI and all TetR is bound in a complex together with ATC.<br />
<br />
[[Image:Reporter_braced.png|778px]]<br />
<br />
The systems of equations presented above describe and predict the behavior of our system. We have simulated the behavior of our system at steady states, and the results can be seen in the section [[ETHZ/Simulation|Simulations]]. In order to increase the accuracy of our results, we conducted an extensive literature survey, in order to isolate and find the parameters of our system. Since this is a burden for every team undertaking a complicated project in synthetic biology, we are presenting our full table of parameters in the [[ETHZ/Parameters|Parameters]] page.<br />
<br />
== References ==<br />
<p><br />
[http://www.nature.com/nature/journal/v403/n6767/abs/403339a0.html &#91;1&#93; Gardner TS, Cantor CR and Collins JJ] <i>"Construction of a genetic toggle switch in Escherichia coli"</i>, Nature 403:339–342, 2000<br /></div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/BiologyETHZ/Biology2007-10-25T23:24:22Z<p>LiNan: /* Introduction */</p>
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<br />
= Introduction =<br />
<br />
On this page, you can find an analysis of the function of our system, its biological design, and a list of the parts that make up the system. Under [https://2007.igem.org/ETHZ/Biology/Lab Lab Notes], you can find the ingredients and equipment we used, the electronic version of our lab notebook and a presentation of all the difficulties that we encountered.<br />
<br />
educatETH <i>E.coli</i> is a system which can distinguish between [http://openwetware.org/wiki/ATc anhydrotetracycline (aTc)] and [http://openwetware.org/wiki/IPTG Isopropyl-beta-D-thiogalactopyranoside (IPTG)] based on a previous learning phase conducted with the same chemicals and the help of [http://partsregistry.org/Acyl-HSLs acylhomoserine lactone (AHL)]. It is composed of three subsystems: the subsystem of constitutively produced proteins, the learning subsystem and the reporting subsystem. The constitutively produced proteins (LacI, TetR and LuxR) control the learning subsystem. At the core of the latter there exists an extended version of the original toggle switch found in [1]. That is, a multi-inducible toggle switch. The main difference is reflected in the use of double promoters, so that the toggle switch only changes its state when both, one of the two chemicals (aTc/IPTG), and AHL are present. As AHL is only present during the learning phase, the toggle maintains its state during testing/recognition, and thus can “memorize”. AHL can therefore be seen as a training- or learning substance. In the reporting subsystem, four reporters ([http://partsregistry.org/Featured_Parts:Fluorescent_proteins fluorescent proteins]) allow supervision of (1.) the chemical the system was trained with and (2.) if the system recognizes the chemical it is being exposed to in the recognition phase as one it has been previously trained with or not.<br />
<br />
== The Complete System ==<br />
<br />
<p>[[Image:Biol_system_stand24.10.png|thumb|left|350px|'''Fig. 1:''' Gene interaction network of educatETH ''E.coli'' ]] The biological design of educatETH <i>E.coli</i> is presented in Fig. 1 and below, we clarify the function of all depicted components. (Are you interested in how the complex system of Fig. 1 was modeled? Then visit the [[ETHZ/Model| System Modeling]]!)</p><br />
<br />
==== Constitutive Subsystem ====<br />
<br />
<p>The constitutively produced proteins of the system are LacI, TetR and LuxR. The LuxR part has a special function: when AHL is present, it forms a LuxR-AHL complex which acts on the learning subsystem (more on this later). For now, we will consider that AHL is absent and therefore LuxR cannot activate transcription. The TetR and LacI parts behave similarly: more specifically, the TetR protein in the absence of aTc inhibits the production of p22cII and LacI in the absence of IPTG inhibits the production of cI. When aTc is present, however, the p22cII production is no longer inhibited (and thus p22cII is produced). Correspondingly, cI is produced when IPTG is present.</p><br />
<br />
==== Learning Subsystem ====<br />
<br />
<p>The learning subsystem is a toggle switch with two operator sites. The upper part of the toggle (cI production) has operator sites for the LuxR-AHL complex and p22cII (whose production has in turn been induced by aTc). The LuxR-AHL complex induces cI production, whereas p22cII inhibits it. The lower part of the toggle (p22cII production) has operator sites for the LuxR-AHL complex and cI (which has been induced by IPTG). In analogy to the upper part, the LuxR-AHL complex induces production of p22cII and cI inhibits it. Therefore, the switch always requires the presence of the LuxR-AHL complex in order for it to operate. Its state depends on the presence of p22cII and cI in the system, which in turn was caused through the exposure of the system to aTc and IPTG.</p><br />
<br />
==== Reporting Subsystem ====<br />
<br />
<p>There are four reporters in the system. CFP (more precisely: enhanced CFP, that is ECFP) and YFP (more precisely: enhanced YFP, that is EYFP) are active during the learning phase of the system and show which chemical the system is exposed to during learning, whereas all four reporters (the latter and GFP and RFP) are active during the recognition phase and show if the system is exposed to the same chemical as in learning or not. <br />
More specifically, the YFP production is regulated with help of two operator sites controlled by cI and aTc (TetR inhibitor). cI inhibits the YFP production and aTc induces it. Therefore, YFP is synthesized when the system is exposed to only aTc and cI is not produced within the system (i.e. the system has not been previously exposed to IPTG). The production of the other fluorescent proteins is regulated in a similar manner. Overall, the production of the fluorescent proteins is regulated as follows:<br />
*YFP gets produced when the system is exposed to only aTc and no cI is produced (i.e. the system has ''not'' been previously exposed to IPTG).<br />
*CFP gets produced when the system is exposed to only IPTG and no p22cII is produced (i.e. the system has ''not'' been previously exposed to aTc).<br />
*GFP gets produced when the system is exposed to only IPTG and no cI is produced (i.e. the system has ''not'' been previously exposed to IPTG).<br />
*RFP gets produced when the system is exposed to only aTc and no p22cII is produced (i.e. the system has ''not'' been previously exposed to aTc).</p><br />
<br />
This behaviour is visualized in Fig. 2.<br />
[[Image:ETHzFlowdiagram2.png|center|thumb|350px|<b>Fig. 2</b>: Flow diagram. This figure shows the protocol with which the final system should be tested, as well as the test results in the form of the reported colors. There are three phases the system has to go through: (1) a training or learning phase in which the system learns an input and stores it in its memory, (2) a memory phase in which the system has to keep the content of its memory and, (3) a recognition phase where the output of the system depends on the content of its memory as well as on the current input. |500px]]<br />
<br />
== System Phases ==<br />
<br />
<p>The system operation is divided into three main phases: a learning phase, a memory phase and a recognition phase. During the learning phase, the system is first exposed to one of the two chemicals it is designed to detect (aTc or IPTG). During the memory phase, the specific chemical (aTc or IPTG) is removed and AHL is added to activate the systems internal toggle switch. This maintains the toggle switch to its acquired steady state, which is reported with YFP (if aTc was detected) or CFP (if IPTG was detected). During the recognition phase, the system is exposed to any of the two chemicals (aTc or IPTG), with AHL present. Lets compare the systems toggle switch state with the effect of the newly introduced chemical: the system shows a different response if it has previously been exposed to this certain chemical and reports with the same XFP as in the learning phase (YFP for aTc, CFP for IPTG) or if it recognizes a different chemical and reports with a different XFP (GFP for trained with aTc and recognizing IPTG, RFP for trained with IPTG and recognizing aTc). The following table represents all possible paths that may be taken by the system during all phases of operation according to external stimuli: </p><br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:center; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;" <br />
|+ '''System phases''' <br />
! <br />
!width="44" style="background:#446084; color:white"| aTc<br />
!width="44" style="background:#446084; color:white"| IPTG<br />
!width="44" style="background:#446084; color:white"| AHL<br />
!width="44" style="background:#446084; color:white"| p22cII<br />
!width="44" style="background:#446084; color:white"| cI<br />
! style="background:#446084; color:white"| Reporting <br />
|- <br />
|colspan="7" style="background:#96c9cf;" align="center"|'''Start''' <br />
|- <br />
| no input<br />
| no<br />
| no<br />
| no<br />
| no<br />
| no<br />
| non<br />
|- <br />
| colspan="7" style="background:#96c9cf;" align="center"| '''Learning'''<br />
|- <br />
| Trained with aTc<br />
| yes<br />
| no<br />
| no<br />
| yes<br />
| no<br />
| YFP<br />
|- <br />
| Trained with IPTG<br />
| no<br />
| yes<br />
| no<br />
| no<br />
| yes<br />
| CFP<br />
|- <br />
| colspan="7" style="background:#96c9cf;" align="center"| '''Memorizing'''<br />
|- <br />
| Trained with aTc<br />
| yes<br />
| no<br />
| yes<br />
| yes<br />
| no<br />
| YFP (fading)<br>finally no color<br />
|- <br />
| Trained with IPTG<br />
| no<br />
| yes<br />
| yes<br />
| no<br />
| yes<br />
| CFP (fading)<br>finally no color<br />
|- <br />
| colspan="7" style="background:#96c9cf;" align="center"| '''Recognition'''<br />
|-<br />
| Trained with aTc<br>Tested with aTc<br />
| yes<br />
| no<br />
| yes<br />
| yes<br />
| no<br />
| YFP<br />
|- <br />
| Trained with aTc<br>Tested with IPTG<br />
| no<br />
| yes<br />
| yes<br />
| yes<br />
| no<br />
| GFP<br />
|- <br />
| Trained with IPTG<br>Tested with IPTG<br />
| no<br />
| yes<br />
| yes<br />
| no<br />
| yes<br />
| CFP<br />
|- <br />
| Trained with IPTG<br>Tested with aTc<br />
| yes<br />
| no<br />
| yes<br />
| no<br />
| yes<br />
| RFP<br />
|- <br />
<br />
|}<br />
<br />
== System Parts ==<br />
<br />
<p>EducatETH <i>E.coli</i> was implemented with 11 basic parts designed by the ETH Zurich team. [https://2007.igem.org/wiki/index.php?title=ETHZ/Biology/parts The list of all the parts, plasmids and strains used] is available. Because the part information is retrieved from the Registry, the page needs some time to load. <br>(Are you interested in this information because you want to implement educatETH <i>E.coli</i> in your lab? Then visit our [https://2007.igem.org/ETHZ/Biology/Lab In the Lab] page!)</p><br />
<br />
== Current Cloning Status ==<br />
<br />
Please write something here :-)<br />
Maybe put a table with all our parts, then whether they arrived from gene art or not (a cross or so), if its in the plasmid or not....<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:center; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;" <br />
|+ '''Current Cloning Status''' <br />
! style="background:#446084; color:white"| part <br />
!width="44" style="background:#446084; color:white"| 1<br />
!width="44" style="background:#446084; color:white"| 2<br />
!width="44" style="background:#446084; color:white"| 3<br />
!width="44" style="background:#446084; color:white"| 4<br />
!width="44" style="background:#446084; color:white"| 5<br />
!width="44" style="background:#446084; color:white"| 6<br />
!width="44" style="background:#446084; color:white"| 7<br />
!width="44" style="background:#446084; color:white"| 8<br />
!width="44" style="background:#446084; color:white"| 9<br />
!width="44" style="background:#446084; color:white"| 10<br />
!width="44" style="background:#446084; color:white"| 11<br />
|- <br />
| arrived from GENEART<br />
| x<br />
| <br />
| <br />
| <br />
| <br />
|<br />
|<br />
|<br />
|<br />
|<br />
|<br />
|- <br />
| parts needing correction<br />
| <br />
| <br />
| <br />
| <br />
| <br />
|<br />
|<br />
|<br />
|<br />
|<br />
|<br />
|- <br />
| available as miniprep<br />
| <br />
| <br />
| <br />
| <br />
| <br />
|<br />
|<br />
|<br />
|<br />
|<br />
|<br />
|- <br />
| available in final plasmid<br />
| <br />
| <br />
| <br />
| <br />
| <br />
|<br />
|<br />
|<br />
|<br />
|<br />
|<br />
<br />
|}<br />
<br />
== References ==<br />
<br />
[http://www.nature.com/nature/journal/v403/n6767/abs/403339a0.html &#91;1&#93; Gardner TS, Cantor CR and Collins JJ] <i>"Construction of a genetic toggle switch in Escherichia coli"</i>, Nature 403:339–342, 2000<br /></div>LiNanhttp://2007.igem.org/wiki/index.php/File:ETHZ_banner_test.pngFile:ETHZ banner test.png2007-10-25T22:41:22Z<p>LiNan: ETHZ_banner_brighter_2</p>
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<div>ETHZ_banner_brighter_2</div>LiNanhttp://2007.igem.org/wiki/index.php/Nan_LiNan Li2007-10-25T13:35:33Z<p>LiNan: </p>
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<h1><font face="Arial" size="4" color="#000099"><i>About Me:</i></font></h1><br />
<p>I grew up in China and got my Bachelor degree there. <br />
Right now I am doing my Master study in Biomedical Engineering at<br />
<a href="http://www.ethz.ch" title="Eidgenössische Technische Hochschule Zürich since 1854">ETH Zürich</a>. I remain my study interest in various topics, however, <IMG height=22 src="https://static.igem.org/mediawiki/2007/4/49/Educateth_Ecoli_b.png" width=150></a> currently ranks the first.<span lang="EN-US" style="font-family: Wingdings">J</span></p><br />
<p><i><span style="font-family: Arial">&quot;A painter paints pictures on canvas; but <br />
musicians paint their pictures on silence.</i>&quot; During my spare time, I enjoy the painting on silence<br />
a lot. Besides, I like hiking with friends and traveling around. The only means <br />
to keep my life refreshing is to experience things new. Moreover,<br />
<a href="http://zh.wikipedia.org/wiki/篆刻" title="Fantastic stone and calligraphy art...">seal engraving</a> always fascinates <br />
me.</span></p><br />
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<h1><font face="Arial" size="4" color="#000099"><i>Contact <br />
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*[http://leemnan.spaces.live.com Personal Blog]<br />
*[[ETHZ | ETH Zürich iGEM 2007]] <br />
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*[https://2006.igem.org/wiki/index.php/ETH_Zurich_2006 ETH Zürich iGEM 2006]<br />
*[https://2006.igem.org/wiki/index.php/ETH_Zurich_2005 ETH Zürich iGEM 2005]<br />
|style="text-align: center;width:500px; valign:center"|[[Image:NJZY.png|right|151px]]<br />
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<br />
=Introduction=<br />
<br />
As previously discussed in the main page, we are interested in designing a system that is able to adapt to its environment. Our ideas are based on discussions about neural networks, and how we can create a biological system that exhibits the behavior of learning without having to resort to evolutionary processes. <br />
<br />
[[Image:ETHzFlowdiagram2.png|thumb|<b>Fig. 1</b>: Flow diagram. This figure shows the protocol with which the final system should be tested as well as the test results in form of the reported colors. The protocol is divided into three phases: (1) a training or learning phase in which the system learns an input and stores it in its memory, (2) a memory phase in which the system keeps the content of its memory, and finally (3) a recognition phase where the output of the system depends on the content of its memory as well as the current input.|450px]]<br />
<br />
Learning can be considered as a switching of behavior, based on some external stimuli. Thus, it comes naturally to work on existing ideas of toggle switches and [[ETHZ/FSM | finite state machines]]. <br />
<br />
The proposed system is able to distinguish between two chemicals. It represents a minimal test system that is intended as a proof of concept. By introducing the ability to distinguish more than two chemical and thereby introducing new [[ETHZ/FSM | system states]], the power of the system or its "intelligence" can be scaled. A protocol depicting how the system should react according to an input is shown in Fig. 1.<br />
<br />
The idea behind this protocol is that:<br />
* The system will be able to learn one of two input signals - aTc or IPTG - during a learning phase, while a "learning signal" (AHL) is not yet present. Depending on the input it will report by producing either cyan or yellow florescence. <br />
* Once the system has learned, the inputs - aTc or IPTG - can be removed and the system goes into a memory state in the presence of AHL. In this state, no output color is reported. Memorizing is guaranteed by removing the input chemicals.<br />
* During the recognition phase, the inputs aTc or IPTG are (re-)inserted. The system reports by changing its color depending on the input and its current memory state. This is why the system has different florescence properties even in the presence of the same input. The recognition phase takes place in the presence of AHL, to keep the memory enabled and avoid another learning phase. Since we would like to separate four different end states, we use four different fluorescent proteins to encode them.<br />
<br />
==Model Overview==<br />
<br />
The model for the proposed system is developed using a top-down approach. We start with a black box approach as shown in Fig. 2. <br />
<br />
[[Image:ETHZBlackbox.png|thumb|<b>Fig. 2</b>: Black box |280px]]<br />
<br />
The system is sketches in Fig. 3. It can be summarized as follows:<br />
* There are two inputs to be learned/detected/adapted to.<br />
* There is one separate input to switch on the memory.<br />
* The system has to alternate between at least three states. Hence, we decided to use two state variables - cI and p22cII (when interpreted as binary variables, in principle allowing for upt o four different states).<br />
* There are four different output signals (four fluorescent proteins). One could also decide to take six output signals into account to further distinguish the learning phase from the recognition phase. However, we restricted ourselves to four outputs to reduce the number of genes that are needed to implement the signals.<br />
<br />
[[Image:ETHZFullsystemmodel.png|left|thumb|<b>Fig. 3</b>: System overview. AHL, IPTG and aTc pass the cell membrane where they build complexes with the sensor proteins LuxR, LacI and TetR. These sensor proteins and/or complexes are used to control the internal system state: the memory represented by the proteins cI and p22cII (repressing each others the production) and the sensed input (IPTG, aTc). CFP, RFP, GFP and YFP stand for yellow, red, cyan and green florescent protein, respectively.|420px]]<br />
<br />
However, we had to keep in mind that the proposed system should be implemented in DNA, and that it would be sensitive to noise. As a result, we took several actions to achieve better experimental results and easier DNA construction:<br />
* To be more robust against perturbations, we coupled the state variables cI and p22cII like it is known from toggle switches [1]. Based on this approach, one state variable is depressing the other one, and the system's internal toggle has the possibility of reaching two stable states.<br />
* Since - due to their size - proteins can only hardly pass the cell membrane (if they are not actively transported through the cell membrane), we decided to use the much smaller inducer molecules AHL, IPTG and aTc as inputs. However, since these inducers cannot directly act on the transcription of the DNA nor on the production of proteins, we need to produce the sensor proteins LuxR, LacI and TetR that build complexes with AHL, IPTG and aTc, respectively.<br />
* The sensor proteins and complexes are used to control the memory formation and the production of the florescent reporter proteins CFP, RFP, GFP and YFP.<br><br><br />
<br />
==Detailed Model==<br />
<br />
In order to test our ideas, we came up with a detailed description of all the interactions in the system. <br />
Starting from the protocol described in Fig. 1 and a [[ETHZ/FSM | formalized few on the system]] we started to identify necessary biological components and their interactions. As we can observe in Fig. 3, our system is composed from three basic subparts:<br />
<br />
===Sensors===<br />
<br />
The first part contains the sensors. Our sensors are the proteins LacI, luxR and TetR, which are constitutively produced. The sensing subsystem is shown in Fig. 4.<br />
<br />
[[Image:Model01b.png|center|thumb|<b>Fig. 4</b>: The proteins that act as sensors are constitutively produced.|140px]]<br />
<br />
===Memory===<br />
<br />
The second subsystem is responsible for the creation and control of memories. The memory control is based on the following underlying mechanisms:<br />
* The sensor proteins form complexes together with the inducers. These complexes are used to either activate (in case of the complex consisting of luxR and AHL) or repress (in case of the complexes consisting of LacI and IPTG as well as TetR and aTc) the DNA transcription of the proteins cI and p22cII. <br />
* p22cII and cI repress the DNA transcription of each other, so that the closed loop system behaves as a toggle; a dynamic system with only two possible steady states (see Fig. 6).<br />
<br />
[[Image:ETHZModelLearning.png|center|thumb|<b>Fig. 5</b>: Learning system: Depending on the inputs IPTG or aTc the proteins cI and p22cII are produced.|300px]]<br />
<br />
* Fig. 5 shows the protein production system that is used during the learning phase. During the learning phase, there is still no cI or p22cII produced. They are produced, only if either IPTG or aTc is added, respectively. Since no AHL is present, the inner toggle switch (see Figure 6) is turned off.<br />
<br />
[[Image:ETHZModelMemory.png|center|thumb|<b>Fig. 6</b>: Memory system. If AHL is present the production of either cI or p22cII is continued.|420px]]<br />
<br />
* During the memory phase, AHL is added and the IPTG and aTc are removed. That is why only the inner toggle switch (see Fig. 6) is turned on while the protein production systems shown in Fig. 5 are deactivated. Depending on what was produced during the learning phase, the production of either cI or p22cII is continued. That is why the system can act as memory, effectively storing the information it is exposed to.<br />
<br />
Based on all the above, we present the final assembly of the memory subsystem in Fig. 7.<br />
<br />
[[Image:Model02b.png|center|thumb|<b>Fig. 7</b>: Final interaction of the learning and memory system. The memory content is represented by the concentrations of the proteins cI and p22cII.|560px]]<br />
<br />
===Reporters===<br />
<br />
Fig. 8 gives an overview of the reporter subsystem. Florescent reporter proteins are expressed depending on the inducer concentrations, and the concentrations of cI and p22cII. For example, the presence of either TetR or cI will repress the production of YFP. However, if the inducer aTc is present, aTc will bind to TetR which can no longer block the production of YFP. We are using four fluorescent proteins, to encode the steady states of our system at the final recognition stage. In this way, we are able to distinguish between all the different transition paths in the system.<br />
<br />
[[Image:Model03b.png|center|thumb|<b>Fig. 8</b>: The production of the florescent reporter proteins depends on the memory content (cI or p22cII) and the current input (aTc or IPTG).|600px]]<br />
<br />
==Final Model==<br />
<br />
So far, we have presented all parts needed to model and simulate the behavior of the proposed system with. By following the details presented in the previous section, we have all the necessary information to fully understand the interior of the black boxes that were presented in Fig. 2 and Fig. 3. Our overall system model is presented in Fig. 9.<br />
<br />
[[Image:ETHZFullsystem.png|center|thumb|<b>Fig. 9</b>: Final model of the educatETH <i>E. coli</i> system.|900px]]<br />
<br />
==Mathematical Model==<br />
<br />
Based on the modeling done so far, we can derive the equations that govern the behavior of our system. The model is given by sets of coupled [[ETHZ/Modeling_Basics | ordinary differential equations]] which are presented below. We use a simple notation for the different elements of the equations. Namely: <br />
* All concentrations are given in brackets (for example [cI]). <br />
* All decay constants are described by a variable d followed by the name of the protein they refer to. <br />
* The production of the proteins is described by a basic constant production level that models the leak of the production system, and a factor of l and c<sub>max</sub> that describe the maximum production of a protein, given in [M]. <br />
* Depending on whether the DNA for a protein is implemented on a low or a high copy plasmid, we distinguish between l<sub>lo</sub> and l<sub>hi</sub>, respectively.<br />
<br />
For a more basic introduction into how we transferred our model into equations, see the section [[ETHZ/Modeling_Basics|Modeling Basics]].<br><br />
<br />
===Constitutively produced proteins===<br />
<br />
The equations for the constitutively produced proteins are very simple, since there is no dependence on other proteins. They are designed so that the protein concentration reaches the value l<sub>hi</sub>*c<sub>max</sub>/d at steady state.<br />
<br />
[[Image:Constitutive_braced.png|330px]]<br />
<br />
===Allosteric regulation===<br />
<br />
These equations describe the formation of complexes between the inducers and sensor proteins. We do not use differential equations, but describe directly the concentrations of the complexes. This is a valid assumption, provided that we always wait a sufficient time, and the system reaches a steady state.<br />
We describe the total amount of proteins with the index 't', while we use the index '*' for proteins that build a complex with their respective inducer. For example: <br />
* [TetR<sub>t</sub>] describes the total amount of TetR that is available. <br />
* [TetR<sub>*</sub>] describes the proteins that are available as a complex with aTc, and<br />
* [TetR] gives the concentration of free TetR proteins.<br />
<br />
[[Image:Eq04.png|208px]]<br />
<br />
===Learning and memory subsystem===<br />
<br />
The learning and memory subsystem is the core of the system that we are trying to model and implement. It is characterized by the feedback between its state variables/proteins cI and p22cII. Its behavior is further complicated by the variation of the production of the aforementioned proteins because of the inputs. The following equations describe the concentrations of the memory proteins as a system of coupled differential equations. The equations consist of two major production parts and a decay part. <br />
* The first production part models the production of either cI or p22cII during the learning phase, and corresponds to the model in Fig. 5.<br />
* The second production part describes the inner toggle switch that was shown in Fig. 6.<br />
<br />
[[Image:Toggle_braced.png|770px]]<br />
<br />
===Reporting subsystem===<br />
<br />
The equations for the reporting subsystem finally describe the production of the florescence proteins depending on the inputs and memory proteins as modeled in Figure 8. Note that both inputs and memory proteins act repressively on the production of the florescence proteins. So e.g. YFP is only produced when there is both no cI and all TetR is bound in a complex together with aTc.<br />
<br />
[[Image:Reporter_braced.png|778px]]<br />
<br />
The systems of equations presented above describe and predict the behavior of our system. We have simulated the behavior of our system at steady states, and the results can be seen in the section [[ETHZ/Simulation|Simulations]]. In order to increase the accuracy of our results, we conducted an extensive literature survey, in order to isolate and find the parameters of our system. Since this is a burden for every team undertaking a complicated project in synthetic biology, we are presenting our full table of parameters in the [[ETHZ/Parameters|Parameters]] page.<br />
<br />
== References ==<br />
<p><br />
[http://www.nature.com/nature/journal/v403/n6767/abs/403339a0.html &#91;1&#93; Gardner TS, Cantor CR and Collins JJ] <i>"Construction of a genetic toggle switch in Escherichia coli"</i>, Nature 403:339–342, 2000<br /></div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/Biology/partsETHZ/Biology/parts2007-10-24T23:40:59Z<p>LiNan: </p>
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__NOTOC__<br />
<br />
= System Parts =<br />
educatETH <i>E.coli</i> consists of 11 parts that can be synthesized independently (want to know how this is done in the lab? Then visit our [https://2007.igem.org/ETHZ/Biology/Lab In the Lab] page!). Four of them (4,5 and 8,9) form together two functional system units, but they have been separated to ensure comparable part lengths and thus enable easier introduction into plasmids. Are you interested in the structure, mode of action or purpose of individual parts? Just click on the specific links in the third column. This will directly guide you to the entries in the [http://partsregistry.org/Main_Page Registry of Standard Biological Parts]. All 11 educatETH <i>E.coli</i> parts are listed in the following table:<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:center; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;" <br />
|+ '''Single System Parts'''<br />
|-<br />
|-<br />
!width="111"| 1<br />
|width="285"| TetR production <br />
| [http://partsregistry.org/Part:BBa_I739001 BBa_I739001]<br />
|width="250"| constitutive subsystem<br />
|width="157"| <partinfo>BBa_I739001 SpecifiedComponents</partinfo><br />
|-<br />
! 2<br />
| LacI production <br />
| [http://partsregistry.org/Part:BBa_I739002 BBa_I739002]<br />
| constitutive subsystem<br />
| <partinfo>BBa_I739002 SpecifiedComponents</partinfo><br />
|-<br />
! 3<br />
| LuxR production <br />
| [http://partsregistry.org/Part:BBa_I739003 BBa_I739003]<br />
| constitutive subsystem<br />
| <partinfo>BBa_I739003 SpecifiedComponents</partinfo><br />
|-<br />
! 4<br />
| 1st half of P22 cII / EYFP production<br />
| [http://partsregistry.org/Part:BBa_I739004 BBa_I739004]<br />
| reporting subsystem<br />
| <partinfo>BBa_I739004 SpecifiedComponents</partinfo><br />
|-<br />
! 5<br />
| 2nd half of P22 cII / EYFP production<br />
| [http://partsregistry.org/Part:BBa_I739005 BBa_I739005]<br />
| reporting subsystem<br />
| <partinfo>BBa_I739005 SpecifiedComponents</partinfo><br />
|-<br />
! 5new<br />
| 2nd half of P22 cII / EYFP production<br />
| [http://partsregistry.org/Part:BBa_E0430 BBa_E0430]<br />
| reporting subsystem<br />
| <partinfo>BBa_E0430 SpecifiedComponents</partinfo><br />
|-<br />
!6<br />
| cI production<br />
| [http://partsregistry.org/Part:BBa_I739006 BBa_I739006]<br />
| learning subsystem<br />
| <partinfo>BBa_I739006 SpecifiedComponents</partinfo><br />
|-<br />
! 7<br />
| P22 cII production<br />
| [http://partsregistry.org/Part:BBa_I739007 BBa_I739007]<br />
| learning subsystem<br />
| <partinfo>BBa_I739007 SpecifiedComponents</partinfo><br />
|-<br />
! 8<br />
| 1st half of cI / ECFP production<br />
|[http://partsregistry.org/Part:BBa_I739008 BBa_I739008] <br />
| reporting subsystem<br />
| <partinfo>BBa_I739008 SpecifiedComponents</partinfo><br />
|-<br />
! 9<br />
| 2nd half of cI / ECFP production<br />
| [http://partsregistry.org/Part:BBa_I739009 BBa_I739009] <br />
| reporting subsystem<br />
| <partinfo>BBa_I739009 SpecifiedComponents</partinfo><br />
|-<br />
! 10<br />
| RFP production <br />
| [http://partsregistry.org/Part:BBa_I739010 BBa_I739010] <br />
| reporting subsystem<br />
| <partinfo>BBa_I739010 SpecifiedComponents</partinfo><br />
|-<br />
! 11<br />
| GFP production <br />
| [http://partsregistry.org/Part:BBa_I739011 BBa_I739011] <br />
| reporting subsystem<br />
| <partinfo>BBa_I739011 SpecifiedComponents</partinfo><br />
|}<br />
<br><br />
Those 11 basic parts have been further assembled into intermediates and composites, which in turn enable the system to operate:<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:center; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;" <br />
|+ '''Composite System Parts'''<br />
|-<br />
|-<br />
!width="111"| 2+3<br />
|width="285"| lacI + luxR production <br />
| [http://partsregistry.org/Part:BBa_I739012 BBa_I739012]<br />
|width="250"| constitutive subsystem<br />
|width="157"| <partinfo>BBa_I739012 SpecifiedComponents</partinfo><br />
|-<br />
! 1+2+3<br />
| tetR + lacI + luxR production <br />
| [http://partsregistry.org/Part:BBa_I739013 BBa_I739013]<br />
| constitutive subsystem<br />
| <partinfo>BBa_I739013 SpecifiedComponents</partinfo><br />
|-<br />
! 4+5<br />
| P22 cII + EYFP production <br />
| [http://partsregistry.org/Part:BBa_I739015 BBa_I739015]<br />
| reporting subsystem<br />
| <partinfo>BBa_I739015 SpecifiedComponents</partinfo><br />
|-<br />
! 8+9<br />
| cI + ECFP production <br />
| [http://partsregistry.org/Part:BBa_I739016 BBa_I739016]<br />
| reporting subsystem<br />
| <partinfo>BBa_I739016 SpecifiedComponents</partinfo><br />
|-<br />
! (4+5)+(8+9)<br />
| (P22 cII + EYFP) + (cI + ECFP) production <br />
| [http://partsregistry.org/Part:BBa_I739017 BBa_I739017]<br />
| reporting subsystem<br />
| <partinfo>BBa_I739017 SpecifiedComponents</partinfo><br />
|-<br />
! 6+7<br />
| cI + P22 cII production <br />
| [http://partsregistry.org/Part:BBa_I739018 BBa_I739018]<br />
| learning subsystem<br />
| <partinfo>BBa_I739018 SpecifiedComponents</partinfo><br />
|-<br />
! 10+11<br />
| RFP + GFP production <br />
| [http://partsregistry.org/Part:BBa_I739019 BBa_I739019]<br />
| reporting subsystem<br />
| <partinfo>BBa_I739019 SpecifiedComponents</partinfo><br />
|-<br />
! (6+7)+(10+11)<br />
| (cI + P22 cII) + (RFP + GFP) production <br />
| [http://partsregistry.org/Part:BBa_I739020 BBa_I739020]<br />
| learning/reporting subsystem<br />
| <partinfo>BBa_I739020 SpecifiedComponents</partinfo><br />
|}<br />
<br><br />
Many of the above mentioned parts contain ''double promoters''. This promoter constructs contain two independent classes of operator sites and can therefore be regulated by two different types of molecules. Double promoters form the basis of the multi-inducible toggle switch and hence the memory of our learning system. This concept could also help future projects in developing devices and systems that need extended regulation. In the following, we introduce a selection of first generation double promoters:<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:center; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;" <br />
|+ '''Double Promoters'''<br />
|-<br />
|-<br />
!width="111"| 1<sup>pro</sup><br />
|width="285"| cI negative / tetR negative promoter<br />
| [http://partsregistry.org/Part:BBa_I739102 BBa_I739102]<br />
|width="250"| reporting subsystem<br />
|width="157"| <partinfo>BBa_I739102 SpecifiedComponents</partinfo><br />
|-<br />
! 2<sup>pro</sup><br />
| lacI negative / P22 cII negative promoter<br />
| [http://partsregistry.org/Part:BBa_I739103 BBa_I739103]<br />
| reporting subsystem<br />
| <partinfo>BBa_I739103 SpecifiedComponents</partinfo><br />
|-<br />
! 3<sup>pro</sup><br />
| luxR/HSL positive / P22 cII negative promoter<br />
| [http://partsregistry.org/Part:BBa_I739104 BBa_I739104]<br />
| learning subsystem<br />
| <partinfo>BBa_I739104 SpecifiedComponents</partinfo><br />
|-<br />
! 4<sup>pro</sup><br />
| luxR/HSL positive / cI negative promoter<br />
| [http://partsregistry.org/Part:BBa_I739105 BBa_I739105]<br />
| learning subsystem<br />
| <partinfo>BBa_I739105 SpecifiedComponents</partinfo><br />
|-<br />
! 5<sup>pro</sup><br />
| tetR negative / P22 cII negative promoter<br />
| [http://partsregistry.org/Part:BBa_I739106 BBa_I739106]<br />
| reporting subsystem<br />
| <partinfo>BBa_I739106 SpecifiedComponents</partinfo><br />
|-<br />
! 6<sup>pro</sup><br />
| cI negative / lacI negative promoter<br />
| [http://partsregistry.org/Part:BBa_I739107 BBa_I739107]<br />
| reporting subsystem<br />
| <partinfo>BBa_I739107 SpecifiedComponents</partinfo><br />
|}<br />
<br><br />
In order to test if the concept of the proposed double promoters is working, simple proof of concept (PoC) parts have been constructed. The PoC promoter, which shows strong similarites to [http://partsregistry.org/Part:BBa_I739102 BBa_I739102], consists of two TetR operator sequences linked to a constitutive promoter. In contrast to the other double promoters , the PoC promoter is only single regulated. The PoC intermediate is part of the PoC composite the concept can be tested with, that is, EYFP production. The p22cII coding region is included to ensure the functionality in multicistronic constructs.<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:center; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;" <br />
|+ '''Proof of Concept'''<br />
|-<br />
|-<br />
!width="111"| 1<sup>poc</sup><br />
|width="285"| PoC promoter<br />
| [http://partsregistry.org/Part:BBa_I739101 BBa_I739101]<br />
|width="250"| proof of concept, no part of the system <br />
|width="157"| <partinfo>BBa_I739101 SpecifiedComponents</partinfo><br />
|-<br />
! 2<sup>poc</sup><br />
| PoC intermediate<br />
| [http://partsregistry.org/Part:BBa_I739014 BBa_I739014]<br />
| proof of concept, no part of the system <br />
| <partinfo>BBa_I739014 SpecifiedComponents</partinfo><br />
|-<br />
! 3<sup>poc</sup><br />
| PoC composite<br />
| [http://partsregistry.org/Part:BBa_I739021 BBa_I739021]<br />
| proof of concept, no part of the system <br />
| <partinfo>BBa_I739021 SpecifiedComponents</partinfo><br />
|}<br />
<br><br />
Although the parts have been synthesized by GENEART and also were shipped in their high-copy plasmids, we desided to change the cloning vectors. This strategy is based on the fact that fluorescent proteins are potentially harmful to the cells. The parts containing DNA sequences coding for these reporter proteins are therefore supposed to be cloned in low-copy number plasmids. Summarized, the following plasmids have been constructed:<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:center; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;" <br />
|+ '''Plasmids'''<br />
|-<br />
|-<br />
!width="111"| 1<sup>vec</sup><br />
|width="285"| pBR322BB1<br />
| [http://partsregistry.org/Part:BBa_I739201 BBa_I739201]<br />
|width="250"| low-copy cloning vector, ApR<br>constitutive subsystem<br />
|width="157"| <partinfo>BBa_I739201 SpecifiedComponents</partinfo><br />
|-<br />
! 2<sup>vec</sup><br />
| pBR322BB2<br />
| [http://partsregistry.org/Part:BBa_I739202 BBa_I739202]<br />
| low-copy cloning vector, TcR<br />
| <partinfo>BBa_I739202 SpecifiedComponents</partinfo><br />
|-<br />
! 3<sup>vec</sup><br />
| pCK01BB1<br />
| [http://partsregistry.org/Part:BBa_I739203 BBa_I739203]<br />
| low-copy cloning vector, CmR<br>reporting subsystem<br />
| <partinfo>BBa_I739203 SpecifiedComponents</partinfo><br />
|-<br />
! 4<sup>vec</sup><br />
| pACYC177BB1<br />
| [http://partsregistry.org/Part:BBa_I739204 BBa_I739204]<br />
| low-copy cloning vector, KmR<br>learning and reporting subsystem<br />
| <partinfo>BBa_I739204 SpecifiedComponents</partinfo><br />
|}<br />
<br><br />
<br />
Two ''E. coli'' strains have been used in this project. In the beginning, only the Top10 strain was worked with. But in a later stage of the project, using JM101 cells seemed to be more productive since they are growing faster than the Top10 cells.<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:center; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;" <br />
|+ '''Cell Strains'''<br />
|-<br />
|-<br />
!width="111"| 1<sup>str</sup><br />
|width="285"| Top10<br />
| [http://partsregistry.org/Part:BBa_V1009 BBa_V1009]<br />
|width="250"| chemically competent E.coli<br>from Invitrogen<br />
|width="157"| <partinfo>BBa_V1009 SpecifiedComponents</partinfo><br />
|-<br />
! 2<sup>str</sup><br />
| JM101<br />
| [http://partsregistry.org/Part:BBa_I739301 BBa_I739301]<br />
| original blue/white cloning strain<br />
| <partinfo>BBa_I739301 SpecifiedComponents</partinfo><br />
|}<br />
<br></div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/Biology/LabETHZ/Biology/Lab2007-10-24T23:40:47Z<p>LiNan: </p>
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<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Biology#System_Phases">System Phases Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Biology/parts">System Parts Page</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Biology/Lab">Lab Notes Page</a><br />
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<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Meet_the_team#The_ETH_Zurich_07_Team">The ETH Zurich 07 Team</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Meet_the_team#Team_Description">Team Description</a><br />
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On this page, you may find information about how educatETH e.coli was implemented in the lab. More specifically, you will find information on the plasmid strains we used, the modifications we did to them in order to be compatible with the Biobrick library and our cloning plan. Moreover, in this page you may find [[ETHZ/Biology/Labbook| here]] an (unfortunately not complete) electronic copy of our lab notebook. If you are here because you are interested in implementing <i>educat</i>ETH<i> E.coli</i> in your lab, then our System Implementation and the System Parts pages may be of help to you!<br />
<br />
<br />
<br />
== Introduction ==<br />
<br />
For all our cloning procedures we used standard protocols according to ''SAMBROOK and RUSSELL'' Molecular Cloning: A Laboratory Manual <b>[1]</b>.<br />
<br />
== Strains ==<br />
<br />
We used the following <i>E. coli </i> strains:<br />
<br />
<br />
[http://openwetware.org/wiki/E._coli_genotypes#TOP10_.28Invitrogen.29|<b><i>E. coli </i>Top10 (Invitrogen):</b>] <br> <br />
*You can find this strain at [http://partsregistry.org/Part:BBa_V1009 BBa_V1009]<br />
*This strain has a streptomycin resistance <br> <br />
*Genotype: F’ {tetR}, mcrA, Δ(mrr-hsdRMS-mcrBC), φ80 lacZ ΔM15, ΔlacX74, deoR, recA1, araD139 Δ(ara-leu)7679, galU, galK, λ-, rpsL,endA1, nupG<br />
*For further information please [http://openwetware.org/wiki/E._coli_genotypes#TOP10_.28Invitrogen.29| click here]<br />
*<i>References</i>: <br />
**Casdaban, M. and Cohen, S. (1980) J Mol Biol 138:179 PMID 6997493 <br> <br />
**Grant, S.G.N. et al. (1990) Proc. Natl. Acad. Sci. USA 87: 4645-4649 PMID 2162051<br />
<br />
<br />
[http://openwetware.org/wiki/E._coli_genotypes#JM101|<b><i>E. coli </i>JM101:</b>] <br><br />
*You can find this strain at [http://partsregistry.org/Part:BBa_I739301 BBa_I739301]<br />
*We call them <i>Jimmys</i><br />
*This strain is the original blue/white cloning strain<br />
*Genotype: glnV44, thi-1, Δ(lac-proAB), F'[lacIqZΔM15 traD36 proAB+]<br />
*For further information please [http://openwetware.org/wiki/E._coli_genotypes#JM101| click here]<br />
*<i>Reference</i>:<br />
**Messing, J. et al. (1981) Nucleic Acids Res. 9, 309; Yanisch-Perron, C., Vieira, J., and Messing, J. (1985) Gene 33, 103<br />
<br />
<br />
== Plasmids ==<br />
For our system we needed three plasmids with different origins of replication and antibiotic resistances. We decided to take low copy plasmids. We decided to use the following plasmids, which we wanted modify so that they would become compatible to the Biobrick Library multiple cloning site:<br />
<br />
<br />
=== Basic plasmids ===<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
|-<br />
! Plasmid !! Resistances !! Copy number !! Origin !! Map <br />
|-<br />
| [[ETHZ/pbr322| pBR322 <b>[2]</b>]] || Ampicillin, Tetracyline || 15-20 <b>[3]</b> || pMB1 || [[Image:Mappbr322.jpg|center|thumb|pBR322 Map|100px]]<br />
|-<br />
| [[ETHZ/pck01| pCK01 <b>[4]</b>]] || Chloramphenicol|| 5-12 <b>[3]</b>|| pSC101 || [[Image:Mappck01.jpg|center|thumb|pCK01 Map|100px]]<br />
|-<br />
| [[ETHZ/pacyc177| pACYC177 <b>[5]</b>] || Ampicillin, Kanamycin|| 10-12 <b>[3]</b> || p15A || [[Image:Mappacyc177.jpg|center|thumb|pACYC177 Map|100px]]<br />
|-<br />
|}<br />
<br />
=== Changes to the plasmids ===<br />
In order to get the Biobrick multiple cloning site into the plasmids, we had to make several changes to the plasmids:<br />
<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
|-<br />
! Plasmid !! Changes !! New name !! New resistance !! New Map <br />
|-<br />
| [[ETHZ/pbr322| pBR322]] <br />
|<br />
*Site directed mutagenesis: Changed the GCA codon of the PstI site of the bla gene into GTA<br />
*Cloned in [[ETHZ/pbr322-3| linker oligos]] (EcoRI/BamHI)<br />
|<br />
[http://partsregistry.org/Part:BBa_I739201 BBa_I739201] <br />
|<br />
Ampicillin <br />
|<br />
[[Image:MapI739201.jpg|center|thumb|BBa_I739201 Map|100px]]<br />
|-<br />
| [[ETHZ/pbr322| pBR322]] <br />
|<br />
*Cloned in [[ETHZ/pbr322-1| linker oligos]] (EcoRI/PstI)<br />
|<br />
[http://partsregistry.org/Part:BBa_I739203 BBa_I739203]<br />
|<br />
Tetracycline<br />
|<br />
[[Image:MapI739203.jpg|center|thumb|BBa_I739203 Map|100px]]<br />
|-<br />
| [[ETHZ/pck01| pCK01]]<br />
|<br />
*[[ETHZ/primer_pcr_killspe_up| Site directed mutagenesis]]: Changed the ACT codon of the SpeI site in the origin of replication into ATT<br />
*Cloned in [[ETHZ/pck01-3| linker oligos]] (AgeI/AseI)<br />
|<br />
[http://partsregistry.org/Part:BBa_I739202 BBa_I739202]<br />
|<br />
Chloramphenicol<br />
|<br />
[[Image:MapI739202.jpg|center|thumb|BBa_I739202 Map|100px]]<br />
|-<br />
| [[ETHZ/pacyc177| pACYC177]] <br />
|<br />
*Cloned in [[ETHZ/pacyc177-1| linker oligos]] (BamHI/PstI)<br />
|<br />
[http://partsregistry.org/Part:BBa_I739204 BBa_I739204]<br />
|<br />
Kanamycin <br />
| [[Image:MapI739204.jpg|center|thumb|BBa_I739204 Map|100px]]<br />
|-<br />
|}<br />
<br />
<br />
==Cloning plan==<br />
<br />
<br />
===Parts assignment into plasmids===<br />
The plan was to put the following parts into the three plasmids:<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
|-<br />
! plasmid !! resistance !! copy type!! contents !! comments<br />
|-<br />
<br />
| [http://partsregistry.org/Part:BBa_I739201 BBa_I739201] || ampicillin || low || [http://partsregistry.org/Part:BBa_I739001 BBa_I739001(TetR) ], [http://partsregistry.org/Part:BBa_I739002 BBa_I739002(LacI) ], [http://partsregistry.org/Part:BBa_I739003 BBa_I739003(LuxR) ] || constitutive subsystem<br />
|-<br />
<br />
| [http://partsregistry.org/Part:BBa_I739202 BBa_I739202] || chloramphenicol|| low || [http://partsregistry.org/Part:BBa_I739004 BBa_I739004(P22 cII) ], [http://partsregistry.org/Part:BBa_E0430 BBa_E0430(EYFP) ], [http://partsregistry.org/Part:BBa_I739008 BBa_I739008(cI) ], [http://partsregistry.org/Part:BBa_I739009 BBa_I739009(ECFP) ] || reporting subsystem<br />
|-<br />
<br />
| [http://partsregistry.org/Part:BBa_I739204 BBa_I739204] || kanamycin|| low || [http://partsregistry.org/Part:BBa_I739006 BBa_I739006(cI) ], [http://partsregistry.org/Part:BBa_I739007 BBa_I739007(P22 cII) ], [http://partsregistry.org/Part:BBa_I739010 BBa_I739010(RFP) ], [http://partsregistry.org/Part:BBa_I739011 BBa_I739011(GFP) ] || learning subsystem, reporting subsystem<br />
|-<br />
|}<br />
<br />
It is important to insert parts responsible for the production of fluorescent proteins in low copy plasmids, as they are potentially harmful for the cell. Unfortunately, working with low copy plasmids makes the procedure more demanding in the lab.<br />
<br />
<br />
=== Procedure ===<br />
The standard BioBrick assembly will be used to put the parts in the plasmids. Detailed information on how the BioBrick part fabrication works can be found [http://openwetware.org/wiki/Synthetic_Biology:BioBricks/Part_fabrication here]. For a shorter explanation of how to assemble 2 parts together check [http://partsregistry.org/Assembly:Standard_assembly here]. [[Image:Assembly _process.png|thumb|300px|DNA assembly process <b>[6]</b>]] Note that the composite part is constructed from the end to the beginning, i.e. each new part is inserted ''before'' the existing one. Composite parts made of parts '''a''' and '''b''' are denoted '''a.b'''.<br />
<br />
<br />
<br />
==== Plasmid 1 ''([http://partsregistry.org/Part:BBa_I739201 BBa_I739201])'' ====<br />
<br />
<br />
# Digest '''[http://partsregistry.org/Part:BBa_I739001 I739001(TetR)]''' and [http://partsregistry.org/Part:BBa_I739201 BBa_I739201] plasmid with EcoRI/PstI and ligate them afterwards.<br />
# Digest '''[http://partsregistry.org/Part:BBa_I739002 I739002(LacI)]''' and '''[http://partsregistry.org/Part:BBa_I739003 I739003(LuxR)]''' with XbaI/PstI <br />
# Digest '''[http://partsregistry.org/Part:BBa_I739001 I739001(TetR)]''' in [http://partsregistry.org/Part:BBa_I739201 BBa_I739201] with SpeI/PstI.<br />
# Ligate digested '''[http://partsregistry.org/Part:BBa_I739001 I739001(TetR)]''' in [http://partsregistry.org/Part:BBa_I739201 BBa_I739201] with digested '''[http://partsregistry.org/Part:BBa_I739002 I739002(LacI)]'''. You get a plasmid containing a '''[http://partsregistry.org/Part:BBa_I739001 I739001(TetR)].[http://partsregistry.org/Part:BBa_I739002 I739002(LacI)]''' composite part. <br />
# Digest '''[http://partsregistry.org/Part:BBa_I739001 I739001(TetR)].[http://partsregistry.org/Part:BBa_I739002 I739002(LacI)]''' with SpeI/PstI.<br />
# Ligate digested '''[http://partsregistry.org/Part:BBa_I739001 I739001(TetR)].[http://partsregistry.org/Part:BBa_I739002 I739002(LacI)]''' with digested '''[http://partsregistry.org/Part:BBa_I739003 I739003(LuxR)]'''. You get the completed '''[http://partsregistry.org/Part:BBa_I739001 I739001(TetR)].[http://partsregistry.org/Part:BBa_I739002 I739002(LacI)].[http://partsregistry.org/Part:BBa_I739003 I739003(LuxR)]''' composite part in the [http://partsregistry.org/Part:BBa_I739201 BBa_I739201] plasmid.<br />
<br />
<br />
<br />
==== Plasmid 2 ''([http://partsregistry.org/Part:BBa_I739202 BBa_I739202])''====<br />
<br />
<br />
# Digest '''[http://partsregistry.org/Part:BBa_I739004 BBa_I739004(P22 cII)]''', '''[http://partsregistry.org/Part:BBa_I739008 BBa_I739008(cI)]''' and the plasmid [http://partsregistry.org/Part:BBa_I739202 BBa_I739202] with EcoRI/PstI.<br />
# Digest '''[http://partsregistry.org/Part:BBa_E0430 BBa_E0430(EYFP)]''' and '''[http://partsregistry.org/Part:BBa_I739009 BBa_I739009(ECFP)]''' with XbaI/PstI.<br />
# Ligate digested '''[http://partsregistry.org/Part:BBa_I739004 BBa_I739004(P22 cII)]''' and digested plasmid [http://partsregistry.org/Part:BBa_I739202 BBa_I739202].<br />
# Ligate digested '''[http://partsregistry.org/Part:BBa_I739008 BBa_I739008(cI)]''' and the plasmid [http://partsregistry.org/Part:BBa_I739202 BBa_I739202].<br />
# Digest '''[http://partsregistry.org/Part:BBa_I739004 BBa_I739004(P22 cII)]''' in [http://partsregistry.org/Part:BBa_I739202 BBa_I739202] with SpeI/PstI.<br />
# Digest '''[http://partsregistry.org/Part:BBa_I739008 BBa_I739008(cI)]''' in [http://partsregistry.org/Part:BBa_I739202 BBa_I739202] with SpeI/PstI.<br />
# Ligate digested '''[http://partsregistry.org/Part:BBa_I739004 BBa_I739004(P22 cII)]''' in [http://partsregistry.org/Part:BBa_I739202 BBa_I739202] with digested '''[http://partsregistry.org/Part:BBa_E0430 BBa_E0430(EYFP)]'''. You get a plasmid containing a '''[http://partsregistry.org/Part:BBa_I739004 BBa_I739004(P22 cII)].[http://partsregistry.org/Part:BBa_E0430 BBa_E0430(EYFP)]''' composite part. <br />
# Ligate digested '''[http://partsregistry.org/Part:BBa_I739008 BBa_I739008(cI)]''' in [http://partsregistry.org/Part:BBa_I739202 BBa_I739202] with '''[http://partsregistry.org/Part:BBa_I739009 BBa_I739009(ECFP)]'''. You get a plasmid containing a '''[http://partsregistry.org/Part:BBa_I739008 BBa_I739008(cI)].[http://partsregistry.org/Part:BBa_I739009 BBa_I739009(ECFP)]''' composite part.<br />
# Digest '''[http://partsregistry.org/Part:BBa_I739004 BBa_I739004(P22 cII)].[http://partsregistry.org/Part:BBa_E0430 BBa_E0430(EYFP)]''' with SpeI/PstI.<br />
# Digest '''[http://partsregistry.org/Part:BBa_I739008 BBa_I739008(cI)].[http://partsregistry.org/Part:BBa_I739009 BBa_I739009(ECFP)]''' with XbaI/PstI.<br />
# Ligate digested '''[http://partsregistry.org/Part:BBa_I739004 BBa_I739004(P22 cII)].[http://partsregistry.org/Part:BBa_E0430 BBa_E0430(EYFP)]''' and digested '''[http://partsregistry.org/Part:BBa_I739008 BBa_I739008(cI)].[http://partsregistry.org/Part:BBa_I739009 BBa_I739009(ECFP)]'''. You get the completed plasmid containing the '''[http://partsregistry.org/Part:BBa_I739004 BBa_I739004(P22 cII)].[http://partsregistry.org/Part:BBa_E0430 BBa_E0430(EYFP)].[http://partsregistry.org/Part:BBa_I739008 BBa_I739008(cI)].[http://partsregistry.org/Part:BBa_I739009 BBa_I739009(ECFP)]''' composite part.<br />
<br />
<br />
<br />
====Plasmid 3 ''([http://partsregistry.org/Part:BBa_I739204 BBa_I739204])''====<br />
<br />
<br />
# Digest '''[http://partsregistry.org/Part:BBa_I739006 BBa_I739006(cI)]''', '''[http://partsregistry.org/Part:BBa_I739010 BBa_I739010(RFP)]''' and the plasmid [http://partsregistry.org/Part:BBa_I739204 BBa_I739204] with EcoRI/PstI.<br />
# Digest '''[http://partsregistry.org/Part:BBa_I739007 BBa_I739007(P22 cII)]''' and '''[http://partsregistry.org/Part:BBa_I739011 BBa_I739011(GFP)]''' with XbaI/PstI.<br />
# Ligate digested '''[http://partsregistry.org/Part:BBa_I739006 BBa_I739006(cI)]''' and digested plasmid [http://partsregistry.org/Part:BBa_I739204 BBa_I739204].<br />
# Ligate digested '''[http://partsregistry.org/Part:BBa_I739010 BBa_I739010(RFP)]''' and the plasmid [http://partsregistry.org/Part:BBa_I739204 BBa_I739204].<br />
# Digest '''[http://partsregistry.org/Part:BBa_I739006 BBa_I739006(cI)]''' in [http://partsregistry.org/Part:BBa_I739204 BBa_I739204] with SpeI/PstI.<br />
# Digest '''[http://partsregistry.org/Part:BBa_I739010 BBa_I739010(RFP)]''' in [http://partsregistry.org/Part:BBa_I739204 BBa_I739204] with SpeI/PstI.<br />
# Ligate digested '''[http://partsregistry.org/Part:BBa_I739006 BBa_I739006(cI)]''' in [http://partsregistry.org/Part:BBa_I739204 BBa_I739204] with digested '''[http://partsregistry.org/Part:BBa_I739007 BBa_I739007(P22 cII)]'''. You get a plasmid containing a '''[http://partsregistry.org/Part:BBa_I739006 BBa_I739006(cI)].[http://partsregistry.org/Part:BBa_I739007 BBa_I739007(P22 cII)]''' composite part. <br />
# Ligate digested '''[http://partsregistry.org/Part:BBa_I739010 BBa_I739010(RFP)]''' in [http://partsregistry.org/Part:BBa_I739204 BBa_I739204] with '''[http://partsregistry.org/Part:BBa_I739011 BBa_I739011(GFP)]'''. You get a plasmid containing a '''[http://partsregistry.org/Part:BBa_I739010 BBa_I739010(RFP)].[http://partsregistry.org/Part:BBa_I739011 BBa_I739011(GFP)]''' composite part.<br />
# Digest '''[http://partsregistry.org/Part:BBa_I739006 BBa_I739006(cI)].[http://partsregistry.org/Part:BBa_I739007 BBa_I739007(P22 cII)]''' with SpeI/PstI.<br />
# Digest '''[http://partsregistry.org/Part:BBa_I739010 BBa_I739010(RFP)].[http://partsregistry.org/Part:BBa_I739011 BBa_I739011(GFP)]''' with XbaI/PstI.<br />
# Ligate digested '''[http://partsregistry.org/Part:BBa_I739006 BBa_I739006(cI)].[http://partsregistry.org/Part:BBa_I739007 BBa_I739007(P22 cII)]''' and digested '''[http://partsregistry.org/Part:BBa_I739010 BBa_I739010(RFP)].[http://partsregistry.org/Part:BBa_I739011 BBa_I739011(GFP)]'''. You get the completed plasmid containing the '''[http://partsregistry.org/Part:BBa_I739006 BBa_I739006(cI)].[http://partsregistry.org/Part:BBa_I739007 BBa_I739007(P22 cII)].[http://partsregistry.org/Part:BBa_I739010 BBa_I739010(RFP)].[http://partsregistry.org/Part:BBa_I739011 BBa_I739011(GFP)]''' composite part.<br />
<br />
<br />
<br />
==References==<br />
<br />
<p>[1] [http://www.MolecularCloning.com Sambrook and Russel], <i>"Molecular Cloning: A Laboratory Manual"</i>, Cold Spring Harbour Laboratory Press, 3rd edition, 2001</p><br />
<p>[3] [http://www1.qiagen.com/faq/faqview.aspx?faqid=350&SearchText=&FaqCategoryId=0&MenuItemId=0&catalog=1&ProductLineId=1000228 QIAGEN FAQs]</p><br />
<p>[4] Fernández et al., <i>"Molecular Microbiology"</i>, 16:205-213, 1995</p><br />
<p>[6] [http://partsregistry.org/Assembly:Standard_assembly Standard Assembly Process]</p></div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/Meet_the_teamETHZ/Meet the team2007-10-24T23:40:40Z<p>LiNan: </p>
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=ETH Zurich 07 Team=<br />
<br />
In this page, you can find more information on this year's team of ETH Zurich. If you also want to see photos taken during the preparation for this year's iGEM, don't forget to see [[ETHZ/Pictures | Pictures!]] as well.<br />
<br />
[[Image:ETHZ_Group_Photo2.jpg|left|thumb|The ETHZ iGEM2007 Team, posing!|420px]] [[Image:ETHZ_Group_photo_5.png|right|thumb|The ETHZ iGEM2007 Team, with its missing members!|420px]]<br />
<br />
<br />
==Team Description==<br />
<br />
Our team is a combination of hard-working biologists and engineers! :)<br />
===Undergraduate Students===<br />
*[[Image:Martin_Brutsche.jpg|left|40px]] Hi, my name is '''Martin''', and I'm a Master Student in Biomedical Engineering at ETH Zurich. Before that, I did my Diploma studies in Mechanical Engineering, at the University of Applied Sciences in Constance, Germany. I love sailing, snooker and holidays in Denmark. I joined iGEM to learn more about synthetic biology and labwork ([https://2007.igem.org/User:brutsche more info]).<br />
<br />
*[[Image:Katerina_Dikaiou.jpg|left|40px]] Hi, I am '''Katerina'''! I'm one of the engineers in the group - I hold a Diploma in Electrical and Computer Engineering from the Aristotle University of Thessaloniki, Greece, and I am currently a Master student of Biomedical Engineering at ETH Zurich. I enjoy working at the interface between engineering and biology, and I also love music, literature and (very) long walks([https://2007.igem.org/User:kdikaiou more info]).<br />
<br />
*[[Image:Raphael_Gubeli.jpg|left|40px]] Hi, I'm '''Raphael''', Master Student of Biotechnology at ETH Zurich. So, that's why you can find me mainly in the lab (day and night!). Anyway, if there is time to see the daylight, you can find me on my bicycle in the city, or skiing down the mountains. I joined iGEM because I think that there is really a need to find international standards like the "biobricks". Of course, despite having a lot of work, it is a lot of pleasure and fun, working on this project ([https://2007.igem.org/User:Raphael more info]).<br />
<br />
*[[Image:sylke_hoehnel2.jpg|left|40px]] Hello! I'm '''Sylke''', a B.Sc.-Biotech Student at the ETH Zurich. I was born in Germany, and have lived in London for four years. So, as you can see, I like jetting around the world. I found iGEM as a great opportunity to be part of a team, working on a fun project that's also related to my studies. Apart from Bio-books, I like art, swimming and snowboarding ([https://2007.igem.org/User:hoehnels more info]).<br />
<br />
*[[Image:Nan_Li.jpg|left|40px]] Hi, I am '''Nan'''. Right now I'm doing my Master study in Biomedical Engineering at [http://www.ethz.ch ETH Zürich] and currently fascinated by educatETH ''E.coli'' (our ETH iGEM 2007 project). During my spare time, I enjoy music - the painting on silence, also I like hiking with friends and traveling around. The only means to keep my life refreshing is to experience things new ([https://2007.igem.org/Nan_Li more info]).<br />
<br />
*[[Image:Stefan_Luzi.jpg|left|40px]] My name is '''Stefan''', M.Sc.-Biotechnology student at ETH Zurich. That means that I like cells, genes and bioreactors... especially bioreactors... the bigger the better, hell yeah! Although there are many people who question the success of synthetic biology, I believe in its potential and am proud to work with our team at the very front of it. During my spare time (yes, I have a life besides my studies) I like hiking, rowing or cycling, and can never reject a cold jug of beer ;-) ([https://2007.igem.org/User:Stefan more info]).<br />
<br />
===Graduate Students===<br />
*[[Image:Christos_Bergeles.jpg|left|40px]] Hoi zaeme, I am '''Christos'''. I graduated with a Diploma in Electrical and Computer Engineering, from the National Technical University of Athens, Greece, and currently I am a PhD Student at ETH Zurich, working with nice, little, autonomous, magnetic, wireless, reconfigurable, fluorescent, swallowable, biocompatible super geeky microrobots. I sleep in my lab currently, but all is good! ([http://christos.bergeles.net more info]).<br />
<br />
*[[Image:Tim_Hohm.jpg|left|40px]] Hi, I am '''Tim'''. I received a diploma in computer science in 2003, from the University of Dortmund, Germany. After staying for two years in the research institute caesar (in Bonn, Germany), 2006 I joined the Systems Optimization Group at ETH Zurich headed by Prof. Zitzler. My research focuses on the application of bio-inspired optimization techniques on systems biology problems ([http://www.tik.ee.ethz.ch/~sop/people/thohm/ more info]).<br />
<br />
<br />
<br />
*[[Image:Christian_Kemmer.jpg|left|40px]] Gruezi, I am '''Christian'''. I am the only graduated Biology-guy in our Team. I studied Electrical Engineering, Biology and Bioprocess Engineering in Germany and Finland. Beside my studies, I worked in serveral scientific Institutes, e.g., the European Neuroscience Institute Goettingen (Germany) and the Bioprocess Engineering Laboratory in Oulu (Finland). I graduated in 2005 with a diploma in Biology. Currently I am a PhD-student at the ETH. I am doing research in applied Biotechnology and synthetic Biology. ([http://www.fussenegger.ethz.ch/people/kemmerc more info]).<br />
<br />
*[[Image:Joe_Knight.jpg|left|40px]] '''Joe Knight''' here. I am a PhD candidate performing research in biomedical engineering at the ETH. Before coming to the ETH I was the 2005-06 Grube Fellow at Stanford University's Biodesign Innovation Program. I joined iGEM because I believe some of the greatest innovations in biotechnology of tomorrow will be made in synthetic biology, and I hope to be part of it all! ([https://2007.igem.org/User:JoeKnight more info]).<br />
<br />
*[[Image:Markus_Uhr.jpg|left|38px]] I'm '''Markus Uhr''', a PhD candidate in Computational Systems Biology, at ETH Zurich. I did my MSc in Computational Science and Engineering, so I'm the guy that likes big equations and solving them on a computer ;-). Besides the geeky stuff I do to make a living I also enjoy running and orienteering, or listening to good music. ([https://2007.igem.org/User:uhrm more info]).<br />
<br />
*[[Image:Rico_Mockel.jpg|left|40px]] Hi, I am '''Rico'''. I am a Ph.D student of the ETH Zurich in Switzerland where I am working on the design of aVLSI motion detection chips for microflyers at the Institute of Neuroinformatics. I am a engineer by heart who always loves to disassemble all kind of machines and wants to understand what is driving them and how things work. Because I think that building machines out of DNA instead of silicon is an extremly exiting new way I decided to participate in iGEM. If you would like to find out more about me have a look ([http://www.ricomoeckel.de here]).<br />
<br />
===Advisors===<br />
[[Image:Sven_Panke.jpg|left|thumb|Sven Panke|80px]]<br />
<br />
[[Image:Joerg_Stelling.gif|left|thumb|Joerg Stelling|80px]]</div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/ParametersETHZ/Parameters2007-10-24T23:39:51Z<p>LiNan: </p>
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<br />
= Parameters for the EducatETH <i>E. coli</i> system =<br />
<br />
<p><br />
In order to provide as realistic simulation results as possible, and to find good estimates for the simulation parameters, we performed an intensive literature review. However, not all parameters could be found in the literature. Furthermore, one has to take into account that biological parameters cannot be estimated to a very high precision.<br />
</p><br><br />
<br />
== Model Parameters ==<br />
<br />
=== General parameters ===<br />
{| class="wikitable" width="100%" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
!width="10%"| Parameter <br />
!width="10%"| Value<br />
!width="20%"| Description<br />
!width="60%"| Comments<br />
|-<br />
| c<sub>1</sub><sup>max</sup><br />
| 0.01 [mM/h]<br />
| max. transcription rate of constitutive promoter (per gene)<br />
| promoter no. J23105; Estimate<br />
|-<br />
| c<sub>2</sub><sup>max</sup><br />
| 0.01 [mM/h]<br />
| max. transcription rate of LuxR-activated promoter (per gene)<br />
| Estimate<br />
|-<br />
| l<sup>hi</sup><br />
| 25<br />
| high-copy plasmid number<br />
| Estimate<br />
|-<br />
| l<sup>lo</sup><br />
| 5<br />
| low-copy plasmid number<br />
| Estimate<br />
|-<br />
| a<br />
| 1%<br />
| basic production levels<br />
| Estimate<br />
|-<br />
|}<br />
<br />
=== Degradation constants ===<br />
{| class="wikitable" width="100%" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
!width="10%"| Parameter <br />
!width="10%"| Value<br />
!width="20%"| Description<br />
!width="60%"| Comments<br />
|-<br />
| d<sub>LacI</sub><br />
| 2.31e-3 [1/s]<br />
| degradation of LacI<br />
| Ref. [10]<br />
|-<br />
| d<sub>TetR</sub><br />
| <br />
*1e-5 [1/s]<br />
*2.31e-3 [1/s]<br />
| degradation of TetR<br />
| <br />
*Ref. [9]<br />
*Ref. [10]<br />
|-<br />
| d<sub>LuxR</sub><br />
| 1e-3 - 1e-4 [1/s]<br />
| degradation of LuxR<br />
| Ref: [6]<br />
|-<br />
| d<sub>CI</sub><br />
| 7e-4 [1/s]<br />
| degradation of CI<br />
| Ref. [7]<br />
|-<br />
| d<sub>P22CII</sub><br />
| <br />
| degradation of P22CII<br />
| <br />
|-<br />
| d<sub>YFP</sub><br />
| 6.3e-3 [1/min]<br />
| degradation of YFP<br />
| suppl. mat. to Ref. [8] corresponding to a half life of 110min<br />
|-<br />
| d<sub>GFP</sub><br />
| 6.3e-3 [1/min]<br />
| degradation of GFP<br />
| in analogy to YFP<br />
|-<br />
| d<sub>RFP</sub><br />
| 6.3e-3 [1/min]<br />
| degradation of RFP<br />
| in analogy to YFP<br />
|-<br />
| d<sub>CFP</sub><br />
| 6.3e-3 [1/min]<br />
| degradation of CFP<br />
| in analogy to YFP<br />
|-<br />
|}<br />
<br />
=== Dissociation constants ===<br />
<br />
{| class="wikitable" width="100%" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
!width="10%"| Parameter <br />
!width="10%"| Value<br />
!width="20%"| Description<br />
!width="60%"| Comments<br />
|-<br />
| K<sub>LacI</sub><br />
| <br />
* 0.1 - 1 [pM]<br />
* 800 [nM]<br />
| LacI repressor dissociation constant<br />
| <br />
* Ref. [2]<br />
* Ref. [12]<br />
|-<br />
| K<sub>IPTG</sub><br />
| 1.3 [&#181;M]<br />
| IPTG-LacI repressor dissociation constant<br />
| Ref. [2]<br />
|-<br />
| K<sub>TetR</sub><br />
| 179 [pM]<br />
| TetR repressor dissociation constant<br />
| Ref. [1]<br />
|-<br />
| K<sub>ATC</sub><br />
| 893 [pM]<br />
| ATC-TetR repressor dissociation constant<br />
| Ref. [1]<br />
|-<br />
| K<sub>LuxR</sub><br />
| 55 - 520 [nM]<br />
| LuxR activator dissociation constant<br />
| Ref: [6]<br />
|-<br />
| K<sub>AHL</sub><br />
| 0.09 - 1 [&#181;M]<br />
| AHL-LuxR activator dissociation constant<br />
| Ref: [6]<br />
|-<br />
| K<sub>CI</sub><br />
|<br />
*8 [pM]<br />
*50 [nM]<br />
| CI repressor dissociation constant<br />
|<br />
*Ref. [12]<br />
*starting with values of Ref. [6] and using Ref. [3]<br />
|-<br />
| K<sub>P22CII</sub><br />
| 0.577 [&#181;M]<br />
| P22CII repressor dissociation constant<br />
| Ref. [11]. Note that they use a protein CII and we have P22CII. Does that match?<br />
|-<br />
|}<br />
<br />
=== Hill cooperativity ===<br />
{| class="wikitable" width="100%" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
!width="10%"| Parameter <br />
!width="10%"| Value<br />
!width="20%"| Description<br />
!width="60%"| Comments<br />
|-<br />
| n<sub>LacI</sub><br />
| <br />
* 1<br />
* 2<br />
| LacI repressor Hill cooperativity<br />
| <br />
* Ref. [5]<br />
* Ref. [12]<br />
|-<br />
| n<sub>IPTG</sub><br />
| 2<br />
| IPTG-LacI repressor Hill cooperativity<br />
| Ref. [5]<br />
|-<br />
| n<sub>TetR</sub><br />
| 3<br />
| TetR repressor Hill cooperativity<br />
| Ref. [3]<br />
|-<br />
| n<sub>ATC</sub><br />
| 2 (1.5-2.5)<br />
| ATC-TetR repressor Hill cooperativity<br />
|Ref. [3]<br />
|-<br />
| n<sub>LuxR</sub><br />
| 2<br />
| LuxR activator Hill cooperativity<br />
| Ref: [6]<br />
|-<br />
| n<sub>AHL</sub><br />
| 1<br />
| AHL-LuxR activator Hill cooperativity<br />
| Ref. [3]<br />
|-<br />
| n<sub>CI</sub><br />
| 2<br />
| CI repressor Hill cooperativity<br />
| Ref. [12]<br />
|-<br />
| n<sub>P22CII</sub><br />
| 4<br />
| P22CII repressor Hill cooperativity<br />
| Ref. [11]. Note that they use a protein CII and we have P22CII. Does that match?<br />
|-<br />
|}<br />
<br />
<br><br />
<br />
== References ==<br />
<p><br />
[http://www.pnas.org/cgi/content/abstract/104/8/2643 &#91;1&#93; Weber W et al.] <i>"A synthetic time-delay circuit in mammalian cells and mice"</i>, P Natl Acad Sci USA 104(8):2643-2648, 2007<br /><br />
[http://www.pnas.org/cgi/content/full/100/13/7702?ck=nck &#91;2&#93; Setty Y et al.] <i>"Detailed map of a cis-regulatory input function"</i>, P Natl Acad Sci USA 100(13):7702-7707, 2003<br /><br />
[http://ieeexplore.ieee.org/iel5/9711/30654/01416417.pdf &#91;3&#93; Braun D et al.] <i>"Parameter Estimation for Two Synthetic Gene Networks: A Case Study"</i>, ICASSP 5:769-772, 2005<br /><br />
[http://www.nature.com/nature/journal/v435/n7038/suppinfo/nature03508.html &#91;4&#93; Fung E et al.] <i>"A synthetic gene--metabolic oscillator"</i>, Nature 435:118-122, 2005 (supplementary material)<br /><br />
[http://dx.doi.org/10.1016/j.jbiotec.2005.08.030 &#91;5&#93; Iadevaia S and Mantzais NV] <i>"Genetic network driven control of PHBV copolymer composition"</i>, J Biotechnol 122(1):99-121, 2006<br /><br />
[http://dx.doi.org/10.1016/j.biosystems.2005.04.006 &#91;6&#93; Goryachev AB et al.] <i>"Systems analysis of a quorum sensing network: Design constraints imposed by the functional requirements, network topology and kinetic constants"</i>, Biosystems 83(2-3):178-187, 2004<br /><br />
[http://www.genetics.org/cgi/content/abstract/149/4/1633 &#91;7&#93; Arkin A et al.] <i>"Stochastic kinetic analysis of developmental pathway bifurcation in phage λ-Infected Escherichia coli cells"</i>, Genetics 149: 1633-1648, 1998<br /><br />
[http://download.cell.com/supplementarydata/cell/107/6/739/DC1/index.htm &#91;8&#93; Colman-Lerner A et al.] <i>"Yeast Cbk1 and Mob2 Activate Daughter-Specific Genetic Programs to Induce Asymmetric Cell Fates"</i>, Cell 107(6): 739-750, 2001 (supplementary material)<br /><br />
[http://www.nature.com/nature/journal/v405/n6786/abs/405590a0.html &#91;9&#93; Becskei A and Serrano L] <i>"Engineering stability in gene networks by autoregulation"</i>, Nature 405: 590-593, 2000<br /><br />
[http://www.biophysj.org/cgi/content/full/89/6/3873?maxtoshow=&HITS=10&hits=10&RESULTFORMAT=&searchid=1&FIRSTINDEX=0&volume=89&firstpage=3873&resourcetype=HWCIT &#91;10&#93; Tuttle et al.] <i>"Model-Driven Designs of an Oscillating Gene Network"</i>, Biophys J 89(6):3873-3883, 2005<br /><br />
[http://www.pnas.org/cgi/reprint/99/2/679 &#91;11&#93; McMillen LM et al.] <i>"Synchronizing genetic relaxation oscillators by intercell signaling"</i>, P Natl Acad Sci USA 99(2):679-684, 2002<br /><br />
[http://www.nature.com/nature/journal/v434/n7037/full/nature03461.html &#91;12&#93; Basu S et al.] <i>"A synthetic multicellular system for programmed pattern formation"</i>, Nature 434:1130-1134, 2005<br /></div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/FSMETHZ/FSM2007-10-24T23:39:44Z<p>LiNan: </p>
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=Finite State Machine View on the System =<br />
<br />
[[Image:FSM.png|thumb|350px|<b>Fig. 1</b>: Graph representing the finite state machine.]]<br />
The proposed system is best described by a [http://en.wikipedia.org/wiki/Mealy_machine Mealy machine], a special type of [http://en.wikipedia.org/wiki/Finite_state_machine finite state machines] (FSM). Mealy machines are defined by a 6-tuple, (''Q'', ''q''<sub>0</sub>, Σ, Λ, ''δ'', ''Ω''), with:<br />
* ''Q'' - a set of states, for the proposed system we use three different states (''q''<sub>0</sub> - not yet trained, ''q''<sub>1</sub> - trained to recognize chemical A, ''q''<sub>2</sub> - trained to recognize chemical B)<br />
* ''q''<sub>0</sub> - a start state, here we assume we start in a state where the system is not yet trained<br />
* Σ = {A+L, A, B+L, B} - an input alphabet<br />
* Λ = {''green'', ''red'', ''blue'', ''yellow''} - an output alphabet<br />
* ''δ'' : ''Q'' × Σ → ''Q'' - a state transition function<br />
* ''Ω'' : ''Q'' × Σ → Λ - an output function<br />
<br />
In detail, the transition function ''δ'' and the output function ''Ω'' look as follows:<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
|- align="center"<br />
! inputs/states !! ''q''<sub>0</sub> !! ''q''<sub>1</sub> !! ''q''<sub>2</sub> !! width="50" |<br />
! inputs/states !! ''q''<sub>0</sub> !! ''q''<sub>1</sub> !! ''q''<sub>2</sub><br />
|- align="center"<br />
! A+L || ''q''<sub>1</sub> || ''q''<sub>1</sub> || ''q''<sub>1</sub> ||<br />
! A+L || ''cyan'' || ''cyan'' || ''green''<br />
|- align="center"<br />
! A || ''q''<sub>0</sub> || ''q''<sub>0</sub> || ''q''<sub>0</sub> ||<br />
! A || ''cyan'' || ''cyan'' || ''cyan''<br />
|- align="center"<br />
! B+L || ''q''<sub>2</sub> || ''q''<sub>2</sub> || ''q''<sub>2</sub> ||<br />
! B+L || ''yellow'' || ''red'' || ''yellow'' <br />
|- align="center"<br />
! B || ''q''<sub>0</sub> || ''q''<sub>0</sub> || ''q''<sub>0</sub> ||<br />
! B || ''yellow'' || ''yellow'' || ''yellow''<br />
|- align="center"<br />
|}<br />
The resulting automaton is shown in Fig. 1. <br /><br />
In its native state (''q''<sub>0</sub>), the system reacts on the presence of either chemical A or B by fluorescing ''cyan'' or ''yellow'', respectively. The system itself remains in state ''q''<sub>0</sub>. Only when the input chemical is combined with a learning signal (chemical L), it changes its state either to ''q''<sub>1</sub> (in presence of chemical A) or ''q''<sub>2</sub> (in presence of chemical B). In these two states, the system is able to distinguish between changing input: while the system is in state ''q''<sub>1</sub>, the system starts fluorescing ''red'' if confronted with chemical B and vice versa when the system is in state ''q''<sub>2</sub> and confronted with chemical A (it fluoresces ''green''). In order to maintain the states ''q''<sub>1</sub> and ''q''<sub>2</sub>, the learning signal L has to be present all the time.</div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/FlipFlopETHZ/FlipFlop2007-10-24T23:39:38Z<p>LiNan: </p>
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=An Engineer's View on Biological Learning=<br />
<br />
[[Image:FlipFlop.png|thumb|450px|<b>Fig. 1</b>: Schematic view on the system implemented by logical gates.]]<br />
An equivalent system behavior can be realized using [http://en.wikipedia.org/wiki/Flip-flop_%28electronics%29 flip-flops], implemented by [http://en.wikipedia.org/wiki/Logic_gate logical gates]:<br />
<br />
As already shown in the [https://2007.igem.org/ETHZ/FSM finite state machine representation] (FSM), the proposed system consists of three different states and recognizes four different inputs. In total, this adds up to twelve different transitions. In order to implement these twelve transitions with a digital system, at least four binary inputs are required (allowing for a maximum of 16 transitions) and since the proposed system has four different outputs, at least two binary outputs are required. Here, we chose an implementation using two JK flip-flops consisting of [http://en.wikipedia.org/wiki/Nand_gate#NAND_gate NAND gates]. An overview of the resulting system is given in Fig. 1 and details concerning flip-flop and NAND gate behavior are given in the following table:<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
! J !! K !! ''Q''<sub>next</sub> !! width="50" | !! A !! B !! A NAND B<br />
|- align="center"<br />
| 0 || 0 || ''Q''<sub>prev</sub> || || 0 || 0 || 1<br />
|- align="center"<br />
| 0 || 1 || 0 || || 0 || 1 || 1<br />
|- align="center"<br />
| 1 || 0 || 1 || || 1 || 0 || 1<br />
|- align="center"<br />
| 1 || 1 || [http://en.wikipedia.org/wiki/Inverse_%28logic%29 ¬]''Q''<sub>prev</sub> || || 1 || 1 || 0<br />
|}<br />
<br />
When initializing the system we set both outputs ''Q'' to zero. The mapping from inputs ([https://2007.igem.org/ETHZ/FSM AHL+IPTG, IPTG, AHL+aTc, aTc]) and system states ([https://2007.igem.org/ETHZ/FSM ''q''<sub>0</sub>, ''q''<sub>1</sub>, ''q''<sub>2</sub>]) to the binary inputs (J<sub>1</sub>, K<sub>1</sub>, J<sub>2</sub>, K<sub>2</sub>) is given by following table:<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
!states/inputs !! AHL+IPTG !! IPTG !! AHL+aTc !! aTc<br />
|- align="center"<br />
! ''q''<sub>0</sub> <br />
|J<sub>1</sub>=0, K<sub>1</sub>=1, J<sub>2</sub>=1, K<sub>2</sub>=0<br />
|J<sub>1</sub>=0, K<sub>1</sub>=0, J<sub>2</sub>=0, K<sub>2</sub>=1<br />
|J<sub>1</sub>=1, K<sub>1</sub>=0, J<sub>2</sub>=0, K<sub>2</sub>=1<br />
|J<sub>1</sub>=0, K<sub>1</sub>=1, J<sub>2</sub>=0, K<sub>2</sub>=1<br />
|- align="center"<br />
! ''q''<sub>1</sub> <br />
|J<sub>1</sub>=0, K<sub>1</sub>=1, J<sub>2</sub>=1, K<sub>2</sub>=0<br />
|J<sub>1</sub>=0, K<sub>1</sub>=0, J<sub>2</sub>=0, K<sub>2</sub>=1<br />
|J<sub>1</sub>=0, K<sub>1</sub>=1, J<sub>2</sub>=0, K<sub>2</sub>=0<br />
|J<sub>1</sub>=0, K<sub>1</sub>=0, J<sub>2</sub>=1, K<sub>2</sub>=1<br />
|- align="center"<br />
! ''q''<sub>2</sub><br />
|J<sub>1</sub>=1, K<sub>1</sub>=0, J<sub>2</sub>=0, K<sub>2</sub>=0<br />
|J<sub>1</sub>=1, K<sub>1</sub>=1, J<sub>2</sub>=0, K<sub>2</sub>=0<br />
|J<sub>1</sub>=1, K<sub>1</sub>=0, J<sub>2</sub>=0, K<sub>2</sub>=1<br />
|J<sub>1</sub>=0, K<sub>1</sub>=1, J<sub>2</sub>=0, K<sub>2</sub>=1<br />
|}<br />
<br />
Here, we assume that the FSM system states ''q''<sub>0</sub>, ''q''<sub>1</sub>, ''q''<sub>2</sub> are mapped to the outputs ''Q''<sub>1</sub> and ''Q''<sub>2</sub> as follows:<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
!FSM states/output coding !! ''Q''<sub>1</sub> !! ''Q''<sub>2</sub><br />
|- align="center"<br />
!''q''<sub>0</sub>||0||0<br />
|- align="center"<br />
!''q''<sub>0</sub>||0||1<br />
|- align="center"<br />
!''q''<sub>0</sub>||1||0<br />
|}<br />
<br />
The final mapping from transitions to the biological reporters is then given by:<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
!J<sub>1</sub>=0, K<sub>1</sub>=1, J<sub>2</sub>=1, K<sub>2</sub>=0<br />
!J<sub>1</sub>=0, K<sub>1</sub>=0, J<sub>2</sub>=0, K<sub>2</sub>=1<br />
!J<sub>1</sub>=1, K<sub>1</sub>=0, J<sub>2</sub>=0, K<sub>2</sub>=1<br />
!J<sub>1</sub>=0, K<sub>1</sub>=1, J<sub>2</sub>=0, K<sub>2</sub>=1<br />
!J<sub>1</sub>=0, K<sub>1</sub>=1, J<sub>2</sub>=1, K<sub>2</sub>=0<br />
!J<sub>1</sub>=0, K<sub>1</sub>=0, J<sub>2</sub>=0, K<sub>2</sub>=1<br />
!J<sub>1</sub>=0, K<sub>1</sub>=1, J<sub>2</sub>=0, K<sub>2</sub>=0<br />
!J<sub>1</sub>=0, K<sub>1</sub>=0, J<sub>2</sub>=1, K<sub>2</sub>=1<br />
!J<sub>1</sub>=1, K<sub>1</sub>=0, J<sub>2</sub>=0, K<sub>2</sub>=0<br />
!J<sub>1</sub>=1, K<sub>1</sub>=1, J<sub>2</sub>=0, K<sub>2</sub>=0<br />
!J<sub>1</sub>=1, K<sub>1</sub>=0, J<sub>2</sub>=0, K<sub>2</sub>=1<br />
!J<sub>1</sub>=0, K<sub>1</sub>=1, J<sub>2</sub>=0, K<sub>2</sub>=1<br />
|- align="center"<br />
| ''cyan''<br />
| ''cyan''<br />
| ''red''<br />
| ''cyan''<br />
| ''cyan''<br />
| ''cyan''<br />
| ''yellow''<br />
| ''green''<br />
| ''cyan''<br />
| ''cyan''<br />
| ''cyan''<br />
| ''yellow''<br />
|}</div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/SimulationETHZ/Simulation2007-10-24T23:39:31Z<p>LiNan: </p>
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=Introduction=<br />
<br />
As has been presented in the [[ETHZ/Model | Modeling]] page, we have created a model of our system that can be described as a finite state machine. In order to examine the behavior of our system thoroughly, we have to simulate it through all the difference phases that can be reached. We carry out our simulations based on the protocol presented in Fig. 1.<br />
<br />
[[Image:ETHzFlowdiagram2.png|center|thumb|<b>Fig. 1</b>: Flow diagram. This figure shows the protocol with which the final system should be tested, as well as the test results in the form of the reported colors. There are three phases the system has to go through: (1) a training or learning phase in which the system learns an input and stores it in its memory, (2) a memory phase in which the system has to keep the content of its memory and, (3) a recognition phase where the output of the system depends on the content of its memory as well as on the current input. |500px]]<br />
<br />
== Simulation of Test Cases ==<br />
To test all the possible conditions and transitions in our system, we run timing simulations for all 4 different branches in the flow diagram of our protocol, and we plot the concentrations of the inducers, memory proteins and reporter proteins. During each learning, memorizing and recognizing phase, we wait for the steady state of the system to be reached. The three phases of the system are thus separated sufficiently in time. In all the cases, we follow the following procedure:<br />
* During the first 1000 minutes of the simulation, we do not add any inducers to the system, and as a result, we check for the baseline production of proteins.<br />
* Between 1000 and 2000 minutes, we add the inducer that should be learned, and we let the system reach steady state.<br />
* After 2000 minutes, we add the inducer AHL for memory formation, and run the simulation for an additional 1000 minutes, to reach again steady state.<br />
* Between 3000 and 4000 minutes, we test the final behavior of the system, and check if it reacts in the desired way, by showing the appropriate color.<br />
<br />
The parameters of the system are crucial if one wants to have accurate and realistic simulations. We present the parameters used to simulate our system, in the section [[ETHZ/Parameters | Parameters]].<br />
<br />
<p>[[Image:ETHZTest1_Stand24.10.png|left|thumb|<b>Fig. 2</b>: Test case 1 - Learn and recognize IPTG. The simulated behavior of the system, when IPTG is presented both during the learning phase, and the recognition phase. In both the learning and recognition phase, the system reports by producing CFP, which matches the desired behavior. |420px]]<br />
[[Image:ETHZTest2_Stand24.10.png|thumb|<b>Fig. 3</b>: Test case 2 - Learn IPTG and get tested for aTc. The simulated behavior of the system, when IPTG is presented during the learning phase, and aTc is presented during the recognition phase. The system reports by producing RFP during the recognition phase, which matches the desired behavior. |420px]]<br />
<br />
[[Image:ETHZTest4_Stand24.10.png|left|thumb|<b>Fig. 4</b>: Test case 3 - Learn aTc and get tested for IPTG. The simulated behavior of the system, when aTc is presented during the learning phase, and IPTG is presented during the recognition phase. The system reports by producing GFP during the recognition phase, which matches the desired behavior. |420px]]</p><br />
<br />
[[Image:ETHZTest3_Stand24.10.png|right|thumb|<b>Fig. 5</b>: Test case 4 - Learn and recognize aTc. The simulated behavior of the system, when aTc is presented both during the learning phase and the recognition phase. In both the learning and recognition phase the system reports by producing YFP, which matches the desired behavior. |420px]]<br />
<br />
==Sensitivity Analysis==<br />
<br />
Before performing simulations we did an intensive literature review to find the parameters of our system. Due to the uncertainty in parameter retrieval, we would like our system to have reduced sensitivity to the parameters. That way, we can have a guaranteed stable performance, even under non-simulated conditions.<br />
<br />
[[Image:ETHZSensitivity.png|right|thumb|<b>Fig. 6</b>: Sensitivity analysis. We plot the sensitivity for each parameter and each test case that corresponds to the different path ways in Fig. 1. |500px]]<br />
<br />
We define sensitivity as the change of the production of the desired florescence protein - which corresponds to the output of our system - depending on the change of the parameters. As shown in Fig. 1 the desired florescence protein output depends on the inducers that we present to our system during learning and recognition phase. Overall we have 4 different combinations leading to the 4 different test cases shown in Fig. 1. E.g. when IPTG is presented to the system both during the learning and recognition phase we expect the production of CFPs. That is why we define the sensitivity for test case 1 as<br />
<br />
[[Image:ETHZSensititivityequation.png|center|300px]]<br />
<br />
<!---<math>Sensitivity = \left | \frac{[ CFP^{105%}_{recognition} ] }{\left [ CFP^{105%}_{baseline}\right ] }- \frac{\left [ CFP^{95%}_{recognition} \right ] }{\left [ CFP^{95%}_{baseline} \right ] }\right |</math>---><br />
<br />
where <br />
* [CFP<sub>recognition</sub><sup>105%</sup>] describes the concentration of CFP during the recognition phase when the parameter that we test for is increased by 5%, <br />
* [CFP<sub>baseline</sub><sup>105%</sup>] describes the concentration of CFP without any inducers given to the system when the parameter that we test for is increased by 5%,<br />
* [CFP<sub>recognition</sub><sup>95%</sup>] describes the concentration of CFP during the recognition phase when the parameter that we test for is decreased by 5%, and <br />
* [CFP<sub>baseline</sub><sup>95%</sup>] describes the concentration of CFP without any inducers given to the system when the parameter that we test for is decreased by 5%.<br />
<br />
All concentrations are gained from timing simulations as shown in Fig. 2 when the concentrations reached steady state. E.g. [CFP<sub>recognition</sub>] corresponds to the value of CFP after 4000 minutes while [CFP<sub>baseline</sub>] is equal to the value for CFP after 1000 minutes.<br />
<br />
We define sensitivity by using the ratio of [CFP<sub>recognition</sub>] and [CFP<sub>baseline</sub>] to see by which factor the CFP concentration is increased during the recognition phase. The higher this value is the easier it will be possible to determine the correct readout.<br />
<br />
We subtract the two ratios of [CFP<sub>recognition</sub>]/[CFP<sub>baseline</sub>] for increasing and decreasing the parameter under test by 5% to quantify how much is the influence of the parameter on the output. If the output ratio does not change the parameter has no influence and the sensitivity is equal to zero.<br />
<br />
We quantified the sensitivity for all parameters and all 4 test cases. Therefor we look at the concentration of <br />
* CFP in test case 1. A timing simulation for this case is shown in Fig. 2.<br />
* RFP in test case 2. A timing simulation for this case is shown in Fig. 3.<br />
* GFP in test case 3. A timing simulation for this case is shown in Fig. 4.<br />
* YFP in test case 4. A timing simulation for this case is shown in Fig. 5.<br />
The results are plotted in Fig. 6. Furthermore we make sure that no undesired florescence protein is produced during the recognition phase.<br />
<br />
The conclusions that can be derived from our sensitivity analysis are the following:<br />
* Most of the parameters are not sensitive which corresponds to the desired behavior since the system should ideally give a constant output for the same input sequence.<br />
* Our system is most sensitive to the parameter "a", which describes the base production. This is not a surprising result, since we depend on having a good switch. However, we cannot reliably switch off the production of the memory proteins cI and p22cII. This leads to a small signal to noise ration and in the worst case the memory does not work at all. For a more detailed analysis of the switching behavior please see our [[ETHZ/Biology | biological implementation]] page.<br />
* Other sensitive parameters are those that are directly related to the production and decay of the memory proteins cI and p22cII. This can be explained by the fact that our system prefers symmetry between the parameter sets of cI and p22cII, in order to support good switching behavior where the same concentration of p22cII leads to the same repression of cI and vice versa. We can especially see that those parameters seem to be more sensitive for the test cases 1 and 2, where cI has to be stored inside the memory. The reason for this behavior is due to the missing symmetry between the parameters for cI and p22cII, we currently have a bias inside the system towards the production of p22cII.</div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/BiologyETHZ/Biology2007-10-24T23:39:02Z<p>LiNan: </p>
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= Introduction =<br />
<br />
On this page, you can find an analysis of the function of our system, its biological design, and a list of the parts that make up the system. Under [https://2007.igem.org/ETHZ/Biology/Lab Lab Notes], you can find the ingredients and equipment we used, the electronic version of our lab notebook and a presentation of all the difficulties that we encountered.<br />
<br />
EducatETH <i>E.coli</i> is a system which can distinguish between [http://openwetware.org/wiki/ATc anhydrotetracycline (aTc)] and [http://openwetware.org/wiki/IPTG Isopropyl-beta-D-thiogalactopyranoside (IPTG)] based on a previous learning phase conducted with the same chemicals and the help of [http://partsregistry.org/Acyl-HSLs AHL]. It is composed of three subsystems: the subsystem of constitutively produced proteins, the learning subsystem and the reporting subsystem. The constitutively produced proteins (LacI, TetR and LuxR) control the learning subsystem. At the core of the latter there exists an extended version of the original toggle switch found in [1]. That is, a multi-inducible toggle switch. The main difference is reflected in the use of double promoters, so that the toggle switch only changes its state when both, one of the two chemicals (aTc/IPTG), and AHL are present. As AHL is only present during the learning phase, the toggle maintains its state during testing/recognition, and thus can “memorize”. AHL can therefore be seen as a training- or learning substance. In the reporting subsystem, four reporters ([http://partsregistry.org/Featured_Parts:Fluorescent_proteins fluorescent proteins]) allow supervision of (1.) the chemical the system was trained with and (2.) if the system recognizes the chemical it is being exposed to in the recognition phase as one it has been previously trained with or not.<br />
<br />
== The Complete System ==<br />
<br />
<p>[[Image:Biol_system_stand24.10.png|thumb|left|300px|'''Fig. 1:''' Gene interaction network of educatETH ''E.coli'' ]] The biological design of educatETH <i>E.coli</i> is presented in Fig. 1 and below, we clarify the function of all depicted components. (Are you interested in how the complex system of Fig. 1 was modeled? Then visit the [[ETHZ/Model| System Modeling]]!)</p><br />
<br />
==== Constitutive Subsystem ====<br />
<br />
<p>The constitutively produced proteins of the system are LacI, TetR and LuxR. The LuxR part has a special function: when AHL is present, it forms a LuxR-AHL complex which acts on the learning subsystem (more on this later). For now, we will consider that AHL is absent and therefore LuxR cannot act on any subsystems. The TetR and LacI parts behave similarly: more specifically, the TetR protein in the absence of aTc inhibits the production of p22cII and LacI in the absence of IPTG inhibits the production of cI. When aTc is present, however, the p22cII production is no longer inhibited (and thus p22cII is produced). Correspondingly, cI is produced when IPTG is present.</p><br />
<br />
==== Learning Subsystem ====<br />
<br />
<p>The learning subsystem is a toggle switch with two operator sites. The upper part of the toggle (cI production) has operator sites for the LuxR-AHL complex and p22cII (which has in turn been induced by aTc). The LuxR-AHL complex induces cI production, whereas p22cII inhibits it. The lower part of the toggle (p22cII production) has operator sites for the LuxR-AHL complex and cI (which has been induced by IPTG). In analogy to the upper part, the LuxR-AHL complex induces production of p22cII and cI inhibits it. Therefore, the switch always requires the presence of the LuxR-AHL complex in order for it to operate. Its state depends on the presence of p22cII and cI in the system, which in turn was caused through the exposure of the system to aTc and IPTG.</p><br />
<br />
==== Reporting Subsystem ====<br />
<br />
<p>There are four reporters in the system. CFP (more precisely: enhanced CFP, that is ECFP) and YFP (more precisely: enhanced YFP, that is EYFP) are active during the learning phase of the system and show which chemical the system is exposed to during learning, whereas all four reporters (the latter and GFP and RFP) are active during the recognition phase and show if the system is exposed to the same chemical as in learning or not. <br />
More specifically, the YFP production is regulated with help of two operator sites controlled by cI and aTc (TetR inhibitor). cI inhibits the YFP production and aTc induces it. Therefore, YFP is synthesized when the system is exposed to only aTc and cI is not produced within the system (i.e. the system has not been previously exposed to IPTG). The production of the other fluorescent proteins is regulated in a similar manner. Overall, the production of the fluorescent proteins is regulated as follows:<br />
*YFP gets produced when the system is exposed to only aTc and no cI is produced (i.e. the system has ''not'' been previously exposed to IPTG).<br />
*CFP gets produced when the system is exposed to only IPTG and no p22cII is produced (i.e. the system has ''not'' been previously exposed to aTc).<br />
*GFP gets produced when the system is exposed to only IPTG and no cI is produced (i.e. the system has ''not'' been previously exposed to IPTG).<br />
*RFP gets produced when the system is exposed to only aTc and no p22cII is produced (i.e. the system has ''not'' been previously exposed to aTc).</p><br />
<br />
This behaviour is visualized in Fig. 2.<br />
[[Image:ETHzFlowdiagram2.png|center|thumb|<b>Fig. 2</b>: Flow diagram. This figure shows the protocol with which the final system should be tested, as well as the test results in the form of the reported colors. There are three phases the system has to go through: (1) a training or learning phase in which the system learns an input and stores it in its memory, (2) a memory phase in which the system has to keep the content of its memory and, (3) a recognition phase where the output of the system depends on the content of its memory as well as on the current input. |500px]]<br />
<br />
== System Phases ==<br />
<br />
<p>The system operation is divided into three main phases: a learning phase, a memory phase and a recognition phase. During the learning phase, the system is first exposed to one of the two chemicals it is designed to detect (aTc or IPTG). During the memory phase, the specific chemical (aTc or IPTG) is removed and AHL is added to activate the systems internal toggle switch. This maintains the toggle switch to its acquired steady state, which is reported with YFP (if aTc was detected) or CFP (if IPTG was detected). During the recognition phase, the system is exposed to any of the two chemicals (aTc or IPTG), with AHL present. Lets compare the systems toggle switch state with the effect of the newly introduced chemical: the system shows a different response if it has previously been exposed to this certain chemical and reports with the same XFP as in the learning phase (YFP for aTc, CFP for IPTG) or if it recognizes a different chemical and reports with a different XFP (GFP for trained with aTc and recognizing IPTG, RFP for trained with IPTG and recognizing aTc). The following table represents all possible paths that may be taken by the system during all phases of operation according to external stimuli: </p><br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:center; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;" <br />
|+ '''System phases''' <br />
! <br />
!width="44" style="background:#446084; color:white"| aTc<br />
!width="44" style="background:#446084; color:white"| IPTG<br />
!width="44" style="background:#446084; color:white"| AHL<br />
!width="44" style="background:#446084; color:white"| p22cII<br />
!width="44" style="background:#446084; color:white"| cI<br />
! style="background:#446084; color:white"| Reporting <br />
|- <br />
|colspan="7" style="background:#96c9cf;" align="center"|'''Start''' <br />
|- <br />
| no input<br />
| no<br />
| no<br />
| no<br />
| no<br />
| no<br />
| non<br />
|- <br />
| colspan="7" style="background:#96c9cf;" align="center"| '''Learning'''<br />
|- <br />
| Trained with aTc<br />
| yes<br />
| no<br />
| no<br />
| yes<br />
| no<br />
| YFP<br />
|- <br />
| Trained with IPTG<br />
| no<br />
| yes<br />
| no<br />
| no<br />
| yes<br />
| CFP<br />
|- <br />
| colspan="7" style="background:#96c9cf;" align="center"| '''Memorizing'''<br />
|- <br />
| Trained with aTc<br />
| yes<br />
| no<br />
| yes<br />
| yes<br />
| no<br />
| YFP (fading)<br>finally no color<br />
|- <br />
| Trained with IPTG<br />
| no<br />
| yes<br />
| yes<br />
| no<br />
| yes<br />
| CFP (fading)<br>finally no color<br />
|- <br />
| colspan="7" style="background:#96c9cf;" align="center"| '''Recognition'''<br />
|-<br />
| Trained with aTc<br>Tested with aTc<br />
| yes<br />
| no<br />
| yes<br />
| yes<br />
| no<br />
| YFP<br />
|- <br />
| Trained with aTc<br>Tested with IPTG<br />
| no<br />
| yes<br />
| yes<br />
| yes<br />
| no<br />
| GFP<br />
|- <br />
| Trained with IPTG<br>Tested with IPTG<br />
| no<br />
| yes<br />
| yes<br />
| no<br />
| yes<br />
| CFP<br />
|- <br />
| Trained with IPTG<br>Tested with aTc<br />
| yes<br />
| no<br />
| yes<br />
| no<br />
| yes<br />
| RFP<br />
|- <br />
<br />
|}<br />
<br />
== System Parts ==<br />
<br />
<p>educatETH <i>E.coli</i> was implemented with 11 basic parts designed by the ETH Zurich team. [https://2007.igem.org/wiki/index.php?title=ETHZ/Biology/parts The list of all the parts, plasmids and strains used] is available. Because the part information is retrieved from the Registry, the page needs some time to load. <br>(Are you interested in this information because you want to implement educatETH <i>E.coli</i> in your lab? Then visit our [https://2007.igem.org/ETHZ/Biology/Lab In the Lab] page!)</p><br />
<br />
== References ==<br />
<p><br />
[http://www.nature.com/nature/journal/v403/n6767/abs/403339a0.html &#91;1&#93; Gardner TS, Cantor CR and Collins JJ] <i>"Construction of a genetic toggle switch in Escherichia coli"</i>, Nature 403:339–342, 2000<br /><br />
<br />
== To Do ==<br />
<br />
=== New ===<br />
<p><ul><br />
<li> <span style=color:blue;>Katerina's note: Put Sylke's new scheme on this page. Note: the scheme is ready, it just needs to be uploaded. DONE</span><br />
<br />
</ul></p><br></div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/Modeling_BasicsETHZ/Modeling Basics2007-10-24T23:38:50Z<p>LiNan: </p>
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<br />
=Modeling Basics=<br />
<br />
The functioning of our model depends mostly on ''protein concentrations''. These proteins are produced within the ''E.coli'' cells, based on genes that we introduced. To understand the system, it is crucial to model the gene expression, gene regulation, and the resulting gene product concentrations accurately.<br />
<br />
This Wiki page is intended to present the basic mechanisms and assumptions that went into the mathematical description of our iGEM model.<br />
<br />
== Constitutive Protein Production ==<br />
<br />
The most simple case, proteins are produced through continuous transcription of genes. At the same time, proteins have a certain half-life time, which means that they are degraded. This leads to the following simple model of protein production/degradation shown in Fig. 1.<br />
<br />
[[Image:basic_fig01.png|thumb|<b>Fig. 1</b>: A system constitutively producing protein P. The production rate is c<sup>max</sup> and the degradation rate is d<sub>P</sub>.]]<br />
<br />
To find the concentration of protein P (as a function of time), the system of Fig. 1 can be written as [http://en.wikipedia.org/wiki/Ordinary_differential_equation ordinary differential equation] (ODE):<br />
<br />
[[Image:basic_eq01.png|center|150px]]<br />
<br />
This equation states that the change of protein concentration is a function of protein production (c<sup>max</sup>) and protein degradation (d<sub>P</sub>[P]). <br />
<br />
It is worth looking at the protein production a bit closer: The production of protein P depends on the expression of a gene that codes for this protein. In the case of constant protein production, the gene can be modeled by a ''constitutive promoter'' and a coding region for the protein (Fig. 2).<br />
<br />
[[Image:basic_fig02.png|thumb|<b>Fig. 2</b>: Model for the gene coding for protein P. The promoter of the gene is continuously expressed.]]<br />
<br />
== Regulated Protein Production ==<br />
<br />
Often, genes are not constitutively expressed but their expression depends on the presence of other proteins R (i.e., transcription factors). These transcription factors can activate or inhibit the promoter of the gene in question. To model such a system, the promoter of the constitutive production system in Fig. 2 must be extended to take into account the presence of the regulatory protein R. Example systems for inhibition and activation of a promoter by R are given in Fig. 3 and Fig.4, respectively.<br />
<br />
[[Image:basic_fig03.png|thumb|<b>Fig. 3</b>: Regulated protein production where the regulatory protein has inhibitory effect. That is, the higher the concentration of R, the smaller the expression of protein P.]]<br />
<br />
[[Image:basic_fig04.png|thumb|<b>Fig. 4</b>: Regulated protein production where the regulatory protein activates protein production. That is, the higher the concentration of R, the larger the expression of protein P.]]<br />
<br />
=== Inhibition ===<br />
<br />
To derive the equations describing the regulated transcription, we first need a model how the transcription factor interacts with DNA. The most simple model assumes that the protein binds reversibly to the DNA:<br />
<br />
[[Image:basic_eq02.png|center]]<br />
<br />
Note that the equation above involves ''n'' transcription factors. For certain transcription factors the number of proteins involved is indeed greater than 1. These are interesting cases that enable applications such as toggle switches.<br />
<br />
When the transcription factor binds to DNA, it blocks the enzymes transcribing the gene. Thus, the higher the concentration of R, the smaller the transcription of the gene. By controlling the concentration of the regulatory protein R, the expression of protein P can effectively be regulated.<br />
<br />
To understand this process in more detail, we make the simplification that the binding of R to DNA is in ''equilibrium''. That is, one can write<br />
<br />
[[Image:basic_eq03.png|center|225px]]<br />
<br />
In case of inhibition, the expression of the gene is proportional to the probability that the DNA is 'free' (i.e., there is no transcription factor bound to it). After some algebraic manipulation of the above equation an expression for the 'free DNA' as a function of transcription factor concentration can be derived:<br />
<br />
[[Image:basic_eq04.png|center|152px]]<br />
<br />
Now, we basically have everything together to write down an ODE for the concentration of protein P whose expression is regulated by the regulatory protein R:<br />
<br />
[[Image:basic_eq05.png|center|253px]]<br />
<br />
The transcription does not always take place at the maximum rate ''c''<sup>max</sup> as was the case for the constitutively produced proteins, but is modulated by the concentration of protein R.<br />
<br />
=== Activation ===<br />
<br />
To understand activation, we start with the same assumption: a transcription factor reversibly binding to DNA.<br />
<br />
[[Image:basic_eq02.png|center]]<br />
<br />
Here, we are interested in the DNA - transcription factor complex because the transcription rate is proportional to the probability that a protein is bound to DNA.<br />
<br />
Again assuming equilibrium conditions, we can derive an expression for the DNA-protein complex concentration as a function of protein concentration:<br />
<br />
[[Image:basic_eq06.png|center|175px]]<br />
<br />
Analogously, we can now write the whole differential equation for protein concentration if transcription is activated by protein R<br />
<br />
[[Image:basic_eq07.png|center|253px]]<br />
<br />
== Basic Production ==<br />
<br />
The equations so far assume perfect inhibition/activation. This means, in case of inhibition, that if the inhibitor concentration is high enough, the transcription of protein P is practically zero. Or, in the case of activation, that in the absence of activator protein there is no transcription. In reality, one observes some ''basic production'', despite high inhibitor concentrations or absence of activator protein. The basic transcription is usually around 10-20% of the maximum transcription rate. The case of inhibition is illustrated in Fig. 5. [[Image:basic_fig05.png|thumb|<b>Fig. 5</b>: Inhibition of transcription factor is only effective between basic transcription and maximum transcription. That is, protein production cannot be shut off completely by inhibition.]] <br />
<br />
Thus we have to introduce some 'basic transcription' that always takes place. For inhibition we have<br />
<br />
[[Image:basic_eq08.png|center|361px]]<br />
<br />
and for activation<br />
<br />
[[Image:basic_eq09.png|center|361px]]<br />
<br />
Note that the basic transcription rate is introduced as a ''leakiness factor'' ''a'' which is a percentage of the maximum transcription rate ''c''<sup>max</sup>. Regulation of transcription by protein R now is only effective in the range between ''a''&middot;''c''<sup>max</sup> and ''c''<sup>max</sup>.<br />
<br />
== Inducer Molecules ==<br />
<br />
A problem in biology is that regulatory proteins (such as protein R in the previous sections) cannot be used as system inputs directly. It is not possible to add such proteins to an assay to steer the behavior of a biological system because these proteins are big and do not diffuse through cell walls. Therefore, they cannot enter the cells.<br />
<br />
To circumvent this limitation, one has to produce these proteins directly in the cell. But then, one needs a possibility to switch the functionality of these proteins on and off. This is where inducer molecules become useful. Inducer molecules are small molecules that can diffuse through cell walls freely. Furthermore, they are able to bind to the regulatory proteins and switch the functionality on or off.<br />
<br />
Thus, we need a model to describe the binding of the inducer to the protein. We again assume that the inducer I binds reversibly to the protein R<br />
<br />
[[Image:basic_eq10.png|center]]<br />
<br />
Further on, again we assume that the reaction is in equilibrium. We thus have<br />
<br />
[[Image:basic_eq11.png|center|157px]]<br />
<br />
Depending on the species at hand we are either interested in the protein-inducer complex concentration or in the concentration of 'free protein'. For some species (e.g., LuxR), the complex form is functional and for others it is the free protein (e.g., TetR).<br />
<br />
It is again possible to derive a formula for both, the concentration of free protein R and the complex concentration R-nI as a function of total inducer concentration (for notational convenience, we write R* for the complex R-nI in the following equations).<br />
<br />
[[Image:basic_eq12.png|center|157px]]<br />
<br />
<br><br />
<br />
[[Image:basic_eq13.png|center|157px]]<br />
<br />
<br />
== Caveats ==<br />
<br />
With this approach to modeling, the equations turn out to be both, easy to understand and to simulate. But to arrive at this point, a certain number of assumptions must be made. This section points out possible problems with these assumptions and ideas to further improve the modeling.<br />
<br />
* '''Equilibrium''': This is a very basic assumption that was the basis of all results in the above discussion. At the same time, it is also the least problematic assumption. This can be seen by remembering the massive machinery involved in transcription and translation of DNA/RNA compared to simple reversible binding of molecules. It is plausible to assume that the latter indeed happens on a much shorter time scale than the first.<br />
<br />
* '''Excess of substrate''': This assumption was implicitly made when we wrote down the equations of regulation and inducer binding. The basic idea is that the total inducer concentration is very close to the concentration of unbound inducer. Mathematically more precise, this condition is met when K<sub>I</sub> &gt;&gt; [R]<sub>t</sub>. As the uncertainty in the values of dissociation constants and steady state concentrations is sometimes very large, it is difficult to say whether this assumption is justified.<br />
<br />
* '''Mechanism dependence''': If the [http://en.wikipedia.org/wiki/Hill_coefficient Hill cooperativity coefficient ]''n'' is greater than one, the above formulas become in fact dependent on the mechanism of the binding process. In these cases, the formulas can be off up to a factor ''n''. As the true mechanism of binding is often unknown, it is practically impossible to take this fact into account.<br />
<br />
* '''Low protein concentration'''. Whenever the concentration is very low (say, below 100 nM in the case of ''E.coli''), the number of molecules per cell becomes small. In this situation, the assumptions behind the ODE modeling approach (e.g., well mixed compartment where each molecule can freely interact with other molecules) are not met any more and simulation results become inaccurate. Then, one would have to resort to stochastic simulations.</div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/ModelETHZ/Model2007-10-24T23:38:42Z<p>LiNan: </p>
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=Introduction=<br />
<br />
As previously discussed in the main page, we are interested in designing a system that is able to adapt to its environment. Our ideas are based on discussions about neural networks, and how we can create a biological system that exhibits the behavior of learning without having to resort to evolutionary processes. <br />
<br />
[[Image:ETHzFlowdiagram2.png|thumb|<b>Fig. 1</b>: Flow diagram. This figure shows the protocol with which the final system should be tested as well as the test results in form of the reported colors. The protocol is divided into three phases: (1) a training or learning phase in which the system learns an input and stores it in its memory, (2) a memory phase in which the system keeps the content of its memory, and finally (3) a recognition phase where the output of the system depends on the content of its memory as well as the current input.|450px]]<br />
<br />
Learning can be considered as a switching of behavior, based on some external stimuli. Thus, it comes naturally to work on existing ideas of toggle switches and [[ETHZ/FSM | finite state machines]]. <br />
<br />
The proposed system is able to distinguish between two chemicals. It represents a minimal test system that is intended as a proof of concept. By introducing the ability to distinguish more than two chemical and thereby introducing new [[ETHZ/FSM | system states]], the power of the system or its "intelligence" can be scaled. A protocol depicting how the system should react according to an input is shown in Fig. 1.<br />
<br />
The idea behind this protocol is that:<br />
* The system will be able to learn one of two input signals - aTc or IPTG - during a learning phase, while a "learning signal" (AHL) is not yet present. Depending on the input it will report by producing either cyan or yellow florescence. <br />
* Once the system has learned, the inputs - aTc or IPTG - can be removed and the system goes into a memory state in the presence of AHL. In this state, no output color is reported. Memorizing is guaranteed by removing the input chemicals.<br />
* During the recognition phase, the inputs aTc or IPTG are (re-)inserted. The system reports by changing its color depending on the input and its current memory state. This is why the system has different florescence properties even in the presence of the same input. The recognition phase takes place in the presence of AHL, to keep the memory enabled and avoid another learning phase. Since we would like to separate four different end states, we use four different fluorescent proteins to encode them.<br />
<br />
==Model Overview==<br />
<br />
The model for the proposed system is developed using a top-down approach. We start with a black box approach as shown in Fig. 2. <br />
<br />
[[Image:ETHZBlackbox.png|thumb|<b>Fig. 2</b>: Black box |280px]]<br />
<br />
The system is sketches in Fig. 3. It can be summarized as follows:<br />
* There are two inputs to be learned/detected/adapted to.<br />
* There is one separate input to switch on the memory.<br />
* The system has to alternate between at least three states. Hence, we decided to use two state variables - cI and p22cII (when interpreted as binary variables, in principle allowing for upt o four different states).<br />
* There are four different output signals (four fluorescent proteins). One could also decide to take six output signals into account to further distinguish the learning phase from the recognition phase. However, we restricted ourselves to four outputs to reduce the number of genes that are needed to implement the signals.<br />
<br />
[[Image:ETHZFullsystemmodel.png|left|thumb|<b>Fig. 3</b>: System overview. AHL, IPTG and aTc pass the cell membrane where they build complexes with the sensor proteins LuxR, LacI and TetR. These sensor proteins and/or complexes are used to control the internal system state: the memory represented by the proteins cI and p22cII (repressing each others the production) and the sensed input (IPTG, aTc). CFP, RFP, GFP and YFP stand for yellow, red, cyan and green florescent protein, respectively.|420px]]<br />
<br />
However, we had to keep in mind that the proposed system should be implemented in DNA, and that it would be sensitive to noise. As a result, we took several actions to achieve better experimental results and easier DNA construction:<br />
* To be more robust against perturbations, we coupled the state variables cI and p22cII like it is known from toggle switches [1]. Based on this approach, one state variable is depressing the other one, and the system's internal toggle has the possibility of reaching two stable states.<br />
* Since - due to their size - proteins can only hardly pass the cell membrane (if they are not actively transported through the cell membrane), we decided to use the much smaller inducer molecules AHL, IPTG and aTc as inputs. However, since these inducers cannot directly act on the transcription of the DNA nor on the production of proteins, we need to produce the sensor proteins LuxR, LacI and TetR that build complexes with AHL, IPTG and aTc, respectively.<br />
* The sensor proteins and complexes are used to control the memory formation and the production of the florescent reporter proteins CFP, RFP, GFP and YFP.<br><br><br />
<br />
==Detailed Model==<br />
<br />
In order to test our ideas, we came up with a detailed description of all the interactions in the system. <br />
Starting from the protocol described in Fig. 1 and a [[ETHZ/FSM | formalized few on the system]] we started to identify necessary biological components and their interactions. As we can observe in Fig. 3, our system is composed from three basic subparts:<br />
<br />
===Sensors===<br />
<br />
The first part contains the sensors. Our sensors are the proteins LacI, luxR and TetR, which are constitutively produced. The sensing subsystem is shown in Fig. 4.<br />
<br />
[[Image:Model01b.png|center|thumb|<b>Fig. 4</b>: The proteins that act as sensors are constitutively produced.|140px]]<br />
<br />
===Memory===<br />
<br />
The second subsystem is responsible for the creation and control of memories. The memory control is based on the following underlying mechanisms:<br />
* The sensor proteins form complexes together with the inducers. These complexes are used to either activate (in case of the complex consisting of luxR and AHL) or repress (in case of the complexes consisting of LacI and IPTG as well as TetR and aTc) the DNA transcription of the proteins cI and p22cII. <br />
* p22cII and cI repress the DNA transcription of each other, so that the closed loop system behaves as a toggle; a dynamic system with only two possible steady states (see Fig. 6).<br />
<br />
[[Image:ETHZModelLearning.png|center|thumb|<b>Fig. 5</b>: Learning system: Depending on the inputs IPTG or aTc the proteins cI and p22cII are produced.|300px]]<br />
<br />
* Fig. 5 shows the protein production system that is used during the learning phase. During the learning phase, there is still no cI or p22cII produced. They are produced, only if either IPTG or aTc is added, respectively. Since no AHL is present, the inner toggle switch (see Figure 6) is turned off.<br />
<br />
[[Image:ETHZModelMemory.png|center|thumb|<b>Fig. 6</b>: Memory system. If AHL is present the production of either cI or p22cII is continued.|420px]]<br />
<br />
* During the memory phase, AHL is added and the IPTG and aTc are removed. That is why only the inner toggle switch (see Fig. 6) is turned on while the protein production systems shown in Fig. 5 are deactivated. Depending on what was produced during the learning phase, the production of either cI or p22cII is continued. That is why the system can act as memory, effectively storing the information it is exposed to.<br />
<br />
Based on all the above, we present the final assembly of the memory subsystem in Fig. 7.<br />
<br />
[[Image:Model02b.png|center|thumb|<b>Fig. 7</b>: Final interaction of the learning and memory system. The memory content is represented by the concentrations of the proteins cI and p22cII.|560px]]<br />
<br />
===Reporters===<br />
<br />
Fig. 8 gives an overview of the reporter subsystem. Florescent reporter proteins are expressed depending on the inducer concentrations, and the concentrations of cI and p22cII. For example, the presence of either TetR or cI will repress the production of YFP. However, if the inducer aTc is present, aTc will bind to TetR which can no longer block the production of YFP. We are using four fluorescent proteins, to encode the steady states of our system at the final recognition stage. In this way, we are able to distinguish between all the different transition paths in the system.<br />
<br />
[[Image:Model03b.png|center|thumb|<b>Fig. 8</b>: The production of the florescent reporter proteins depends on the memory content (cI or p22cII) and the current input (aTc or IPTG).|600px]]<br />
<br />
==Final Model==<br />
<br />
So far, we have presented all parts needed to model and simulate the behavior of the proposed system with. By following the details presented in the previous section, we have all the necessary information to fully understand the interior of the black boxes that were presented in Fig. 2 and Fig. 3. Our overall system model is presented in Fig. 9.<br />
<br />
[[Image:ETHZFullsystem.png|center|thumb|<b>Fig. 9</b>: Final model of the EducatETH <i>E. coli</i> system.|900px]]<br />
<br />
==Mathematical Model==<br />
<br />
Based on the modeling done so far, we can derive the equations that govern the behavior of our system. The model is given by sets of coupled [[ETHZ/Modeling_Basics | ordinary differential equations]] which are presented below. We use a simple notation for the different elements of the equations. Namely: <br />
* All concentrations are given in brackets (for example [cI]). <br />
* All decay constants are described by a variable d followed by the name of the protein they refer to. <br />
* The production of the proteins is described by a basic constant production level that models the leak of the production system, and a factor of l and c<sub>max</sub> that describe the maximum production of a protein, given in [M]. <br />
* Depending on whether the DNA for a protein is implemented on a low or a high copy plasmid, we distinguish between l<sub>lo</sub> and l<sub>hi</sub>, respectively.<br />
<br />
For a more basic introduction into how we transferred our model into equations, see the section [[ETHZ/Modeling_Basics|Modeling Basics]].<br><br />
<br />
===Constitutively produced proteins===<br />
<br />
The equations for the constitutively produced proteins are very simple, since there is no dependence on other proteins. They are designed so that the protein concentration reaches the value l<sub>hi</sub>*c<sub>max</sub>/d at steady state.<br />
<br />
[[Image:Constitutive_braced.png|330px]]<br />
<br />
===Allosteric regulation===<br />
<br />
These equations describe the formation of complexes between the inducers and sensor proteins. We do not use differential equations, but describe directly the concentrations of the complexes. This is a valid assumption, provided that we always wait a sufficient time, and the system reaches a steady state.<br />
We describe the total amount of proteins with the index 't', while we use the index '*' for proteins that build a complex with their respective inducer. For example: <br />
* [TetR<sub>t</sub>] describes the total amount of TetR that is available. <br />
* [TetR<sub>*</sub>] describes the proteins that are available as a complex with aTc, and<br />
* [TetR] gives the concentration of free TetR proteins.<br />
<br />
[[Image:Eq04.png|208px]]<br />
<br />
===Learning and memory subsystem===<br />
<br />
The learning and memory subsystem is the core of the system that we are trying to model and implement. It is characterized by the feedback between its state variables/proteins cI and p22cII. Its behavior is further complicated by the variation of the production of the aforementioned proteins because of the inputs. The following equations describe the concentrations of the memory proteins as a system of coupled differential equations. The equations consist of two major production parts and a decay part. <br />
* The first production part models the production of either cI or p22cII during the learning phase, and corresponds to the model in Fig. 5.<br />
* The second production part describes the inner toggle switch that was shown in Fig. 6.<br />
<br />
[[Image:Toggle_braced.png|770px]]<br />
<br />
===Reporting subsystem===<br />
<br />
The equations for the reporting subsystem finally describe the production of the florescence proteins depending on the inputs and memory proteins as modeled in Figure 8. Note that both inputs and memory proteins act repressively on the production of the florescence proteins. So e.g. YFP is only produced when there is both no cI and all TetR is bound in a complex together with aTc.<br />
<br />
[[Image:Reporter_braced.png|778px]]<br />
<br />
The systems of equations presented above describe and predict the behavior of our system. We have simulated the behavior of our system at steady states, and the results can be seen in the section [[ETHZ/Simulation|Simulations]]. In order to increase the accuracy of our results, we conducted an extensive literature survey, in order to isolate and find the parameters of our system. Since this is a burden for every team undertaking a complicated project in synthetic biology, we are presenting our full table of parameters in the [[ETHZ/Parameters|Parameters]] page.<br />
<br />
== References ==<br />
<p><br />
[http://www.nature.com/nature/journal/v403/n6767/abs/403339a0.html &#91;1&#93; Gardner TS, Cantor CR and Collins JJ] <i>"Construction of a genetic toggle switch in Escherichia coli"</i>, Nature 403:339–342, 2000<br /></div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/PicturesETHZ/Pictures2007-10-24T23:38:15Z<p>LiNan: </p>
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=Introduction=<br />
<br />
In this page, you can find pictures that we took during our working on the EducatETH <i>E. coli</i> project. <br />
<br />
===Brainstorming===<br />
<p><br />
[[Image:Brainstorming.jpg|left|thumb|300px|Brainstorming on the learning system]] [[Image:Brainstorming2.jpg|center|thumb|300px|Brainstorming on the learning system]]<br />
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<br />
===Working in the lab===<br />
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[[Image:Igem_bench.jpg|left|thumb|300px|Our iGEM bench!]] [[Image:Ecoli1.jpg|center|thumb|300px|Agar plates]]<br />
[[Image:Ecoli2.jpg|left|thumb|300px|more Agar plates]]<br />
</p></div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/InternalETHZ/Internal2007-10-24T23:37:48Z<p>LiNan: </p>
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__NOTOC__<br />
<br />
=Introduction=<br />
<br />
This page documents the process of coming up with a suitable idea for this year's iGEM contest. It is only intended to be a loose gathering of remarks and ideas, instead of a well ordered part of our project documentation. Still, if you are interested in getting a glimpse on our first weeks, you are more than welcome to go on. But beware: afterwards, you might end up with more open questions than answers ...<br />
<br />
Have fun!<br />
<br />
== Brainstorming ==<br />
<br />
During the first two weeks of July, the team tries to come up with a set of ideas for a project. The team is divided into three groups, which will brainstorm individually. Sven and Joerg are available on the 4th and 5th of July in case groups needs to consult them. The objective is that each group comes up with many fancy ideas (it is not yet important that they can be realized at all). To facilitate this, keep in mind the following "brainstorming rules":<br />
<br />
# Defer judgment - the rules of nature don't apply<br />
# Encourage wild ideas<br />
# Build on the ideas of others<br />
# Be visual<br />
# Go for quantity<br />
# Stay focused on topic<br />
<br />
=== Preliminary ideas ===<br />
<br />
* '''PID Controller''': Design a PID controller out of biological elements. The P component can be a simple output to a regulatory protein, and the I component can be the overall protein production at a time period. What can the D component be?<br />
* '''Motion Detector''': Cells are grown on a petri dish. Below the dish, moving images are displayed. A 3-state automaton is proposed. Output A is created when light is present. Output B is created when light is absent. Moving patterns will cause some cells to create both outputs over time. This will result in some “inspector” cells producing output C, by collecting outputs A and B.<br />
* '''Analog-to-Digital Converter''': Compare the level of protein concentration with thresholds, and digitize the output.<br />
* '''Neural Network''': Create a sort of biological neural network with bacteria. We should address the issue of learning, and find a way to incorporate the feedback in the cell decision making process. Directed evolution can be a sort of feedback, but we want to avoid this. (This idea was the basis for the “learning project”)<br />
* '''Paramedic Cells''': Some cells are able to detect signals coming from other cells, and create food for them, or create proteins in order to save them and make them function better.<br />
* '''Cell Batteries''': Cells are able to create and store large quantities of ATP, during a “storing process”. Afterwards, they can detect a signal and give back all the energy they stored, in a short burst, like a capacitor. Other ideas are that the cells can “blow up” and emit large amounts of GFP, based on the ATP that they have accumulated.<br />
* '''Flashing Bacteria''': Cells are grown on a light pattern. The cells that are on the bright parts of the image are oscillating in phase, while the others are remaining dark. This results in the observation of a flashing pattern.<br />
* '''Biocam''': Visible to Fluorescent light converter.<br />
* '''BioCD''': “Print” cells on a film, then read them out and “reconstruct” the original data. Basically, it is an analog to digital converter, followed by a system that can interpret the digitized data. (This idea was the basis for the “Music of life project”, where cells would produce fluorescent proteins based on an analog input. Then, the amount and type of fluorescence would code some music).<br />
* '''Clock''': A follow-the-leader system. We have to groups of cells. The first group creates something that repels the second group. The second group creates a protein that attracts the first group. This way, the first group tries to “catch” the second group, whereas the second group tries to “evade” the first group. This results in them moving around. We can say that the second group is the leader, and the first group exhibits a "follow-the-leader” behavior. Emitting GFP, the groups then act as a sort of analog watch.<br />
* '''Sensors''': Various systems that can sense PH, pressure, temperature, meat quality, moisture, e.t.c. have been proposed.<br />
<br />
=== Preferred projects===<br />
Two ideas of the above are chosen for further examination. The team will be again split up in two groups (different ones than before to make sure that new ideas come up with mixing of people). Each group has to come up with a prototype model of the initial system, remarks on its feasibility and coolness. Our results<br />
will be presented to all team members, so that potential projects may be limited to a final project. The preferred projects are: <br />
<br />
# '''Music of Life''': The basic idea is that instead of having an analog-to-digital converter with four outputs (three fluorescent proteins, and no output), we can have two switches. When switch A is on, RFP is produced. When switch B is on, GFP is produced. When both switches A and B are on, a yellowish output is observed. By recording these outputs, we can later create music, by assigning each fluorescent protein to a chord. For example, RFP would correspond to a G chord. The strength of the fluorescence can signify the strength of the chord. If the cells are placed on a spinning disk, we can have something like a vinyl player. A camera is observing the cells, and music is created on the fly. <br />
# '''Learning''': Based on the idea of the neural network, we want to create a biological system where the cells can learn a specific behavior. In order to simplify the system, we decided that the cells can learn to recognize a specific type of other cells. We divide the process in a learning phase and a recognition phase. First, cells A are put together with cells B. Then, cells A are “learning” to recognize cells B. If afterwards they are put in a petri dish with cells B, they will emit GFP. Otherwise, they will stay dark.<br />
<br />
=== Final project===<br />
<br />
The chosen project is a modified version of Learning which was presented at the last meeting. The system proposed is modified, as in its current state may be implemented with two switches only, something which is really smashing. Keeping the idea of learning, training, and testing phases, we have come up with educatETH <i>E.coli</i>.</div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/EngineeringETHZ/Engineering2007-10-24T23:37:03Z<p>LiNan: </p>
<hr />
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<br />
<center>[[ETHZ | Main Page]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Biology | Biology Pespective]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Engineering | Engineering Perspective]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Meet_the_team | Meet the Team]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Internal | Team Notes]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Pictures | Pictures!]]</center><br><br />
__NOTOC__<br />
<br />
<center>[[ETHZ | Main Page]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Model | System Modeling]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Simulation | Simulations]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Biology | System Implementation]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Biology/Lab| Lab Notes]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Meet_the_team | Meet the Team]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Internal | Team Notes]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Pictures | Pictures!]]</center><br><br />
<br />
=====.:: Introduction ::.=====<br />
<br />
<p><br />
Having decided to work on an engineered biological system which exhibits learning, we elaborated on its design. Discussing with the biologists of the team, we realized that what we knew from the field of logic design as JK flip-flop with a latch may be implemented with biological parts using a modified toggle switch. Initial simulations showed us that it was possible to reach the desired behaviour. Therefore, a complete framework of differential equations describing the system was constructed and parameters were searched in the literature. Simulations performed with our new detailed model are very encouraging.<br />
In this page, the equations that model our system are and explained. The values that were chosen for the system parameters are presented and the results of our simulations are analyzed. References are provided at the end of the page. For an introduction to system modeling in synthetic biology, please read our modeling tutorial [[ETHZ/Modeling_Basics|here]].</p><br><br />
<br />
=====.:: System Model ::.=====<br />
<br />
<p>Following the guidelines presented in our modeling guide, we divided the biological system into subsystems, each of which was modelled with a system of differential equations. According to what presented in the [[ETHZ/Biology|Biology Perspective]], our system is composed the following three subsystems:<br><br />
# A subsystem of constitutively produced proteins (see Fig. 1), <br />
# The learning subsystem(see Fig. 2), and <br />
# The reporting subsystem (see Fig. 3). <br />
The first two subsystems interact, and thus, they should be considered together. The third subsystem has no feedback with the other two, as it is only used for producing the appropriate fluorescent proteins.<br />
<br />
The subsystem of the constitutively produced proteins serves as a regulatory system, and can be modeled with the following three decoupled partial differential equations (see Fig. 1):<br />
<br />
[[Image:constitutive_part.png|center|thumb|'''Fig.1: '''Subsystem 1-Constitutively produced proteins|340px]]<br />
<br />
The second subsystem is the main part of our model. It stores the information concerning the learned information for the chemical of interest, and drives the production of the appropriate reporter during the recognition phase. It is actually a toggle switch that reaches its steady state depending on the chemical that it has been previously exposed to (see Fig. 2):<br />
<br />
[[Image:toggle_part_b.png|center|thumb|'''Fig.2: '''Subsystem 2-Basic learning subsystem (toggle)|900px]]<br />
<br />
The third subsystem reports the state that our system is, during the different phases of learning and recognition. During the learning phase, this subsystem reports which chemical the cells are exposed to, and during the recognition phase, it reports if the cells recognize the chemicals that they are currently exposed to (see Fig. 3):<br />
<br />
[[Image:reporting_part_b.png|center|thumb|'''Fig.3: '''Subsystem 3-Reporting subsystem |900px]] <br />
<br />
Note that the three constitutively produced proteins LacI, TetR and LuxR exist in two different forms; as free proteins and in complexes they build with IPTG, aTc and AHL, respectively. We need to model this complex-forming procedure, with another set of differential equations (Fig. 4):<br />
<br />
[[Image:allosteric_part.png|center|thumb|'''Fig.4: '''Allosteric regulation|240px]]<br />
<br />
In order to have meaningful results from our simulations, we browsed through the literature in order to find appropriate values for our parameters. We reduced our parameter space by joining parameters together, and we gave reasonable estimates, for the values that could not be extracted from available publications. Since this is a hard part that every team has to face, we present the table with the chosen parameters below:<br />
<br />
[[Image:parameters.png|center|thumb|'''Fig.5: '''Parameters of the model|800px]]<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
! Parameter <br />
! Value<br />
! Description<br />
! References<br />
|-<br />
| c<sub>1</sub><sup>max</sup><br />
| 0.01 [mM/h]<br />
| max. transcription rate of<br> constitutive promoter <br>(per gene)<br />
| Estimate<br />
|-<br />
| c<sub>2</sub><sup>max</sup><br />
| 0.01 [mM/h]<br />
| max. transcription rate of<br> luxR-activated promoter <br>(per gene)<br />
| Estimate<br />
|-<br />
| l<sup>hi</sup><br />
| 25<br />
| mid-copy plasmid number<br />
| Estimate<br />
|-<br />
| l<sup>lo</sup><br />
| 5<br />
| low-copy plasmid number<br />
| Estimate<br />
|-<br />
| a<sub>p22cII,LacI</sub><br />
| 0.1 - 0.2<br />
| basic production of<br> p22cII/LacI-inhibited genes<br />
| Discussion<br />
|-<br />
| a<sub>p22cII</sub><br />
| 0.1 - 0.2<br />
| basic production of<br> p22cII-inhibited genes<br />
| Discussion<br />
|-<br />
| a<sub>cI,TetR</sub><br />
| 0.1 - 0.2<br />
| basic production of<br> cI/TetR-inhibited genes<br />
| Discussion<br />
|-<br />
| a<sub>cI</sub><br />
| 0.1 - 0.2<br />
| basic production of<br> cI-inhibited genes<br />
| Discussion<br />
|-<br />
| a<sub>p22cII,TetR</sub><br />
| 0.1 - 0.2<br />
| basic production of<br> p22cII/TetR-inhibited genes<br />
| Discussion<br />
|-<br />
| a<sub>cI,LacI</sub><br />
| 0.1 - 0.2<br />
| basic production of<br> cI/TetR-inhibited genes<br />
| Discussion<br />
|-<br />
| d<sub>LacI</sub><br />
| 2.31e-3 [pro sec]<br />
| degradation of lacI<br />
| [10]<br />
|-<br />
| d<sub>TetR</sub><br />
| 1e-5 [pro sec]/2.31e-3 [pro sec]<br />
| degradation of TetR<br />
| [9], [10]<br />
|-<br />
| d<sub>LuxR</sub><br />
| 1e-3 - 1e-4 [per sec]<br />
| degradation of LuxR<br />
| [6]<br />
|-<br />
| d<sub>cI</sub><br />
| 7e-4 [per sec]<br />
| degradation of cI<br />
| [7]<br />
|-<br />
| d<sub>p22cII</sub><br />
| <br />
| degradation of p22cII<br />
|-<br />
| d<sub>YFP</sub><br />
| 6.3e-3 [per min]<br />
| degradation of YFP<br />
| suppl. mat. to Ref. [8]<br />
|-<br />
| d<sub>GFP</sub><br />
| 6.3e-3 [per min]<br />
| degradation of GFP<br />
| in analogy to YFP<br />
|-<br />
| d<sub>RFP</sub><br />
| 6.3e-3 [per min]<br />
| degradation of RFP<br />
| in analogy to YFP<br />
|-<br />
| d<sub>CFP</sub><br />
| 6.3e-3 [per min]<br />
| degradation of CFP<br />
| in analogy to YFP<br />
|-<br />
| K<sub>LacI</sub><br />
| 1.3e-3 - 2e-3 [mM/h]<br />
| lacI repressor <br>dissociation constant<br />
| [2], [5]<br />
|-<br />
| K<sub>IPTG</sub><br />
| 1.5e-10 [mM/h]<br />
| IPTG-lacI repressor <br>dissociation constant<br />
| [5]<br />
|-<br />
| K<sub>TetR</sub><br />
| 5.6 (+-2) [nM-1]<br />
| tetR repressor <br>dissociation constant<br />
| [1]<br />
|-<br />
| K<sub>aTc</sub><br />
| 1120 (+-400) [nM-1]<br />
| aTc-tetR repressor <br>dissociation constant<br />
| [1], [3]<br />
|-<br />
| K<sub>LuxR</sub><br />
| <br />
* 0.003 [mM/s]<br />
* 55 - 520 [nM]<br />
| luxR activator <br>dissociation constant<br />
| [6]<br />
|-<br />
| K<sub>AHL</sub><br />
| <br />
* 0.009 [mM/s] - 0.1 [mM/s]<br />
* 0.09 - 1 [&#181;M]<br />
| AHL-luxR activator <br>dissociation constant<br />
| [6]<br />
|-<br />
| K<sub>cI</sub><br />
| 2e-3 [mM/h]<br />
| cI repressor <br>dissociation constant<br />
| [5]<br />
|-<br />
| K<sub>p22cII</sub><br />
| <br />
| p22cII repressor <br>dissociation constant<br />
|-<br />
| n<sub>LacI</sub><br />
| 1<br />
| lacI repressor <br>Hill cooperativity<br />
| [5]<br />
|-<br />
| n<sub>IPTG</sub><br />
| 2<br />
| IPTG-lacI repressor <br>Hill cooperativity<br />
| [5]<br />
|-<br />
| n<sub>TetR</sub><br />
| 3<br />
| tetR repressor <br>Hill cooperativity<br />
| [3]<br />
|-<br />
| n<sub>aTc</sub><br />
| 2 (1.5-2.5)<br />
| aTc-tetR repressor <br>Hill cooperativity<br />
| [3]<br />
|-<br />
| n<sub>LuxR</sub><br />
| 2<br />
| luxR activator <br>Hill cooperativity<br />
| [6]<br />
|-<br />
| n<sub>AHL</sub><br />
| 1<br />
| AHL-luxR activator <br>Hill cooperativity<br />
| [3]<br />
|-<br />
| n<sub>cI</sub><br />
| 1.9<br />
| cI repressor <br>Hill cooperativity<br />
| [5]<br />
|-<br />
| n<sub>p22cII</sub><br />
| <br />
| p22cII repressor <br>Hill cooperativity<br />
|-<br />
|}<br />
<br />
<br />
<br />
</p><br><br />
<br />
=====<b>.:: Simulations ::.</b><br>=====<br />
<br />
=====.:: References ::.=====<br />
<p><br />
[1] Weber W., Stelling J., Rimann M., Keller B., Daoud-El Baba M., Weber C.C., Aubel D., and Fussenegger M., <i>"A synthetic time-delay circuit in mammalian cells and mice"</i>, Proceedings of the National Academy of Sciences, vol. 104, no. 8, pp. 2643, 2007.<br><br />
[2] Setty Y., Mayo AE, Surette MG, and Alon U., <i>"Detailed map of a cis-regulatory input function"</i>, Proceeding of the National Academy of Sciences, vol. 100, no. 13, pp. 7702--7707, 2003.<br><br />
[3] Braun D., Basu S., and Weiss R., <i>"Parameter Estimation for Two Synthetic Gene Networks: A Case Study"</i>, IEEE Int'l Conf. Acoustics, Speech, and Signal Processing 2005, vol. 5, 2005.<br><br />
[4] Fung E., Wong W.W., Suen J.K., Bulter T., Lee S., and Liao J.C., <i>"A synthetic gene--metabolic oscillator"</i>, Nature, vol. 435, no. 7038, pp. 118--122, 2005, supplementary material.<br><br />
[5] Iadevaia S., and Mantzaris N.V., <i>"Genetic network driven control of PHBV copolymer composition"</i>, Journal of Biotechnology, vol. 122, no. 1, pp. 99--121, 2006.<br><br />
[6] Goryachev AB, Toh DJ, and Lee T., <i>"Systems analysis of a quorum sensing network: Design constraints imposed by the functional requirements, network topology and kinetic constants"</i>, Biosystems, vol. 83, no. 2-3, pp. 178--187, 2006.<br><br />
[7] Arkin A., Ross J., and McAdams H.H., <i>"Stochastic kinetic analysis of developmental pathway bifurcation in phage λ-Infected Escherichia coli cells"</i>, Genetics, vol. 149, no. 4, pp. 1633--1648, 1998.<br><br />
[8] Colman-Lerner A., Chin T.E., and Brent R., <i>"Yeast Cbk1 and Mob2 Activate Daughter-Specific Genetic Programs to Induce Asymmetric Cell Fates"</i>, Cell, vol. 107, no. 6, pp. 739--750, 2001.<br><br />
[9] Becskei A., and Serrano L., <i>"Engineering stability in gene networks by autoregulation"</i>, Nature, vol. 405, no.6786, pp.590--593, 2000.<br><br />
[10] Tuttle L.M., Salis H., Tomshine J., and Kaznessis Y.N., <i>"Model-Driven Designs of an Oscillating Gene Network"</i>, Biophysical Journal, vol. 89, no. 6, pp. 3873--3883, 2005.<br><br />
<br />
</p><br><br />
[http://www.pnas.org/cgi/content/abstract/104/8/2643 &#91;1&#93; Weber W et al.] <i>"A synthetic time-delay circuit in mammalian cells and mice"</i>, P Natl Acad Sci USA 104(8):2643-2648, 2007<br /><br />
[http://www.pnas.org/cgi/content/full/100/13/7702?ck=nck &#91;2&#93; Setty Y et al.] <i>"Detailed map of a cis-regulatory input function"</i>, P Natl Acad Sci USA 100(13):7702-7707, 2003<br /><br />
[http://ieeexplore.ieee.org/iel5/9711/30654/01416417.pdf &#91;3&#93; Braun D et al.] <i>"Parameter Estimation for Two Synthetic Gene Networks: A Case Study"</i>, ICASSP 5:769-772, 2005<br /><br />
[http://www.nature.com/nature/journal/v435/n7038/suppinfo/nature03508.html &#91;4&#93; Fung E et al.] <i>"A synthetic gene--metabolic oscillator"</i>, Nature 435:118-122, 2005 (supplementary material)<br /><br />
[http://dx.doi.org/10.1016/j.jbiotec.2005.08.030 &#91;5&#93; Iadevaia S and Mantzais NV] <i>"Genetic network driven control of PHBV copolymer composition"</i>, J Biotechnol 122(1):99-121, 2006<br /><br />
[http://dx.doi.org/10.1016/j.biosystems.2005.04.006 &#91;6&#93; Goryachev AB et al.] <i>"Systems analysis of a quorum sensing network: Design constraints imposed by the functional requirements, network topology and kinetic constants"</i>, Biosystems 83(2-3):178-187, 2004<br /><br />
[http://www.genetics.org/cgi/content/abstract/149/4/1633 &#91;7&#93; Arkin A et al.] <i>"Stochastic kinetic analysis of developmental pathway bifurcation in phage λ-Infected Escherichia coli cells"</i>, Genetics 149: 1633-1648, 1998<br /><br />
[http://download.cell.com/supplementarydata/cell/107/6/739/DC1/index.htm &#91;8&#93; Colman-Lerner A et al.] <i>"Yeast Cbk1 and Mob2 Activate Daughter-Specific Genetic Programs to Induce Asymmetric Cell Fates"</i>, Cell 107(6): 739-750, 2001 (supplementary material)<br /><br />
[http://www.nature.com/nature/journal/v405/n6786/abs/405590a0.html &#91;9&#93; Becskei A and Serrano L] <i>"Engineering stability in gene networks by autoregulation"</i>, Nature 405: 590-593, 2000<br /><br />
[http://www.biophysj.org/cgi/content/full/89/6/3873?maxtoshow=&HITS=10&hits=10&RESULTFORMAT=&searchid=1&FIRSTINDEX=0&volume=89&firstpage=3873&resourcetype=HWCIT &#91;10&#93; Tuttle et al.] <i>"Model-Driven Designs of an Oscillating Gene Network"</i>, Biophys J 89(6):3873-3883, 2005<br /><br />
[http://www.pnas.org/cgi/reprint/99/2/679 &#91;11&#93; McMillen LM et al.] <i>"Synchronizing genetic relaxation oscillators by intercell signaling"</i>, P Natl Acad Sci USA 99(2):679-684, 2002<br /><br />
[http://www.nature.com/nature/journal/v434/n7037/full/nature03461.html &#91;12&#93; Basu S et al.] <i>"A synthetic multicellular system for programmed pattern formation"</i>, Nature 434:1130-1134, 2005<br /><br />
<br />
=====.:: To Do ::.=====<br />
<br />
*''Christos'': 1. Add gifs concerning the simulations<br />
*''Christos'': 2. Remove the table with the parameters, once we have satisfying values.<br />
*''Katerina'': 1. "The third subsystem has no feedback with the other two, as it is only used for producing the appropriate fluorescent proteins." Is this really true? gfp and rfp are situated in bigger parts with functionality. I think this needs to be rewritten more clearly.</div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/Wiki_relETHZ/Wiki rel2007-10-24T23:36:50Z<p>LiNan: </p>
<hr />
<div><center>[[Image:ETH_banner.png|900px]]</center><br />
<br />
<center>[[ETHZ/Main_page | Main Page]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Biology | Biology Pespective]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Engineering | Engineering Perspective]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Meet_the_team | Meet the Team]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Internal | Team Notes]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Pictures | Pictures!]]</center><br />
<br />
__NOTOC__<br />
<br />
<center><font size = '+2'><b> .:: ETH Zurich - EducatETH ''E.coli'' ::. </b></font></center><br><br />
<br />
=='''.:: Introduction ::.'''==<br />
<p>It is the third time the [http://www.ethz.ch ETH Zurich] takes part in [https://2007.igem.org/wiki/index.cgi International Genetically Engineered Machine Competition] (iGEM). This year, our combined team of biologists and engineers has undertaken the task of educating <i>E.coli</i> ! More specifically, in our project (Fig. 1) [[Image:Educateth_Ecoli.png|thumb|The ETH Zurich team undertook the task of engineering a learning mechanism for <i>E.coli</i>. '''(Fig. 1)'''|500px]] we are trying to create <i>E.coli</i> which have the ability to distinguish between two chemicals they may be exposed to after they have undergone a training phase.</p><br />
<br />
<p>Stay in this page for an overview of how EducatETH <i>E.coli</i> works, the motivation behind it and its possible future applications. If you want to see the biological design of our system and the parts that it consists of, or if you are interested in building it yourself and want to read the lab protocols, the [[ETHZ/Biology | Biology Perspective]] will be of interest to you. If you want to know more on how EducatETH <i>E.coli</i> has been modeled and simulated, or on its equivalences to systems such as flip-flops and finite state machines, please visit the [[ETHZ/Engineering | Engineering Perspective]]. You may also want to visit [[ETHZ/Meet_the_team | Meet the Team]] for information regarding the team and [[ETHZ/Pictures | Pictures!]] for our photo gallery. Finally, in [[ETHZ/Internal | Team Notes]] you can read the notes exchanged by the team during preparation for the competition.</p><br />
<br />
====='''.:: Team Members ::.'''=====<br />
<br />
<p>As [https://2007.igem.org/wiki/index.cgi iGEM] is a synthetic biology competition, the ETH Zurich team consists of balanced numbers of biology and engineering students. Our team members are: </p><ul> <br />
<li><i>Project advisors</i>: [https://2007.igem.org/User:sven Sven Panke], Joerg Stelling<br />
<li><i>Undergraduate students</i>: [https://2007.igem.org/User:brutsche Martin Brutsche], [https://2007.igem.org/User:kdikaiou Katerina Dikaiou], [https://2007.igem.org/User:Raphael Raphael Guebeli], [https://2007.igem.org/User:hoehnels Sylke Hoehnel], [https://2007.igem.org/User:LiNan Nan Li], [https://2007.igem.org/User:Stefan Stefan Luzi]<br />
<li><i>Graduate students</i>: [http://christos.bergeles.net Christos Bergeles], [http://www.tik.ee.ethz.ch/~sop/people/thohm/ Tim Hohm], [http://fm-eth.ethz.ch/eth/peoplefinder/FMPro?-db=phonebook.fp5&-format=pf%5fdetail%5fde.html&-lay=html&-op=cn&Typ=Staff&Suche%5fText=kemmer&Suche%5fText%5fpre=kemmer&-recid=3772770936&-find=/ Christian Kemmer], [https://2007.igem.org/User:JoeKnight Joseph Knight], [https://2007.igem.org/User:uhrm Markus Uhr], [http://www.ricomoeckel.de Rico Möckel]<br />
</ul><br />
<p>For more information on the team members, follow the links or visit [[ETHZ/Meet_the_team | Meet the Team]].</p><br />
<br />
====='''.:: Motivation ::.'''=====<br />
<p> Learning plays a major role for living organisms, as it enables them to survive by adapting to an ever-changing environment. Engineering a simple biological system which exhibits learning behavior is of great interest, as it can support our understanding of this procedure by comparison with natural systems. On the other hand, learning and memorizing plays an equally big role in engineering; from handwriting recognition on PDAs to plain logical circuits storing their binary state on computers, it can be found on numerous everyday life applications. Constructing a biological analogue of a simple memory as known from logic design can hopefully function as a biological building block from which more complex systems may be constructed. We therefore think that EducatETH <i>E.coli</i> operates on an exciting interface between engineering and biology.</p><br />
<br />
====='''.:: <i>E.coli</i> Intelligence (E.I) - Specifications and analogies ::.'''=====<br />
<br />
<p>How can we make <i>E.coli</i> bacteria able to report us about their environment? Can we teach bacteria to tell if they have seen before a specific chemical? [[Image:System_blended4.png|thumb|350px| '''Fig. 2: '''EducatETH E. Coli System ]]</p><br />
<br />
<p>This problem is essentially broken down to constructing a toggle switch which can maintain the state it acquired during a training phase. In Logic Design, this is done using a JK flip-flop with a latch. With this approach, reporting in the testing phase may be implemented with AND gates using the state of the toggle and the current chemical as inputs.<br><br />
(Want to read more about this? Visit the [[ETHZ/Engineering| Engineering Perspective]]!)</p><br />
<br />
<p>Back to biology, a toggle switch has been successfully implemented in [1]. This toggle switch, however, changes states according to one input only as it has one operator site and therefore loses its previous state whenever the system is exposed to a different chemical. This means that it cannot memorize information. To overcome this, we modified the toggle switch in [1] using two operator sites. In this way, a second chemical acting as a “helper” substance present only in the training phase may be used so that the toggle maintains its state during testing.<br> <br />
(Want to see the biological design of our modified toggle? Visit the [[ETHZ/Biology | Biology Perspective]]!)</p><br />
<br />
====='''.:: A short system description ::.'''=====<br />
<br />
<p>EducatETH <i>E.coli</i> is able to recognize between two chemical substances (aTC and IPTG) it has previously been exposed with help of an external chemical signal (AHL).<br />
In the first part of the training phase (“meeting”), the system is exposed to one of the two chemicals (aTC and IPTG) and AHL is added, causing a steady system behavior. In the second part of the training phase (“memorizing”), all chemicals but AHL are removed, allowing the system to maintain its state. Finally, in the testing phase (“recognizing”), the system is exposed to any of the two chemicals again. Via comparison of the toggle's steady state with the system response the new chemical causes, the system recognizes if it has been exposed to this chemical before or not. As seen on Table , four system states are possible in the testing phase. We chose to use two reporters to control which chemical EducatETH E.coli is exposed to during the training phase (CFP for , YFP for ) and two others to control whether the system sees the same chemical as it did in the training phase (GFP) or not (RFP). Thus, the system response is determined uniquely. </p><br />
<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;" <br />
! Training <br>chemical <br />
! Testing <br>chemical <br />
! Fluorescence <br>during training<br />
! Fluorescence <br> during testing <br />
|- <br />
| aTc<br />
| aTc<br />
| CFP or YFP?<br />
| GFP<br />
|- <br />
| aTc <br />
| IPTG<br />
| CFP or YFP?<br />
| RFP<br />
|- <br />
| IPTG <br />
| aTc <br />
| CFP or YFP?<br />
| RFP<br />
|- <br />
| IPTG<br />
| IPTG <br />
| CFP or YFP?<br />
| GFP<br />
|}<br />
<br />
====='''.:: References ::.'''=====<br />
[1]: Toggle Switch<br />
<br />
====='''.:: Acknowledgements ::.'''=====<br />
<ul><br />
<li><p>[http://europa.eu The European Union]</p><br />
<li><p>[http://www.ethz.ch The ETH Zurich]</p><br />
<li><p>[http://www.geneart.com GENEART]</p><br />
</ul><br><br />
====='''.:: Links ::.'''=====<br />
<ul><br />
<li><p>[https://2006.igem.org/wiki/index.php/ETH_Zurich_2005 The ETH Zurich 2005 project]</p><br />
<li><p>[https://2006.igem.org/wiki/index.php/ETH_Zurich_2006 The ETH Zurich 2006 project]</p></ul><br><br />
=='''.:: To Do ::.'''==<br />
====='''.:: New ones ::.'''=====<br />
<br />
<p><ul><br />
<li> Change banner on top of page.<br />
<li> Decide on headings type. I like larger headings more, I also like the horizontal line because it separates, put it only on Introduction for you to see how it is.<br />
<li> Possibly put table of contents. <br />
<li> Possibly take EducatETH <i>E.coli</i> from Figure.<br />
<li> Change Figure 2.<br />
<li> Possibly change figure caption format from xxx (Fig. x) to Fig.x: xxx. Put second format on Figure 2 for all to check.<br />
<li> Fix table showing possible system states and reporters. Maybe with some color?<br />
<li> Put cross-reference on table.<br />
<li> Align table left<br />
<li> Add which reporters do what in paragraph.<br />
<li> Put (compressed!) team photo instead of Fig.1<br />
<li> Fix paragraph with system description, looks somehow bulky. Maybe italics or bold on important things?<br />
<li> Fix reference on toggle switch.<br />
<li> Possibly remove Introduction heading altogether, and just start with the text.<br />
</ul></p><br />
<br />
====='''.:: Old ones ::.'''=====<br />
<p><ul><br />
<li>Need picture: Bacteria red, Bacteria Green, two pictures showing different exposure to chemicals (''Sylke: see my presentation Sven will hold for me tonight (20.09) -> the FACS guy wants probes of our XFP expressing E.coli - the one who brings it to him can just put it under the microscope and take a few pictures for the wiki'')<br />
<li>Need picture: Einstein ecoli (''Sylke: does Stefan have the layer file?'')<br />
<li>Stupidity: All E. Colis are equal, but our E. Colis are more equal than the others :D<br />
<li>''Katerina'': 2. If we have a separate "Meet the team" page (which is good that we have), we have to make sure that all data about each one of us (short bio+photo) appear also on each one's user page-the same version would be the best, in my opinion.<br />
<li>''Katerina'': 3. Figure 2, bottom right part needs to be a bit larger/more clear, in my opinion, as it's important. (Christos: If you click, then it becomes larger. Will have the same at the bio part as well... Should I make it bigger anyway? I will change it, it is wrong anyway :D)<br />
</ul></p></div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/Biology/LabbookETHZ/Biology/Labbook2007-10-24T23:36:28Z<p>LiNan: </p>
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__NOTOC__<br />
<br />
== Lab book ==<br />
<br />
==== Week 1 ====<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
! Date <br />
! TODO's <br />
! Completed <br />
! People<br />
|-<br />
| Mon, 06. Aug. 2007 <br />
|<br />
| <br />
* Preparing the Solutions <br />
| Sylke<br>Raphael<br>Stefan<br>Markus<br>Martin<br>Christos<br>Joe<br />
|-<br />
| Tue, 07. Aug. 2007 <br />
| <br />
|<br />
* Prepare competent cells for all parts<br />
* Transformation of all the parts <br />
| Sylke<br>Raphael<br>Stefan<br>Markus<br>Martin<br>Christos<br>Joe<br />
|-<br />
| Wed, 08. Aug. 2007 <br />
| <br />
| <br />
* Preparing the grown cultures (12) for the MINIPREP <br> (o/n cultures) <br />
| Raphael<br>Stefan<br />
|-<br />
| Thu, 09. Aug. 2007 <br />
| <br />
| <br />
* MINIPREP of the grown (10) o/n cultures<br />
* Gelelectrophoresis of the grown cultures (step: 0.8% Agarose) <br />
| Raphael<br>Stefan<br>Martin<br>Christos<br>Joe<br />
|-<br />
| Fri, 10. Aug. 2007 <br />
| <br />
| <br />
* 7 working parts/plasmids (step after "DIGESTS"): <br> (B0034, R0062, R0053, E0434, B0015, R0010, E0422)<br />
* 4 parts/plasmids minipreped: <br> (R0040, R0051, Q04121, C0053)<br />
|<br />
Christos <br> Markus <br> Stefan<br />
|-<br />
| Sat, 11. Aug. 2007 <br />
| <br />
| no labwork <br />
|<br />
|-<br />
| Sun, 12. Aug. 2007 <br />
|<br />
| labwork cancelled<br />
|<br />
|}<br />
<br />
==== Week 2 ====<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
! Date <br />
! TODO's <br />
! Completed <br />
! People<br />
|-<br />
| Mon, 13. Aug. 2007 <br> start at 3 pm <br />
|<br />
* Prepare competent cells<br />
* Transformations of J23100, J37033, Q04400, Q04510<br />
* Control Restrictions (step after "MINIPREP") <br>R0040, R0051, Q04121, C0053<br />
| <br />
* o/n culture (E.Coli Top10)<br />
* Control Restrictions (didn't work)<br />
| Martin<br> Markus <br> Christos <br> Tim <br><br />
|-<br />
| Tue, 14. Aug. 2007 <br />
| Morning Shift: <br><br />
* Start Preparing competent cells (for J23100, J37033, Q04400, Q04510) <br> <br />
Evening Shift: <br><br />
* Transformations of J23100, J37033, Q04400, Q04510<br />
| Morning Shift: <br><br />
* Prepared competent cells (stored in the -80°C freezer in the basement) <br><br />
Evening Shift: <br><br />
* Transformation of J23100, J37033, Q04400, Q04510 and R0040, R0051, Q04121, C0053 (in the 37°C incubator until Wednesday) <br />
* Prepared new Liquid LB, LB Agar (both in the autoclave), Agarose Gel with concentrations of 0.8% and 2.4% <br />
|Morning Shift (9am-1pm?): <br> Markus <br> Tim <br> Evening Shift (5pm-...):<br> Martin <br> Christos<br />
|-<br />
| Wed, 15. Aug. 2007 <br />
| <br />
* Ligation (step: "LINK ASSEMBLY"): <br> R0053 + E0422 <br> R0010 + E0422 <br> R0010 + E0434 <br> S/P: R0053, R0010 <br> X/P: E0422, E0434 <br />
|<br />
* Ligation didn't work due to bad quality of enzymes (probably) <br />
| From 12:<br> Martin<br>Markus<br><br><br />
|-<br />
| Thu, 16. Aug. 2007 <br />
| <br />
* Miniprep (J23100, J37033, Q04400, Q04510, R0040, R0051, Q04121, C0053)<br />
* Transformation of #13 and #14<br />
| <br />
* Miniprep of #4 (J23100), #5 (J37033), #8 (Q04400), #11 (R0040), #12 (R0051), #15 (Q04510) <br> One batch is miniprepped (after step 19 in the miniprep procedure) and a second batch is frozen as a backup (which is to be miniprepped from step 3 on)<br />
* Transformation of #13 (Q04121) and #14 (C0053) <br> Numbers #13 and #14 are now growing in the 37°C incubator (step 13 in the transformation procedure)<br />
|<br />
Markus<br>Christos<br>(Martin)<br />
|-<br />
| Fri, 17. Aug. 2007 <br />
|<br />
* o/n of #13 and #14<br />
* Check whether miniprep of parts #4 #5 #8 #11 #12 (#13 #14) #15 was successful<br />
| <br />
* #13 and #14 didn't grow<br />
* # 4, 8 and 11 had the plasmid, they were streaked out new on plates, that we have them now on plates<br />
* New white pipette tips prepared (autoclave)<br />
* New bottles of Liquid LB and LB Agar prepared (autoclave)<br />
| Martin<br />
|-<br />
| Sat, 18. Aug. 2007 <br />
| <br />
| <br />
|<br />
|-<br />
| Sun, 19. Aug. 2007 <br />
|<br />
|<br />
|<br />
|}<br />
<br />
==== Week 3 ====<br />
<br />
Little rearrangements of the parts. Planning of the sequences to order them.<br />
<br />
<br />
==== Week 4 ====<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
! Date <br />
! TODO's <br />
! Completed <br />
! People<br />
|-<br />
| Mon, 27. Aug. 2007 <br />
|<br />
| <br />
| <br />
|-<br />
| Tue, 28. Aug. 2007 <br />
| <br />
|<br />
| <br />
|-<br />
| Wed, 29. Aug. 2007 <br />
| <br />
| <br />
| <br />
|-<br />
| Thu, 30. Aug. 2007 <br />
| <br />
| <br />
| <br />
|-<br />
| Fri, 31. Aug. 2007 <br />
| <br />
| <br />
|<br />
|-<br />
| Sat, 01. Sept. 2007 <br />
| <br />
* Transform pbr322, pcyc177 and pck01 <br />
| <br />
* Transform pbr322, pcyc177 and pck01 and plated them <br />
| Stefan<br />
|-<br />
| Sun, 02. Sept. 2007 <br />
|<br />
* Prepare o/n of pbr322, pcyc177, pck01<br />
| <br />
* o/n of pcyc177, pck01<br />
* the plates of pcyc177 and pck01 are in the fridge<br />
* transformed pbr322 because the culture didn't grow on the plate<br />
| Stefan<br />
|}<br />
<br />
==== Week 5 ====<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
! Date <br />
! TODO's <br />
! Completed <br />
! People<br />
|-<br />
| Mon, 03. Sept. 2007 <br />
|<br />
* Prepare new competent cells<br />
* Miniprep pcyc177 and pcK01<br />
* prepare new o/n culture of pbr322<br />
* Run agarose gel of Minipreped plasmids<br />
| <br />
* New competent cells prepared, they are now in the -80° Frezzer in the basement, column #17, dark orange box (we have now 30-35 EDTs of competent cells...)<br />
* Minipreped pcyc177 and pck01 (in the -18° freezer, where the antibiotics are)<br />
* pbr322 didn't grow again, so no o/n could be prepared, but we get a culture from Andy on tuesday<br />
* new o/n of pcyc177 and pck01 prepared (3 Falcons each), because we need to have more plasmids<br />
* 2 boxes of blue pipette tips are in the autoclave<br />
* Stefan ran the agarose gel (?) <br />
| Martin<br>Stefan<br />
|-<br />
| Tue, 04. Sept. 2007 <br />
|<br />
* Miniprep pcyc177 and pck01<br />
* cut the prepped plasmids to test if we've got the right ones<br />
* run agarose gel to test the cut and uncut ones <br />
* prepare new o/n of pbr322 (from Andy) <br />
|<br />
* Miniprep of pcyc177 and pck01 (but not yet tested)<br />
* Prepared 3 o/ns of pbr322 (finally ;-) and each 1 o/n of pcyc177 and pck01, just in case there are problems with the miniprep <br />
| Martin<br>Christian<br />
|-<br />
| Wed, 05. Sept. 2007 <br />
| <br />
* Miniprep of pbr322<br />
* Test-Digest of pcyc177 and pck01 and agarose gel...<br />
* Streak out all three plasmids on new plates, so we have them in reserve <br />
|<br />
* New Plate of pbr322.<br />
* Minipreps and Agarose Gels will be done tomorrow <br />
| Martin<br />
|-<br />
| Thu, 06. Sept. 2007 <br />
| <br />
* Miniprep of pbr322, pacyc177, pck01<br />
* Test with agarose gel <br />
| <br />
* Gel of the older plasmids -> plasmid present<br />
| Christian<br />
|-<br />
| Fri, 07. Sept. 2007 <br />
| <br />
* Miniprep of pbr322, pacyc177, pck01 <br />
| <br />
* Plasmids miniprepped<br />
| Martin<br />
|-<br />
| Sat, 08. Sept. 2007 <br />
| <br />
| <br />
|<br />
|-<br />
| Sun, 09. Sept. 2007 <br />
|<br />
| <br />
|<br />
|}<br />
<br />
==== Week 6 ====<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
! Date <br />
! TODO's <br />
! Completed <br />
! People<br />
|-<br />
| Mon, 10. Sept. 2007 <br />
| <br />
* Miniprep pBR322<br />
* annealing of different MCSs<br />
* Digest of pCK01 with BamHI+AseI<br />
* digest of pACYC177 with BamHI+PstI<br />
* digest of pBR322 with EcoRI+PstI <br />
all digests o/n<br />
| <br />
| <br />
Christian<br />
| <br />
|-<br />
| Tue, 11. Sept. 2007 <br />
|<br />
* Gelextraction of backbones pBR322, pCK01, pACYC digest did NOT work<br />
* 1x ligation of MCS inside backbones o/d, Trafo<br />
* 1x ligation of MCS inside backbones o/n<br />
| <br />
* plate all 3 plasmids for new minipreps<br />
|<br />
Christian<br />
|-<br />
| Wed, 12. Sept. 2007 <br />
|<br />
* Trafo of o/n ligations<br />
* o/n cultures of putative clones <br />
|<br />
|<br />
Christian<br />
|-<br />
| Thu, 13. Sept. 2007 <br />
| <br />
* Minipreps of putative clones pCK01-MCS and pBR322-MCS<br />
* control digests of putative clones<br />
* new o/n cultures of the putative clones of o/n ligations<br />
| <br />
| <br />
Christian<br />
|-<br />
| Fri, 14. Sept. 2007 <br />
| <br />
*separation of control digests of putative clones <br />
|<br />
'''*pBR322-MCS (Tet-selection) clone2 positive''' <br />
|<br />
Christian<br />
|-<br />
| Sat, 15. Sept. 2007 <br />
| <br />
| <br />
|<br />
|-<br />
| Sun, 16. Sept. 2007 <br />
|<br />
| <br />
|<br />
|}<br />
<br />
==== Week 7 ====<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
! Date <br />
! TODO's <br />
! Completed <br />
! People<br />
|-<br />
| Mon, 17. Sept. 2007 <br />
|<br />
* new digest of pACYC177 with BamHI+PstI o/n<br />
* digest of pACYC177, pBR322 AP<br />
* ligation of 177 and 322AP<br />
| <br />
* digest of pBR322 AP (the concentration of DNA was too low for pacyc177...)<br />
* ligation of pBR322 o/n<br />
* 100 ml o/n culture to MAXIprep pacyc177<br />
* Transformation of pBR322 AP to have it on plates (because andy only miniprepped them)<br />
| <br />
Christian <br> Martin, Raphael<br />
|-<br />
| Tue, 18. Sept. 2007 <br />
|<br />
* different control digests of pBR322-MCS (Tet) (see last week)<br />
* separation of pACYC177 digest<br />
* Test Digests of pck01 with XbaI, SpeI, PstI, Xba/Pst, Xba/Spe (because all of them should be in the plasmid due to the sequence, and if they are it would be crap!!!)<br />
* Transformation of the ligated pbr322 AP (MCS)<br />
* Prep pacyc177<br />
* Digest prepped pacyc177<br />
|<br />
> no DNA on pACYC177 digest-gel, only degradation smear<br> <br><br />
<br />
* Plates of pbr322 AP grew<br />
* No Digest of pck01 worked due to too low DNA concentration... (che cazzo di low copy plasmids !!!!)<br />
* Miniprepped only 20 ml of the pacyc o/n culture with Quiagen Kit, the results were great! We have loads of DNA! (thank god! )<br />
* Digest of pacyc177 with BamHI (45 µl), then precipitated, in the gel was still very much DNA, but there were still 3 bands, so we guess, that it hasn't cut, maybe because the BamHI in the center is very old, perhaps we should Digest it in Höngg again.<br />
* Digest of pacyc177 with PstI o/n (pray that it will work!)<br />
* New o/n cultures of pck01 (to prep it like pacyc177), pbr322 AP (to prep it too, to have something on stock again, if the ligation didn't work), top10 (to make new competent cells)<br />
* test digest of pck01 with notI, but due to the low DNA concentration I don't think it will work. I took glooves, if it now work, then we have caught some DNases in the earlier test digests<br />
| <br />
<br />
Christian <br><br />
Martin<br>Raphael<br />
|-<br />
| Wed, 19. Sept. 2007 <br />
| <br />
* o/n culture of pbr322 AP (MCS), then test digest and see if it is ligated<br />
* Prep of pck01 and test digests (xba, pst, spe, pvuI, notI)<br />
* check the digests of pacyc177 (pst) and pck01 (notI)<br />
* design new linkers for pck01, design primers for PCR for the extraction of SpeI from pck01 <br />
|<br />
|<br />
|-<br />
| Thu, 20. Sept. 2007 <br />
| <br />
| <br />
| <br />
|-<br />
| Fri, 21. Sept. 2007 <br />
| <br />
| <br />
|<br />
|-<br />
| Sat, 22. Sept. 2007 <br />
| <br />
| <br />
|<br />
|-<br />
| Sun, 23. Sept. 2007 <br />
|<br />
| <br />
|<br />
|}<br />
<br />
==== Week 8 ====<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
! Date <br />
! TODO's <br />
! Completed <br />
! People<br />
|-<br />
| Mon, 24. Sept. 2007 <br />
|<br />
| <br />
| <br />
|-<br />
| Tue, 25. Sept. 2007 <br />
|<br />
|<br />
| <br />
|-<br />
| Wed, 26. Sept. 2007 <br />
| <br />
|<br />
|<br />
|-<br />
| Thu, 27. Sept. 2007 <br />
| <br />
| <br />
| <br />
|-<br />
| Fri, 28. Sept. 2007 <br />
| <br />
| <br />
|<br />
|-<br />
| Sat, 29. Sept. 2007 <br />
| <br />
| <br />
|<br />
|-<br />
| Sun, 30. Sept. 2007 <br />
|<br />
| <br />
|<br />
|}<br />
<br />
====Week 9 ====<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
! Date <br />
! TODO's <br />
! Completed <br />
! People<br />
|-<br />
| Mon, 1. Oct. 2007 <br />
|<br />
| <br />
| <br />
|-<br />
| Tue, 2. Oct. 2007 <br />
|<br />
|<br />
| <br />
|-<br />
| Wed, 3. Oct. 2007 <br />
| <br />
|<br />
|<br />
|-<br />
| Thu, 4. Oct. 2007 <br />
| <br />
| <br />
| <br />
|-<br />
| Fri, 5. Oct. 2007 <br />
| <br />
| <br />
|<br />
|-<br />
| Sat, 6. Oct. 2007 <br />
| <br />
| <br />
|<br />
|-<br />
| Sun, 7. Oct. 2007 <br />
|<br />
| <br />
|<br />
|}<br />
<br />
==== Week 10 ====<br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
! Date <br />
! TODO's <br />
! Completed <br />
! People<br />
|-<br />
| Mon, 8. Oct. 2007 <br />
|<br />
| <br />
| <br />
|-<br />
| Tue, 9. Oct. 2007 <br />
|<br />
|<br />
| <br />
|-<br />
| Wed, 10. Oct. 2007 <br />
| <br />
|<br />
|<br />
|-<br />
| Thu, 11. Oct. 2007 <br />
| <br />
| <br />
| <br />
|-<br />
| Fri, 12. Oct. 2007 <br />
| <br />
| <br />
|<br />
|-<br />
| Sat, 13. Oct. 2007 <br />
| <br />
| <br />
|<br />
|-<br />
| Sun, 14. Oct. 2007 <br />
|<br />
| <br />
|<br />
|}</div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZETHZ2007-10-24T23:35:51Z<p>LiNan: </p>
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<p align="center"><font size="6"><b>ETH Zurich - educatETH <i>E.coli</i> System</b></font></p><br />
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<br />
=Introduction=<br />
<blockquote>"All <i>E.coli</i> 's are equal, but some <i>E.coli</i> 's are more equal than others..." ''(freely adapted from "Animal Farm" by George Orwell)''</blockquote> <br />
<br />
... this is what George Orwell would have written, were he a synthetic biologist. In the <i>E.coli</i> colonies on petri dishes, all bacteria are equal; except for some special ones. Our project is about modeling and designing these special <i>E.coli</i> that are "more equal" than the rest: they have the ability to learn and recognize their environment, and their story will be presented through this wiki ...<br />
<br />
=Motivation=<br />
<br />
Our combined team of biologists and engineers is working on the ''E.coli'' 's ability, first, to recognize two different inputs (here we use two different chemicals), second, to remember which input was presented to them, and third, when confronted with a new input, to recognize whether it is the one that it was trained with or not - in other words, '''we educated the <i>E. coli</i>'''.<br />
<br />
It is obvious thus, that we are coping with the problem of implementing memory capabilities in bacterial colonies. Our system is a memorizing system, and it also has the ability to understand its environment through a recognition phase. If we assume that the training chemicals are harmful for humans, we can use the developed system to understand whether a particular environment is dangerous for humans or not. In this sense, our system can have applications in the health/safety sector, as is described below:<br />
<br />
====Intelligent Biosensors and Self-Adaptation====<br />
<br />
We constructed a system capable of sensing different chemicals and producing different fluorescent proteins. Since the cells can be trained to produce one of several specific fluorescent protein types when a certain chemical is present, one can also view those cells as intelligent biosensors, able to change their properties in a training phase.<br />
It is also possible that the environment (and its chemicals) itself is the training phase, and hence, that the biosensors are adapting themselves to the existing environment. Eventually, the intelligent biosensors are not limited to detect chemicals. Temperature, pH, light, pressure etc. could be detected with an appropriate system as well. <br />
<br />
The main applications of our system however, lie in fully exploiting its memorizing potential, as ca be understood from the following:<br />
<br />
====Multipurpose Cell Lines====<br />
<br />
Our system can be trained to behave in a specific way, by setting its inducible toggle switch to one of its two states. This specific states can trigger specific and different events such as enzyme synthesis, transcriptional regulation, virion production, or even cell death. In this case, one can view the bacterial cell line containing this system as a multipurpose cell line. One can add a certain chemical to a cell line, and train it to the desired behavior, instead of constructing two independent cell lines. <br />
<br />
This means, one applies an “input engineering” instead of a “DNA engineering” approach. If one extends this idea to several inducible toggle switches being harbored in the same cell line, the number of possible phenotypes increases to 2<sup>n</sup>, where n equals the number of toggle switches. For example, if one would have 5 toggle switches inside a cell line, 32 different behavior patterns would be possible. <br />
<br />
For the purpose of creating a toggle that is activated in a specific phase, as is required for stable biological automatons, we introduced the concept of [https://2007.igem.org/ETHZ/Biology/parts#double_promoters double promoters] to the [http://partsregistry.org/Main_Page Registry of Standard Biological Parts], which can be helpful for future projects. As a concept, double promoters are expandable to handle multiple promoter sites, in the case of a greater number of toggles.<br />
<br />
=Link to Epigenetics=<br />
Epigenetics refers to features like chromatin or DNA modifications that do not involve changes in the underlying DNA sequence and are stable over many cell divisions [1],[2]. If one has a closer look at our proposed system, one can also view it as a model-system for epigenetics: Although the DNA sequence itself stays the same, two different subpopulations of cells with different phenotypes can develop from it. Put simply, depending in which state (subpopulation) the toggle switch is, the cells will produce different fluorescent proteins upon addition of inducer molecules (aTc or IPTG). For example, if aTc is added one subpopulation will be red while the other will be yellow although both carry exactly the same DNA information. Therefore, the epigenetic feature here is the binding of specific repressor proteins whose production is dependent on the toggle switch state.<br />
<br />
==== References ====<br />
[http://www.nature.com/nature/journal/v447/n7143/abs/nature05913.html;jsessionid=62903C604764B175945C03DB8639ECBD &#91;1&#93; Bird A] <i>"Perceptions of epigenetics"</i>, Nature 447:396-398, 2007 <br /><br />
[http://linkinghub.elsevier.com/retrieve/pii/S096098220701007X &#91;2&#93; Ptashne M] <i>"On the use of the word ‘epigenetic’"</i>, Current Biology 17(7):R233-R236, 2007<br /><br />
<br />
=Team Members=<br />
[[Image:ETHZ_Group_photo_5.png|right|thumb|ETHZ iGEM2007 Team|300px]]<br />
<br />
The ETH Zurich team consists of good mixture between biologists and engineering students. <br />
We are:<br />
<ul> <br />
<li><i>Undergraduate students</i>: <br><br />
[https://2007.igem.org/User:brutsche Martin Brutsche], [https://2007.igem.org/User:kdikaiou Katerina Dikaiou], <br>[https://2007.igem.org/User:Raphael Raphael Guebeli], [https://2007.igem.org/User:hoehnels Sylke Hoehnel], <br><br />
[https://2007.igem.org/Nan_Li Nan Li], <br />
[https://2007.igem.org/User:Stefan Stefan Luzi]<br />
<li><i>Graduate students</i>: <br><br />
[http://christos.bergeles.net Christos Bergeles], <br />
[http://www.tik.ee.ethz.ch/~sop/people/thohm/ Tim Hohm], <br><br />
[http://www.fussenegger.ethz.ch/people/kemmerc Christian Kemmer], <br />
[https://2007.igem.org/User:JoeKnight Joseph Knight], <br><br />
[http://csb.inf.ethz.ch/people/uhr.html Markus Uhr], <br />
[http://www.ricomoeckel.de Rico Möckel]<br />
<li><i>Project advisors</i>: <br><br />
[http://www.ipe.ethz.ch/laboratories/bpl/people/panke Sven Panke], <br><br />
[http://csb.inf.ethz.ch/people/stelling.html Joerg Stelling]<br />
</ul><br />
<br />
For more information about us, visit our [[ETHZ/Meet_the_team | Meet the Team]] page.<br />
<br />
=Acknowledgments=<br />
The idea for the project as well as its implementation was done by the ETH iGEM 2007 team. We would like to thank the people in [http://www.ipe.ethz.ch/laboratories/bpl/index Sven Panke's Lab], especially Andreas Meyer who was always there for us when we had a problem. Additionally, we would like to thank [http://www.facs.ethz.ch Alfredo Franco-Obregóns lab] and Oralea Büchi for the help with the flow cytometry.<br />
<br />
We would also like to acknowledge the financial support by [http://europa.eu EU], the [http://www.ethz.ch ETH Zurich], and [http://www.geneart.com GeneArt]:<br />
<center><br />
{| border="0"<br />
|-<br />
| [http://europa.eu http://www.tik.ee.ethz.ch/~thohm/EU.gif]<br />
| width="40" |<br />
| [http://www.ethz.ch http://www.tik.ee.ethz.ch/~thohm/ethlogo.jpg]<br />
| width="40" |<br />
| [http://www.geneart.com http://www.tik.ee.ethz.ch/~thohm/geneart.gif]<br />
|}<br />
</center><br />
<br />
=Site Map=<br />
<br />
In this wiki, we will present you a detailed description of the proposed system: starting with the [[ETHZ/Model | modeling of the system]], we describe both, [[ETHZ/Simulation | simulations and theoretical considerations]] of the system, as well as the actual [[ETHZ/Biology | implementation using bio-bricks]] accompanied by our [[ETHZ/Biology/Lab | lab notes]]. Additionally, you find some further [[ETHZ/Meet_the_team | information on the team]], some more details about [[ETHZ/Internal | ideas we developed]] before we came up with the system we finally implemented, and some [[ETHZ/Pictures | pictures]] documenting our work.<br />
<br />
The site map of our wiki is the following:<br />
<br />
{| class="wikitable" width="100%" border="1" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;"<br />
!width="25%"| Modeling Pages <br />
!width="25%"| Biology Pages<br />
!width="25%"| ETHZ Team Pages<br />
!width="25%"| Links<br />
|-<br />
| [[ETHZ/Model | Modeling of the learning system]]<br />
| [[ETHZ/Biology | Biological implementation]]<br />
| [[ETHZ/Meet_the_team | Team page]]<br />
| [https://2006.igem.org/wiki/index.php/ETH_Zurich_2005 The ETH Zurich 2005 project]<br />
|-<br />
| [[ETHZ/FlipFlop | Representation using flip-flops]]<br />
| [[ETHZ/Biology/parts | Biobricks/parts]]<br />
| [[ETHZ/Pictures | Pictures]]<br />
| [https://2006.igem.org/wiki/index.php/ETH_Zurich_2006 The ETH Zurich 2006 project]<br />
|-<br />
| [[ETHZ/FSM | Representation using finite state machines]]<br />
| [[ETHZ/Biology/Lab | Lab notes]]<br />
| [[ETHZ/Internal | Brainstorming sessions]]<br />
| <br />
|-<br />
| [[ETHZ/Simulation | Model simulations and theoretical considerations]]<br />
| <br />
| <br />
| <br />
|-<br />
| [[ETHZ/Parameters | Parameters used in our simulations]]<br />
| <br />
| <br />
| <br />
|-<br />
|}<br />
<br />
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<p align="center"><font size="6"><b>ETH Zurich - educatETH <i>E.coli</i> System</b></font></p><br />
</body><br />
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<br />
=Introduction=<br />
<blockquote>"All <i>E.coli</i> 's are equal, but some <i>E.coli</i> 's are more equal than others..." ''(freely adapted from the Animal Farm by George Orwell)''</blockquote> <br />
<br />
... this is what George Orwell would have written, if he was a synthetic biologist. In the <i>E.coli</i> colonies on petri dishes, all bacteria are equal, except for some special ones. Our project is about modeling and designing these special <i>E.coli</i> that are "more equal" than the rest: they have the ability to recognize their environment and their story will be presented through this wiki ...<br />
<br />
=Motivation=<br />
<br />
WORKING ON IT! <br />
<br />
MISSING = WHAT IS EXACTLY THE SPECIAL THINGS THAT WE DID? DOUBLE PROMOTERS? BIOLOGICAL AUTOMATON WITH MEMORY? COOL WAYS TO EXPLAIN A BIOLOGICAL SYSTEM AS AN ENGINEERING SYSTEM? ROBUST SYSTEM (sensitivity looks pretty good to me...) WE CAN ADD A SECTION LIKE THAT HERE<br />
<br />
Our combined team of biologists and engineers was working on the ''E.coli'' 's ability, first, to recognize two different inputs (here we used two different chemicals), second, to remember which input was presented to them, and third, when confronted with a new input, to recognize whether it is the one that it was trained with or not.<br />
<br />
Thereby, the educatETH ''E.coli'' 's provide a first step towards the development of biological equivalents to complex engineering building blocks like, e.g. , [[ETHZ/FlipFlop | flip-flops]] and other logical circuits. We also introduced the concept of [https://2007.igem.org/ETHZ/Biology/parts#double_promoters double promoters] to the [http://partsregistry.org/Main_Page Registry of Standard Biological Parts], which can be helpful for future projects. The concept of learning and recognition plays a major role in different high-level applications like, e.g., recognition of handwriting on a [http://en.wikipedia.org/wiki/Personal_digital_assistant PDA] as well as [[ETHZ/Applications | biological applications]]. On the other hand, learning plays a major role for living organisms, as well: it enables them to survive by adapting to an ever-changing environment. Therefore, engineering a simple biological system which exhibits learning behavior is of great interest as it can support our understanding of this procedure by comparison with natural systems. <br />
<br />
In the following, we would like to present you a detailed description of the proposed system: starting with the [[ETHZ/Model | modeling of the system]], we describe both, [[ETHZ/Simulation | simulations and theoretical considerations]] of the system, as well as the actual [[ETHZ/Biology | implementation using bio-bricks]] accompanied by our [[ETHZ/Biology/Lab | lab notes]]. Additionally, you find some further [[ETHZ/Meet_the_team | information on the team]], some more details about [[ETHZ/Internal | ideas we developed]] before we came up with the system we finally implemented, and some [[ETHZ/Pictures | pictures]] documenting our work.<br />
<br />
=Team Members=<br />
[[Image:ETHZ_Group_photo_5.png|center|thumb|ETHZ iGEM2007 Team|500px]]<br />
<br />
The ETH Zurich team consists of good mixture between biologists and engineering students, we are:<br />
<ul> <br />
<li><i>Undergraduate students</i>: <br />
[https://2007.igem.org/User:brutsche Martin Brutsche], [https://2007.igem.org/User:kdikaiou Katerina Dikaiou], [https://2007.igem.org/User:Raphael Raphael Guebeli], [https://2007.igem.org/User:hoehnels Sylke Hoehnel], <br />
[https://2007.igem.org/Nan_Li Nan Li], <br />
[https://2007.igem.org/User:Stefan Stefan Luzi]<br />
<li><i>Graduate students</i>: <br />
[http://christos.bergeles.net Christos Bergeles], <br />
[http://www.tik.ee.ethz.ch/~sop/people/thohm/ Tim Hohm], [http://www.fussenegger.ethz.ch/people/kemmerc Christian Kemmer], <br />
[https://2007.igem.org/User:JoeKnight Joseph Knight], <br />
[http://csb.inf.ethz.ch/people/uhr.html Markus Uhr], <br />
[http://www.ricomoeckel.de Rico Möckel]<br />
<li><i>Project advisors</i>: <br />
[http://www.ipe.ethz.ch/laboratories/bpl/people/panke Sven Panke], <br />
[http://csb.inf.ethz.ch/people/stelling.html Joerg Stelling]<br />
</ul><br />
For more information about us, visit our [[ETHZ/Meet_the_team | Meet the Team]] page.<br />
<br />
=Acknowledgments=<br />
The idea for the project as well as its implementation was done by the ETH iGEM 2007 team. Still, we would like to thank the people in [http://www.ipe.ethz.ch/laboratories/bpl/index Sven Panke's Lab], especially Andreas Meyer who was always there for us when we had a problem. Additionally, we would like to thank [http://www.facs.ethz.ch Alfredo Franco-Obregóns lab] and Oralea Büchi for the help with the flow cytometry.<br />
<br />
We would also like to acknowledge the financial support by [http://europa.eu EU], the [http://www.ethz.ch ETH Zurich], and [http://www.geneart.com GeneArt]:<br />
<center><br />
{| border="0"<br />
|-<br />
| [http://europa.eu http://www.tik.ee.ethz.ch/~thohm/EU.gif]<br />
| width="40" |<br />
| [http://www.ethz.ch http://www.tik.ee.ethz.ch/~thohm/ethlogo.jpg]<br />
| width="40" |<br />
| [http://www.geneart.com http://www.tik.ee.ethz.ch/~thohm/geneart.gif]<br />
|}<br />
</center><br />
<br />
=Site Map=<br />
* [[ETHZ/Model | Modeling of the learning system]]<br />
* [[ETHZ/FlipFlop | Implementation using flip-flops]]<br />
* [[ETHZ/FSM | Representation with finite state machines]]<br />
* [[ETHZ/Modeling_Basics | Modeling tutorial]]<br />
* [[ETHZ/Simulation | Model simulations and theoretical considerations]]<br />
* [[ETHZ/Biology | Biological implementation]]<br />
* [[ETHZ/Biology/parts | Biobricks/parts]]<br />
* [[ETHZ/Applications#Applications| Potential applications]] and a link to [[ETHZ/Applications | epigenetics]]<br />
* [[ETHZ/Biology/Lab | Lab notes]]<br />
<br />
* [[ETHZ/Meet_the_team | Team page]]<br />
* [[ETHZ/Pictures | Pictures]]<br />
* [[ETHZ/Internal | Other ideas we developed]]<br />
* others I forgot?<br />
<br />
* [https://2006.igem.org/wiki/index.php/ETH_Zurich_2005 The ETH Zurich 2005 project]<br />
* [https://2006.igem.org/wiki/index.php/ETH_Zurich_2006 The ETH Zurich 2006 project]<br />
<br />
==='''.:: TODOs ::.'''===<br />
* put a fancy picture in the intro<br />
* put all Links to subpages including a short description in the "site map" section<br />
* check if you like the contents of your personal pages<br />
<br />
<html><body> <br />
<a href="http://www3.clustrmaps.com/counter/maps.php?url=https://2007.igem.org/ETHZ" id="clustrMapsLink"><img src="http://www3.clustrmaps.com/counter/index2.php?url=https://2007.igem.org/ETHZ" style="border:0px;" alt="Locations of visitors to this page" title="Locations of visitors to this page" id="clustrMapsImg" onError="this.onError=null; this.src='http://www2.clustrmaps.com/images/clustrmaps-back-soon.jpg'; document.getElementById('clustrMapsLink').href='http://www2.clustrmaps.com'" /> <br />
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<img src="http://www.easycounter.com/counter.php?stefanluzi" <br />
border="0" alt="Welcome to ETHz - iGEM07"></a> <br />
</body></html></div>LiNanhttp://2007.igem.org/wiki/index.php/Nan_LiNan Li2007-10-24T11:51:53Z<p>LiNan: </p>
<hr />
<div><!--- --[[User:LiNan|LiNan]] 07:51, 24 October 2007 (EDT) ---><br />
<br />
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<body background="https://static.igem.org/mediawiki/2007/c/c0/Igem2007logo_2.png" style="background-attachment: fixed" ><br />
<p align="center"><font face="Arial"><br />
</font></p><br />
<h1><font face="Arial" size="4" color="#000099"><i>About Me:</i></font></h1><br />
<p>I grew up in China and got my Bachelor degree in<br />
<a href="http://en.wikipedia.org/wiki/Zhejiang_University" title="University located in the Heaven City - Hangzhou since 1897">Zhejiang University</a>, Zhejiang, China. <br />
Right now I am doing my Master study in Biomedical Engineering at<br />
<a href="http://www.ethz.ch" title="Eidgenössische Technische Hochschule Zürich since 1854">ETH Zürich</a>. I remain my study interest in various topics, however, <a href="https://2007.igem.org/ETHZ"><IMG height=22 src="https://static.igem.org/mediawiki/2007/4/49/Educateth_Ecoli_b.png" width=150></a> currently ranks the first.<span lang="EN-US" style="font-family: Wingdings">J</span></p><br />
<p><i><span style="font-family: Arial">&quot;A painter paints pictures on canvas; but <br />
musicians paint their pictures on silence.&quot;</i> During my spare time, I enjoy the painting on silence<br />
a lot. Besides, I like hiking with friends and traveling around. The only means <br />
to keep my life refreshing is to experience things new. Moreover,<br />
<a href="http://zh.wikipedia.org/wiki/篆刻" title="Fantastic stone and calligraphy art...">seal engraving</a> always fascinates <br />
me.</span></p><br />
<p>&nbsp;</p><br />
<h1><font face="Arial" size="4" color="#000099"><i>Contact <br />
Information:</i></font></h1><br />
<ul><br />
<li>Mobile:</li><br />
<li>Email:&nbsp;&nbsp;<a href="mailto:leemnan@gmail.com">leemnan@gmail.com</a></li><br />
<li>Mail Address:</li><br />
</ul><br />
<br />
<p>&nbsp;</p><br />
<h1><font face="Arial" size="4" color="#000099"><i>Links:</i></font></h1><br />
<br />
</body><br />
<br />
</html><br />
<br />
{| class="wikitable" style="width:500px; height:200px" border="0" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #ffffff; border: 0px #aaa solid; border-collapse: collapse;" <br />
|valign="top"|<br />
*[http://leemnan.spaces.live.com My Personal Blog]<br />
*[[ETHZ | ETH Zürich iGEM 2007]] <br />
**[[ETHZ/Model | System Modeling]]<br />
**[[ETHZ/Simulation | Simulations]]<br />
**[[ETHZ/Biology | System Implementation]]<br />
**[[ETHZ/Biology/Lab | Lab Notes]]<br />
**[[ETHZ/Meet_the_team | Meet the Team]]<br />
**[[ETHZ/Internal | Team Notes]]<br />
**[[ETHZ/Pictures | Pictures!]]<br />
*[https://2006.igem.org/wiki/index.php/ETH_Zurich_2006 ETH Zürich iGEM 2006]<br />
*[https://2006.igem.org/wiki/index.php/ETH_Zurich_2005 ETH Zürich iGEM 2005]<br />
|style="text-align: center;width:500px; valign:center"|[[Image:NJZY.png|right|151px]]<br />
|}</div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZETHZ2007-10-24T01:52:02Z<p>LiNan: /* Introduction */</p>
<hr />
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<!--1st drop down menu --><br />
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<a href="https://2007.igem.org/wiki/index.php?title=ETHZ#Introduction">Introduction Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ#Team_Members">Team Members Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ#Acknowledgments">Acknowledgments Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ#Site_Map">Site map Section</a><br />
</div><br />
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<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Model#Introduction">Introduction Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Model#Model_Overview">Model Overview Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Model#Detailed_Model">Detailed Model Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Model#Final_Model">Final Model Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Modeling_Basics">Modeling Basics Page</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Model#Mathematical_Model">Mathematical Model Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/FSM">FSM View Page</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/FlipFlop">Flip-Flop View Page</a><br />
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<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Simulation#Simulation_of_Test_Cases">Test Cases Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Simulation#Sensitivity_Analysis">Sensitivity Analysis Section</a><br />
</div><br />
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<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Biology#Introduction">Introduction Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Biology#The_Complete_System">The Complete System Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Biology#System_Phases">System Phases Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Biology/parts">System Parts Page</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Biology/Lab">Lab Notes Page</a><br />
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<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Meet_the_team#The_ETH_Zurich_07_Team">The ETH Zurich 07 Team</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Meet_the_team#Team_Description">Team Description</a><br />
</div><br />
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__NOTOC__<br />
<br />
<center><font size = '+3'><b> ETH Zurich - educatETH ''E.coli'' System</b></font></center><br><br />
<br />
=Introduction=<br />
<blockquote>"All <i>E.coli</i> 's are equal, but some <i>E.coli</i> 's are more equal than others..." ''(freely adapted from the Animal Farm by George Orwell)''</blockquote> <br />
<br />
... this is what George Orwell would have written, if he was a synthetic biologist. In the <i>E.coli</i> colonies on petri dishes, all bacteria are equal, except from some special ones. Our project is about modeling and designing these special <i>E.coli</i> 's, that are "more equal" that the rest, having the ability to recognize their environment, and their story will be presented through this wiki...<br />
<br />
=Motivation=<br />
<br />
WORKING ON IT! <br />
<br />
MISSING = WHAT IS EXACTLY THE SPECIAL THINGS THAT WE DID? DOUBLE PROMOTERS? BIOLOGICAL AUTOMATON WITH MEMORY? COOL WAYS TO EXPLAIN A BIOLOGICAL SYSTEM AS AN ENGINEERING SYSTEM? ROBUST SYSTEM (sensitivity looks pretty good to me...) WE CAN ADD A SECTION LIKE THAT HERE<br />
<br />
Our combined team of biologists and engineers was working on the ''E.coli'' 's ability, first, to recognize two different inputs (here we used two different chemicals), second, to remember which input was presented to them, and third, when confronted with a new input, to recognize whether it is the one that it was trained with or not.<br />
<br />
Thereby, the educatETH ''E.coli'' 's provide a first step towards the development of biological equivalents to complex engineering building blocks like, e.g. , [[ETHZ/FlipFlop | flip-flops]] and other logical circuits. We also introduced the concept of [https://2007.igem.org/ETHZ/Biology/parts#double_promoters double promoters] to the [http://partsregistry.org/Main_Page Registry of Standard Biological Parts], which can be helpful for future projects. The concept of learning and recognition plays a major role in different high-level applications like, e.g., recognition of handwriting on a [http://en.wikipedia.org/wiki/Personal_digital_assistant PDA] as well as [[ETHZ/Applications | biological applications]]. On the other hand, learning plays a major role for living organisms, as well: it enables them to survive by adapting to an ever-changing environment. Therefore, engineering a simple biological system which exhibits learning behavior is of great interest as it can support our understanding of this procedure by comparison with natural systems. <br />
<br />
In the following, we would like to present you a detailed description of the proposed system: starting with the [[ETHZ/Model | modeling of the system]], we describe both, [[ETHZ/Simulation | simulations and theoretical considerations]] of the system, as well as the actual [[ETHZ/Biology | implementation using bio-bricks]] accompanied by our [[ETHZ/Biology/Lab | lab notes]]. Additionally, you find some further [[ETHZ/Meet_the_team | information on the team]], some more details about [[ETHZ/Internal | ideas we developed]] before we came up with the system we finally implemented, and some [[ETHZ/Pictures | pictures]] documenting our work.<br />
<br />
=Team Members=<br />
[[Image:ETHZ_Group_photo_5.png|center|thumb|ETHZ iGEM2007 Team|500px]]<br />
<br />
The ETH Zurich team consists of good mixture between biologists and engineering students, we are:<br />
<ul> <br />
<li><i>Undergraduate students</i>: <br />
[https://2007.igem.org/User:brutsche Martin Brutsche], [https://2007.igem.org/User:kdikaiou Katerina Dikaiou], [https://2007.igem.org/User:Raphael Raphael Guebeli], [https://2007.igem.org/User:hoehnels Sylke Hoehnel], <br />
[https://2007.igem.org/Nan_Li Nan Li], <br />
[https://2007.igem.org/User:Stefan Stefan Luzi]<br />
<li><i>Graduate students</i>: <br />
[http://christos.bergeles.net Christos Bergeles], <br />
[http://www.tik.ee.ethz.ch/~sop/people/thohm/ Tim Hohm], [http://www.fussenegger.ethz.ch/people/kemmerc Christian Kemmer], <br />
[https://2007.igem.org/User:JoeKnight Joseph Knight], <br />
[http://csb.inf.ethz.ch/people/uhr.html Markus Uhr], <br />
[http://www.ricomoeckel.de Rico Möckel]<br />
<li><i>Project advisors</i>: <br />
[http://www.ipe.ethz.ch/laboratories/bpl/people/panke Sven Panke], <br />
[http://csb.inf.ethz.ch/people/stelling.html Joerg Stelling]<br />
</ul><br />
For more information about us, visit our [[ETHZ/Meet_the_team | Meet the Team]] page.<br />
<br />
=Acknowledgments=<br />
The idea for the project as well as its implementation was done by the ETH iGEM 2007 team. Still, we would like to thank the people in [http://www.ipe.ethz.ch/laboratories/bpl/index Sven Panke's Lab], especially Andreas Meyer who was always there for us when we had a problem. Additionally, we would like to thank [http://www.facs.ethz.ch Alfredo Franco-Obregóns lab] and Oralea Büchi for the help with the flow cytometry.<br />
<br />
We would also like to acknowledge the financial support by [http://europa.eu EU], the [http://www.ethz.ch ETH Zurich], and [http://www.geneart.com GeneArt]:<br />
<center><br />
{| border="0"<br />
|-<br />
| [http://europa.eu http://www.tik.ee.ethz.ch/~thohm/EU.gif]<br />
| width="40" |<br />
| [http://www.ethz.ch http://www.tik.ee.ethz.ch/~thohm/ethlogo.jpg]<br />
| width="40" |<br />
| [http://www.geneart.com http://www.tik.ee.ethz.ch/~thohm/geneart.gif]<br />
|}<br />
</center><br />
<br />
=Site Map=<br />
* [[ETHZ/Model | Modeling of the learning system]]<br />
* [[ETHZ/FlipFlop | Implementation using flip-flops]]<br />
* [[ETHZ/FSM | Representation with finite state machines]]<br />
* [[ETHZ/Modeling_Basics | Modeling tutorial]]<br />
* [[ETHZ/Simulation | Model simulations and theoretical considerations]]<br />
* [[ETHZ/Biology | Biological implementation]]<br />
* [[ETHZ/Biology/parts | Biobricks/parts]]<br />
* [[ETHZ/Applications#Applications| Potential applications]] and a link to [[ETHZ/Applications | epigenetics]]<br />
* [[ETHZ/Biology/Lab | Lab notes]]<br />
<br />
* [[ETHZ/Meet_the_team | Team page]]<br />
* [[ETHZ/Pictures | Pictures]]<br />
* [[ETHZ/Internal | Other ideas we developed]]<br />
* others I forgot?<br />
<br />
* [https://2006.igem.org/wiki/index.php/ETH_Zurich_2005 The ETH Zurich 2005 project]<br />
* [https://2006.igem.org/wiki/index.php/ETH_Zurich_2006 The ETH Zurich 2006 project]<br />
<br />
==='''.:: TODOs ::.'''===<br />
* put a fancy picture in the intro<br />
* put all Links to subpages including a short description in the "site map" section<br />
* check if you like the contents of your personal pages<br />
<br />
<html><body> <br />
<a href="http://www3.clustrmaps.com/counter/maps.php?url=https://2007.igem.org/ETHZ" id="clustrMapsLink"><img src="http://www3.clustrmaps.com/counter/index2.php?url=https://2007.igem.org/ETHZ" style="border:0px;" alt="Locations of visitors to this page" title="Locations of visitors to this page" id="clustrMapsImg" onError="this.onError=null; this.src='http://www2.clustrmaps.com/images/clustrmaps-back-soon.jpg'; document.getElementById('clustrMapsLink').href='http://www2.clustrmaps.com'" /> <br />
</a> <br />
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<html><body> <br />
<a> <br />
<img src="http://www.easycounter.com/counter.php?stefanluzi" <br />
border="0" alt="Welcome to ETHz - iGEM07"></a> <br />
</body></html></div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZETHZ2007-10-24T01:50:35Z<p>LiNan: /* Introduction */</p>
<hr />
<div>[[Image:Eth_zh_logo_4.png|830px]]<br />
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<!--1st drop down menu --><br />
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<a href="https://2007.igem.org/wiki/index.php?title=ETHZ#Introduction">Introduction Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ#Team_Members">Team Members Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ#Acknowledgments">Acknowledgments Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ#Site_Map">Site map Section</a><br />
</div><br />
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<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Model#Model_Overview">Model Overview Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Model#Detailed_Model">Detailed Model Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Model#Final_Model">Final Model Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Modeling_Basics">Modeling Basics Page</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Model#Mathematical_Model">Mathematical Model Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/FSM">FSM View Page</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/FlipFlop">Flip-Flop View Page</a><br />
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<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Simulation#Simulation_of_Test_Cases">Test Cases Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Simulation#Sensitivity_Analysis">Sensitivity Analysis Section</a><br />
</div><br />
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<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Biology#Introduction">Introduction Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Biology#The_Complete_System">The Complete System Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Biology#System_Phases">System Phases Section</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Biology/parts">System Parts Page</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Biology/Lab">Lab Notes Page</a><br />
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<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Meet_the_team#The_ETH_Zurich_07_Team">The ETH Zurich 07 Team</a><br />
<a href="https://2007.igem.org/wiki/index.php?title=ETHZ/Meet_the_team#Team_Description">Team Description</a><br />
</div><br />
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<script type="text/javascript"><br />
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<br />
__NOTOC__<br />
<br />
<center><font size = '+3'><b> ETH Zurich - educatETH ''E.coli'' System</b></font></center><br><br />
<br />
=Introduction=<br />
<blockquote>"All <i>E.coli</i>'s are equal, but some <i>E.coli</i>'s are more equal than others..." ''(freely adapted from the Animal Farm by George Orwell)''</blockquote> <br />
<br />
... this is what George Orwell would have written, if he was a synthetic biologist. In the <i>E.coli</i> colonies on petri dishes, all bacteria are equal, except from some special ones. Our project is about modeling and designing these special <i>E.coli</i>'s, that are "more equal" that the rest, having the ability to recognize their environment, and their story will be presented through this wiki...<br />
<br />
=Motivation=<br />
<br />
WORKING ON IT! <br />
<br />
MISSING = WHAT IS EXACTLY THE SPECIAL THINGS THAT WE DID? DOUBLE PROMOTERS? BIOLOGICAL AUTOMATON WITH MEMORY? COOL WAYS TO EXPLAIN A BIOLOGICAL SYSTEM AS AN ENGINEERING SYSTEM? ROBUST SYSTEM (sensitivity looks pretty good to me...) WE CAN ADD A SECTION LIKE THAT HERE<br />
<br />
Our combined team of biologists and engineers was working on the ''E.coli'' 's ability, first, to recognize two different inputs (here we used two different chemicals), second, to remember which input was presented to them, and third, when confronted with a new input, to recognize whether it is the one that it was trained with or not.<br />
<br />
Thereby, the educatETH ''E.coli'' 's provide a first step towards the development of biological equivalents to complex engineering building blocks like, e.g. , [[ETHZ/FlipFlop | flip-flops]] and other logical circuits. We also introduced the concept of [https://2007.igem.org/ETHZ/Biology/parts#double_promoters double promoters] to the [http://partsregistry.org/Main_Page Registry of Standard Biological Parts], which can be helpful for future projects. The concept of learning and recognition plays a major role in different high-level applications like, e.g., recognition of handwriting on a [http://en.wikipedia.org/wiki/Personal_digital_assistant PDA] as well as [[ETHZ/Applications | biological applications]]. On the other hand, learning plays a major role for living organisms, as well: it enables them to survive by adapting to an ever-changing environment. Therefore, engineering a simple biological system which exhibits learning behavior is of great interest as it can support our understanding of this procedure by comparison with natural systems. <br />
<br />
In the following, we would like to present you a detailed description of the proposed system: starting with the [[ETHZ/Model | modeling of the system]], we describe both, [[ETHZ/Simulation | simulations and theoretical considerations]] of the system, as well as the actual [[ETHZ/Biology | implementation using bio-bricks]] accompanied by our [[ETHZ/Biology/Lab | lab notes]]. Additionally, you find some further [[ETHZ/Meet_the_team | information on the team]], some more details about [[ETHZ/Internal | ideas we developed]] before we came up with the system we finally implemented, and some [[ETHZ/Pictures | pictures]] documenting our work.<br />
<br />
=Team Members=<br />
[[Image:ETHZ_Group_photo_5.png|center|thumb|ETHZ iGEM2007 Team|500px]]<br />
<br />
The ETH Zurich team consists of good mixture between biologists and engineering students, we are:<br />
<ul> <br />
<li><i>Undergraduate students</i>: <br />
[https://2007.igem.org/User:brutsche Martin Brutsche], [https://2007.igem.org/User:kdikaiou Katerina Dikaiou], [https://2007.igem.org/User:Raphael Raphael Guebeli], [https://2007.igem.org/User:hoehnels Sylke Hoehnel], <br />
[https://2007.igem.org/Nan_Li Nan Li], <br />
[https://2007.igem.org/User:Stefan Stefan Luzi]<br />
<li><i>Graduate students</i>: <br />
[http://christos.bergeles.net Christos Bergeles], <br />
[http://www.tik.ee.ethz.ch/~sop/people/thohm/ Tim Hohm], [http://www.fussenegger.ethz.ch/people/kemmerc Christian Kemmer], <br />
[https://2007.igem.org/User:JoeKnight Joseph Knight], <br />
[http://csb.inf.ethz.ch/people/uhr.html Markus Uhr], <br />
[http://www.ricomoeckel.de Rico Möckel]<br />
<li><i>Project advisors</i>: <br />
[http://www.ipe.ethz.ch/laboratories/bpl/people/panke Sven Panke], <br />
[http://csb.inf.ethz.ch/people/stelling.html Joerg Stelling]<br />
</ul><br />
For more information about us, visit our [[ETHZ/Meet_the_team | Meet the Team]] page.<br />
<br />
=Acknowledgments=<br />
The idea for the project as well as its implementation was done by the ETH iGEM 2007 team. Still, we would like to thank the people in [http://www.ipe.ethz.ch/laboratories/bpl/index Sven Panke's Lab], especially Andreas Meyer who was always there for us when we had a problem. Additionally, we would like to thank [http://www.facs.ethz.ch Alfredo Franco-Obregóns lab] and Oralea Büchi for the help with the flow cytometry.<br />
<br />
We would also like to acknowledge the financial support by [http://europa.eu EU], the [http://www.ethz.ch ETH Zurich], and [http://www.geneart.com GeneArt]:<br />
<center><br />
{| border="0"<br />
|-<br />
| [http://europa.eu http://www.tik.ee.ethz.ch/~thohm/EU.gif]<br />
| width="40" |<br />
| [http://www.ethz.ch http://www.tik.ee.ethz.ch/~thohm/ethlogo.jpg]<br />
| width="40" |<br />
| [http://www.geneart.com http://www.tik.ee.ethz.ch/~thohm/geneart.gif]<br />
|}<br />
</center><br />
<br />
=Site Map=<br />
* [[ETHZ/Model | Modeling of the learning system]]<br />
* [[ETHZ/FlipFlop | Implementation using flip-flops]]<br />
* [[ETHZ/FSM | Representation with finite state machines]]<br />
* [[ETHZ/Modeling_Basics | Modeling tutorial]]<br />
* [[ETHZ/Simulation | Model simulations and theoretical considerations]]<br />
* [[ETHZ/Biology | Biological implementation]]<br />
* [[ETHZ/Biology/parts | Biobricks/parts]]<br />
* [[ETHZ/Applications#Applications| Potential applications]] and a link to [[ETHZ/Applications | epigenetics]]<br />
* [[ETHZ/Biology/Lab | Lab notes]]<br />
<br />
* [[ETHZ/Meet_the_team | Team page]]<br />
* [[ETHZ/Pictures | Pictures]]<br />
* [[ETHZ/Internal | Other ideas we developed]]<br />
* others I forgot?<br />
<br />
* [https://2006.igem.org/wiki/index.php/ETH_Zurich_2005 The ETH Zurich 2005 project]<br />
* [https://2006.igem.org/wiki/index.php/ETH_Zurich_2006 The ETH Zurich 2006 project]<br />
<br />
==='''.:: TODOs ::.'''===<br />
* put a fancy picture in the intro<br />
* put all Links to subpages including a short description in the "site map" section<br />
* check if you like the contents of your personal pages<br />
<br />
<html><body> <br />
<a href="http://www3.clustrmaps.com/counter/maps.php?url=https://2007.igem.org/ETHZ" id="clustrMapsLink"><img src="http://www3.clustrmaps.com/counter/index2.php?url=https://2007.igem.org/ETHZ" style="border:0px;" alt="Locations of visitors to this page" title="Locations of visitors to this page" id="clustrMapsImg" onError="this.onError=null; this.src='http://www2.clustrmaps.com/images/clustrmaps-back-soon.jpg'; document.getElementById('clustrMapsLink').href='http://www2.clustrmaps.com'" /> <br />
</a> <br />
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<center><font size = '+3'><b> ETH Zurich - educatETH ''E.coli'' System</b></font></center><br><br />
<br />
=Introduction=<br />
<blockquote>"All <i>E.coli</i>'s are equal, but some <i>E.coli</i>'s are more equal than others..." ''(freely adapted from the Animal Farm by George Orwell)''</blockquote> <br />
<br />
... this is what George Orwell would have written, if he was a synthetic biologist. In the <i>E. coli</i> colonies on petri dishes, all bacteria are equal, except from some special ones. Our project is about modeling and designing these special <i>E. coli</i>'s, that are "more equal" that the rest, having the ability to recognize their environment, and their story will be presented through this wiki...<br />
<br />
=Motivation=<br />
<br />
WORKING ON IT! <br />
<br />
MISSING = WHAT IS EXACTLY THE SPECIAL THINGS THAT WE DID? DOUBLE PROMOTERS? BIOLOGICAL AUTOMATON WITH MEMORY? COOL WAYS TO EXPLAIN A BIOLOGICAL SYSTEM AS AN ENGINEERING SYSTEM? ROBUST SYSTEM (sensitivity looks pretty good to me...) WE CAN ADD A SECTION LIKE THAT HERE<br />
<br />
Our combined team of biologists and engineers was working on the ''E.coli'' 's ability, first, to recognize two different inputs (here we used two different chemicals), second, to remember which input was presented to them, and third, when confronted with a new input, to recognize whether it is the one that it was trained with or not.<br />
<br />
Thereby, the educatETH ''E.coli'' 's provide a first step towards the development of biological equivalents to complex engineering building blocks like, e.g. , [[ETHZ/FlipFlop | flip-flops]] and other logical circuits. We also introduced the concept of [https://2007.igem.org/ETHZ/Biology/parts#double_promoters double promoters] to the [http://partsregistry.org/Main_Page Registry of Standard Biological Parts], which can be helpful for future projects. The concept of learning and recognition plays a major role in different high-level applications like, e.g., recognition of handwriting on a [http://en.wikipedia.org/wiki/Personal_digital_assistant PDA] as well as [[ETHZ/Applications | biological applications]]. On the other hand, learning plays a major role for living organisms, as well: it enables them to survive by adapting to an ever-changing environment. Therefore, engineering a simple biological system which exhibits learning behavior is of great interest as it can support our understanding of this procedure by comparison with natural systems. <br />
<br />
In the following, we would like to present you a detailed description of the proposed system: starting with the [[ETHZ/Model | modeling of the system]], we describe both, [[ETHZ/Simulation | simulations and theoretical considerations]] of the system, as well as the actual [[ETHZ/Biology | implementation using bio-bricks]] accompanied by our [[ETHZ/Biology/Lab | lab notes]]. Additionally, you find some further [[ETHZ/Meet_the_team | information on the team]], some more details about [[ETHZ/Internal | ideas we developed]] before we came up with the system we finally implemented, and some [[ETHZ/Pictures | pictures]] documenting our work.<br />
<br />
=Team Members=<br />
[[Image:ETHZ_Group_photo_5.png|center|thumb|ETHZ iGEM2007 Team|500px]]<br />
<br />
The ETH Zurich team consists of good mixture between biologists and engineering students, we are:<br />
<ul> <br />
<li><i>Undergraduate students</i>: <br />
[https://2007.igem.org/User:brutsche Martin Brutsche], [https://2007.igem.org/User:kdikaiou Katerina Dikaiou], [https://2007.igem.org/User:Raphael Raphael Guebeli], [https://2007.igem.org/User:hoehnels Sylke Hoehnel], <br />
[https://2007.igem.org/Nan_Li Nan Li], <br />
[https://2007.igem.org/User:Stefan Stefan Luzi]<br />
<li><i>Graduate students</i>: <br />
[http://christos.bergeles.net Christos Bergeles], <br />
[http://www.tik.ee.ethz.ch/~sop/people/thohm/ Tim Hohm], [http://www.fussenegger.ethz.ch/people/kemmerc Christian Kemmer], <br />
[https://2007.igem.org/User:JoeKnight Joseph Knight], <br />
[http://csb.inf.ethz.ch/people/uhr.html Markus Uhr], <br />
[http://www.ricomoeckel.de Rico Möckel]<br />
<li><i>Project advisors</i>: <br />
[http://www.ipe.ethz.ch/laboratories/bpl/people/panke Sven Panke], <br />
[http://csb.inf.ethz.ch/people/stelling.html Joerg Stelling]<br />
</ul><br />
For more information about us, visit our [[ETHZ/Meet_the_team | Meet the Team]] page.<br />
<br />
=Acknowledgments=<br />
The idea for the project as well as its implementation was done by the ETH iGEM 2007 team. Still, we would like to thank the people in [http://www.ipe.ethz.ch/laboratories/bpl/index Sven Panke's Lab], especially Andreas Meyer who was always there for us when we had a problem. Additionally, we would like to thank [http://www.facs.ethz.ch Alfredo Franco-Obregóns lab] and Oralea Büchi for the help with the flow cytometry.<br />
<br />
We would also like to acknowledge the financial support by [http://europa.eu EU], the [http://www.ethz.ch ETH Zurich], and [http://www.geneart.com GeneArt]:<br />
<center><br />
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|-<br />
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| [http://www.geneart.com http://www.tik.ee.ethz.ch/~thohm/geneart.gif]<br />
|}<br />
</center><br />
<br />
=Site Map=<br />
* [[ETHZ/Model | Modeling of the learning system]]<br />
* [[ETHZ/FlipFlop | Implementation using flip-flops]]<br />
* [[ETHZ/FSM | Representation with finite state machines]]<br />
* [[ETHZ/Modeling_Basics | Modeling tutorial]]<br />
* [[ETHZ/Simulation | Model simulations and theoretical considerations]]<br />
* [[ETHZ/Biology | Biological implementation]]<br />
* [[ETHZ/Biology/parts | Biobricks/parts]]<br />
* [[ETHZ/Applications#Applications| Potential applications]] and a link to [[ETHZ/Applications | epigenetics]]<br />
* [[ETHZ/Biology/Lab | Lab notes]]<br />
<br />
* [[ETHZ/Meet_the_team | Team page]]<br />
* [[ETHZ/Pictures | Pictures]]<br />
* [[ETHZ/Internal | Other ideas we developed]]<br />
* others I forgot?<br />
<br />
* [https://2006.igem.org/wiki/index.php/ETH_Zurich_2005 The ETH Zurich 2005 project]<br />
* [https://2006.igem.org/wiki/index.php/ETH_Zurich_2006 The ETH Zurich 2006 project]<br />
<br />
==='''.:: TODOs ::.'''===<br />
* put a fancy picture in the intro<br />
* put all Links to subpages including a short description in the "site map" section<br />
* check if you like the contents of your personal pages<br />
<br />
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<img src="http://www.easycounter.com/counter.php?stefanluzi" <br />
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</body></html></div>LiNanhttp://2007.igem.org/wiki/index.php/Nan_LiNan Li2007-10-24T00:43:20Z<p>LiNan: </p>
<hr />
<div><!--- <br />
<p align="center"><font size="6"><b>ETH Zurich - educatETH <i>E.coli</i> System</b></font></p><br />
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</font></p><br />
<h1><font face="Arial" size="4" color="#000099"><i>About Me:</i></font></h1><br />
<p>I grew up in China and got my Bachelor degree in<br />
<a href="http://en.wikipedia.org/wiki/Zhejiang_University" title="University located in the Heaven City - Hangzhou since 1897">Zhejiang University</a>, Zhejiang, China. <br />
Right now I am doing my Master study in Biomedical Engineering at<br />
<a href="http://www.ethz.ch" title="Eidgenössische Technische Hochschule Zürich since 1854">ETH Zürich</a>. I remain my study interest in various topics, however, <a href="https://2007.igem.org/ETHZ"><IMG height=22 src="https://static.igem.org/mediawiki/2007/4/49/Educateth_Ecoli_b.png" width=150></a> currently ranks the first.<span lang="EN-US" style="font-family: Wingdings">J</span></p><br />
<p><i><span style="font-family: Arial">&quot;A painter paints pictures on canvas; but <br />
musicians paint their pictures on silence.&quot;</i> During my spare time, I enjoy the painting on silence<br />
a lot. Besides, I like hiking with friends and traveling around. The only means <br />
to keep my life refreshing is to experience things new. Moreover,<br />
<a href="http://zh.wikipedia.org/wiki/篆刻" title="Fantastic stone and calligraphy art...">seal engraving</a> always fascinates <br />
me.</span></p><br />
<p>&nbsp;</p><br />
<h1><font face="Arial" size="4" color="#000099"><i>Contact <br />
Information:</i></font></h1><br />
<ul><br />
<li>Mobile:</li><br />
<li>Email:&nbsp;&nbsp;<a href="mailto:leemnan@gmail.com">leemnan@gmail.com</a></li><br />
<li>Mail Address:</li><br />
</ul><br />
<br />
<p>&nbsp;</p><br />
<h1><font face="Arial" size="4" color="#000099"><i>Links:</i></font></h1><br />
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{| class="wikitable" style="width:500px; height:200px" border="0" cellspacing="0" cellpadding="2" style="text-align:left; margin: 1em 1em 1em 0; background: #ffffff; border: 0px #aaa solid; border-collapse: collapse;" <br />
|valign="top"|<br />
*[http://leemnan.spaces.live.com My Personal Blog]<br />
*[[ETHZ | ETH Zürich iGEM 2007]] <br />
**[[ETHZ/Model | System Modeling]]<br />
**[[ETHZ/Simulation | Simulations]]<br />
**[[ETHZ/Biology | System Implementation]]<br />
**[[ETHZ/Biology/Lab | Lab Notes]]<br />
**[[ETHZ/Meet_the_team | Meet the Team]]<br />
**[[ETHZ/Internal | Team Notes]]<br />
**[[ETHZ/Pictures | Pictures!]]<br />
*[https://2006.igem.org/wiki/index.php/ETH_Zurich_2006 ETH Zürich iGEM 2006]<br />
*[https://2006.igem.org/wiki/index.php/ETH_Zurich_2005 ETH Zürich iGEM 2005]<br />
|style="text-align: center;width:500px; valign:center"|[[Image:NJZY.png|right|151px]]<br />
|}</div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/SimulationETHZ/Simulation2007-10-23T21:02:15Z<p>LiNan: Figure rearrange</p>
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<center>[[ETHZ | Main Page]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Model | System Modeling]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Simulation | Simulations]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Biology | System Implementation]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Biology/Lab| Lab Notes]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Meet_the_team | Meet the Team]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Internal | Team Notes]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Pictures | Pictures!]]</center><br><br />
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=Introduction=<br />
<br />
As has been presented in the [[ETHZ/Model | Modeling]] page, we have created a model of our system that can be described as a finite state machine. In order to examine the behavior of our system thoroughly, we have to simulate it through all the difference phases that can be reached. We carry out our simulations based on the protocol presented in Fig. 1.<br />
<br />
[[Image:ETHzFlowdiagram2.png|center|thumb|<b>Fig. 1</b>: Flow diagram. This figure shows the protocol with which the final system should be tested, as well as the test results in the form of the reported colors. There are three phases the system has to go through: (1) a training or learning phase in which the system learns an input and stores it in its memory, (2) a memory phase in which the system has to keep the content of its memory and, (3) a recognition phase where the output of the system depends on the content of its memory as well as on the current input. |500px]]<br />
<br />
== Simulation of Test Cases ==<br />
To test all the possible conditions and transitions in our system, we run timing simulations for all 4 different branches in the flow diagram of our protocol, and we plot the concentrations of the inducers, memory proteins and reporter proteins. During each learning, memorizing and recognizing phase, we wait for the steady state of the system to be reached. The three phases of the system are thus separated sufficiently in time. In all the cases, we follow the following procedure:<br />
* During the first 1000 minutes of the simulation, we do not add any inducers to the system, and as a result, we check for the baseline production of proteins.<br />
* Between 1000 and 2000 minutes, we add the inducer that should be learned, and we let the system reach steady state.<br />
* After 2000 minutes, we add the inducer AHL for memory formation, and run the simulation for an additional 1000 minutes, to reach again steady state.<br />
* Between 3000 and 4000 minutes, we test the final behavior of the system, and check if it reacts in the desired way, by showing the appropriate color.<br />
<br />
The parameters of the system are crucial if one wants to have accurate and realistic simulations. We present the parameters used to simulate our system, in the section [[ETHZ/Parameters | Parameters]].<br />
<br />
<p>[[Image:ETHZTest1correct.png|left|thumb|<b>Fig. 2</b>: Test case 1 - Learn and recognize IPTG. The simulated behavior of the system, when IPTG is presented both during the learning phase, and the recognition phase. In both the learning and recognition phase, the system reports by producing CFP, which matches the desired behavior. |420px]]<br />
[[Image:ETHZTest2correct.png|thumb|<b>Fig. 3</b>: Test case 2 - Learn IPTG and get tested for aTc. The simulated behavior of the system, when IPTG is presented during the learning phase, and aTc is presented during the recognition phase. The system reports by producing RFP during the recognition phase, which matches the desired behavior. |420px]]<br />
<br />
[[Image:ETHZTest4correct.png|left|thumb|<b>Fig. 4</b>: Test case 3 - Learn aTc and get tested for IPTG. The simulated behavior of the system, when aTc is presented during the learning phase, and IPTG is presented during the recognition phase. The system reports by producing GFP during the recognition phase, which matches the desired behavior. |420px]]</p><br />
<br />
[[Image:ETHZTest3correct.png|right|thumb|<b>Fig. 5</b>: Test case 4 - Learn and recognize aTc. The simulated behavior of the system, when aTc is presented both during the learning phase and the recognition phase. In both the learning and recognition phase the system reports by producing YFP, which matches the desired behavior. |420px]]<br />
<br />
==Sensitivity Analysis==<br />
<br />
Before performing simulations we did an intensive literature review to find the parameters of our system. Due to the uncertainty in parameter retrieval, we would like our system to have reduced sensitivity to the parameters. That way, we can have a guaranteed stable performance, even under non-simulated conditions.<br />
<br />
[[Image:ETHZSensitivity.png|right|thumb|<b>Fig. 6</b>: Sensitivity analysis. We plot the sensitivity for each parameter and each test case that corresponds to the different path ways in Fig. 1. |500px]]<br />
<br />
We define sensitivity as the change of the production of the desired florescence protein - which corresponds to the output of our system - depending on the change of the parameters. As shown in Fig. 1 the desired florescence protein output depends on the inducers that we present to our system during learning and recognition phase. Overall we have 4 different combinations leading to the 4 different test cases shown in Fig. 1. E.g. when IPTG is presented to the system both during the learning and recognition phase we expect the production of CFPs. That is why we define the sensitivity for test case 1 as<br />
<br />
[[Image:ETHZSensititivityequation.png|center|300px]]<br />
<br />
<!---<math>Sensitivity = \left | \frac{[ CFP^{105%}_{recognition} ] }{\left [ CFP^{105%}_{baseline}\right ] }- \frac{\left [ CFP^{95%}_{recognition} \right ] }{\left [ CFP^{95%}_{baseline} \right ] }\right |</math>---><br />
<br />
where <br />
* [CFP<sub>recognition</sub><sup>105%</sup>] describes the concentration of CFP during the recognition phase when the parameter that we test for is increased by 5%, <br />
* [CFP<sub>baseline</sub><sup>105%</sup>] describes the concentration of CFP without any inducers given to the system when the parameter that we test for is increased by 5%,<br />
* [CFP<sub>recognition</sub><sup>95%</sup>] describes the concentration of CFP during the recognition phase when the parameter that we test for is decreased by 5%, and <br />
* [CFP<sub>baseline</sub><sup>95%</sup>] describes the concentration of CFP without any inducers given to the system when the parameter that we test for is decreased by 5%.<br />
<br />
All concentrations are gained from timing simulations as shown in Fig. 2 when the concentrations reached steady state. E.g. [CFP<sub>recognition</sub>] corresponds to the value of CFP after 4000 minutes while [CFP<sub>baseline</sub>] is equal to the value for CFP after 1000 minutes.<br />
<br />
We define sensitivity by using the ratio of [CFP<sub>recognition</sub>] and [CFP<sub>baseline</sub>] to see by which factor the CFP concentration is increased during the recognition phase. The higher this value is the easier it will be possible to determine the correct readout.<br />
<br />
We subtract the two ratios of [CFP<sub>recognition</sub>]/[CFP<sub>baseline</sub>] for increasing and decreasing the parameter under test by 5% to quantify how much is the influence of the parameter on the output. If the output ratio does not change the parameter has no influence and the sensitivity is equal to zero.<br />
<br />
We quantified the sensitivity for all parameters and all 4 test cases. Therefor we look at the concentration of <br />
* CFP in test case 1. A timing simulation for this case is shown in Fig. 2.<br />
* RFP in test case 2. A timing simulation for this case is shown in Fig. 3.<br />
* YFP in test case 3. A timing simulation for this case is shown in Fig. 4.<br />
* GFP in test case 4. A timing simulation for this case is shown in Fig. 5.<br />
The results are plotted in Fig. 6. Furthermore we make sure that no undesired florescence protein is produced during the recognition phase.<br />
<br />
The conclusions that can be derived from our sensitivity analysis are the following:<br />
* Most of the parameters are not sensitive which corresponds to the desired behavior since the system should ideally give a constant output for the same input sequence.<br />
* Our system is most sensitive to the parameter "a", which describes the base production. This is not a surprising result, since we depend on having a good switch. However, we cannot reliably switch off the production of the memory proteins cI and p22cII. This leads to a small signal to noise ration and in the worst case the memory does not work at all. For a more detailed analysis of the switching behavior please see our [[ETHZ/Biology | biological implementation]] page.<br />
* Other sensitive parameters are those that are directly related to the production and decay of the memory proteins cI and p22cII. This can be explained by the fact that our system prefers symmetry between the parameter sets of cI and p22cII, in order to support good switching behavior where the same concentration of p22cII leads to the same repression of cI and vice versa. We can especially see that those parameters seem to be more sensitive for the test cases 1 and 2, where cI has to be stored inside the memory. The reason for this behavior is due to the missing symmetry between the parameters for cI and p22cII, we currently have a bias inside the system towards the production of p22cII.</div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/ModelETHZ/Model2007-10-23T11:51:49Z<p>LiNan: text - figure fitting</p>
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<br />
<center>[[ETHZ | Main Page]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Model | System Modeling]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Simulation | Simulations]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Biology | System Implementation]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Biology/Lab| Lab Notes]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Meet_the_team | Meet the Team]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Internal | Team Notes]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Pictures | Pictures!]]</center><br><br />
<br />
__NOTOC__<br />
<br />
=Introduction=<br />
<br />
As previously discussed in the main page, we are interested in designing a system that is able to adapt to its environment. Our ideas were based on discussions about neural networks, and how we can create a biological system that exhibits the behavior of learning, without having to resort to evolutionary processes. <br />
<br />
[[Image:ETHzFlowdiagram2.png|thumb|<b>Fig. 1</b>: Flow diagram. This figure shows the protocol with which the final system should be tested as well as the test results in form of the reported colors. There are three phases the system performs: (1) a training or learning phase in which the system learns an input and stores it in its memory, (2) a memory phase in which the system keeps the content of its memory, and finally (3) a recognition phase where the output of the system depends on the content of its memory as well as the current input.|450px]]<br />
<br />
Learning can be considered as a switching of behavior, based on some external stimuli. Thus it comes naturally to work on existing ideas of toggle switches and finite state automatons. <br />
<br />
In our system, we are able to distinguish between only two chemicals. We propose only a minimal system that should be able to act as a proof of concept. By introducing the ability to have more final states, in an abstract manner, this correlates with the level of "intelligence" of the biological system. A protocol on how the system should react according to an input is shown in Fig. 1.<br />
<br />
The idea behind this protocol is that:<br />
* The system will be able to learn one of two input signals - aTc or IPTG - during a learning phase if no input signal AHL is present. Depending on the input it will report by producing either green or yellow florescence. <br />
* Once the system has learned, the inputs - aTc or IPTG - can be removed and the system goes into a memory state in the presence of the "helper" substance AHL. In this state no output color is reported. Memorizing is guaranteed by removing the input chemicals. This results in a subsequent successful recognition phase.<br />
* During the recognition phase, the inputs aTc or IPTG are (re-)inserted. The system reports by changing its color depending on the input and its current memory state. This is why the system has different florescent properties even in the presence of the same input. The recognition phase takes place in the presence of AHL to keep the memory enabled and avoid another learning phase. Since we would like to separate four different end states for our system, we had to use four fluorescent proteins to encode them.<br />
<br />
==Model Overview==<br />
<br />
One can start developing our system with a top-down approach. We start with the classical black box approach as shown in Figure 2. <br />
<br />
[[Image:ETHZBlackbox.png|thumb|<b>Fig. 2</b>: Black box |280px]]<br />
<br />
Basedcan be summarized as followshas can be summarized as follows:<br />
* Two inputs to be learned/detected/adapted to,<br />
* One input to switch on the memory,<br />
* To alternate between at least three states. Hence why we decided to use two state variables - cI and p22cII.<br />
* Four florescent signals for the outputs. One could also decide to take six output signals into account, to further distinguish the learning phase from the recognition phase. However, we restricted ourselves to 4 outputs to reduce the number of genes that are needed to implement the signals.<br />
<br />
[[Image:ETHZFullsystemmodel.png|left|thumb|<b>Fig. 3</b>: System overview. AHL, IPTG and aTc pass the cell membrane where they build complexes with the sensor proteins LuxR, LacI and TetR. These sensor proteins and/or complexes are used to control memory formation and the production of the reporter proteins. The memory content is represented by the proteins cI and p22cII that repress the production of each other. CFP, RFP, GFP and YFP stand for yellow, red, cyan and green florescent protein, respectively.|420px]]<br />
<br />
Based on the above, the internal structure of the system can be defined, and seen in Figure 3. However, we had to keep in mind that the proposed system should be implemented in DNA, and that it would be sensitive to noise. As a result, we took several actions to achieve better experimental results and easier DNA construction:<br />
* To be more robust against perturbations, we coupled the state variables cI and p22cII in the way that is well known from memory circuits. Based on this approach, one state variable is depressing the other one, and the system's internal toggle has the possibility of reaching two stable states.<br />
* Since - due to their size - proteins can only hardly pass the cell membrane (if they are not actively transported through the cell membrane), we decided to use the much smaller inducer molecules AHL, IPTG and aTc as inputs. However, since these inducers cannot directly act on the transcription of the DNA nor on the production of proteins, we need to produce the sensor proteins LuxR, LacI and TetR that build complexes with AHL, IPTG and aTc, respectively.<br />
* The sensor proteins and complexes are used to control the memory formation and the production of the florescent reporter proteins CFP, RFP, GFP and YFP.<br><br><br />
<br />
==Detailed Model==<br />
<br />
In order to test our ideas, we had to find the equations that describe the proposed model, and fill in the gaps. In this section, we provide the details of our model, giving precise descriptions of the involved molecules and proteins. Our model is created on the basic principles of finite state machines. It is a biological automaton that moves from the learning states, to the memorizing and recognizing states, as it was presented in Fig. 1. For a detailed analysis of the underlying finite state machine please see the section [[ETHZ/FSM|Finite State Machine View of the System]]. To see how our memory system translates into a flip flop memory have a look at the section [[ETHZ/FlipFlop|An Engineer's View on Biological Learning]]<br><br />
<br />
===Sensors===<br />
<br />
As we can observe in Fig. 3, our system is composed from three basic subparts. The first part contains the sensors. Our sensors are the proteins LacI, luxR and TetR, and they are constitutively produce, in order to regulate the operation of the rest of the system. The sensing subsystem can be seen in Fig. 4.<br />
<br />
[[Image:Model01b.png|center|thumb|<b>Fig. 4</b>: The proteins that act as sensors are constitutively produced.|140px]]<br />
<br />
===Memory===<br />
<br />
The second subsystem is responsible for the creation and control of memories. The memory control is based on the following underlying mechanisms:<br />
* The sensor proteins form complexes together with the inducers. These complexes are used to either activate (in case of the complex consisting of luxR and AHL) or repress (in case of the complexes consisting of LacI and IPTG as well as TetR and aTc) the DNA transcription of the proteins cI and p22cII. <br />
* p22cII and cI repress the DNA transcription of each other, so that the closed loop system behaves as a toggle; a dynamic system with only two possible steady states (see Fig. 6).<br />
<br />
[[Image:ETHZModelLearning.png|center|thumb|<b>Fig. 5</b>: Learning system. Depending on the inputs IPTG or aTc the proteins cI and p22cII are produced.|300px]]<br />
<br />
* Fig. 5 shows the protein production system that is used during the learning phase. During the learning phase, there is still no cI or p22cII produced. They are produced, only if either IPTG or aTc is added, respectively. Since no AHL is present, the inner toggle switch (see Figure 6) is turned off.<br />
<br />
[[Image:ETHZModelMemory.png|center|thumb|<b>Fig. 6</b>: Memory system. If AHL is present the production of either cI or p22cII is continued.|420px]]<br />
<br />
* During the memory phase, AHL is added and the IPTG and aTc are removed. That is why only the inner toggle switch (see Fig. 6) is turned on while the protein production systems shown in Fig. 5 are deactivated. Depending on what was produced during the learning phase, the production of either cI or p22cII is continued. That is why the system can act as memory, effectively storing the information that it is exposed to.<br />
<br />
Based on all the above, we present the final assembly of the memory subsystem in Fig. 7.<br />
<br />
[[Image:Model02b.png|center|thumb|<b>Fig. 7</b>: Final interaction of the learning and memory system. The memory content is represented by the concentrations of the proteins cI and p22cII.|560px]]<br />
<br />
===Reporters===<br />
<br />
Fig. 8 gives an overview about the reporter subsystem. Florescent reporter proteins are expressed depending on the inducer concentrations, and the concentrations of cI and p22cII. For example, the presence of either TetR or cI will repress the production of YFP. However, if the inducer aTc is present, aTc will bind to TetR which can no longer block the production of YFP. We are using four fluorescent proteins, to encode the steady states of our system at the final recognition stage. In this way, we are able to distinguish between all the different transition paths of our biological automaton.<br />
<br />
[[Image:Model03b.png|center|thumb|<b>Fig. 8</b>: The production of the florescent reporter proteins depends on the memory content (cI or p22cII) and the current input (aTc or IPTG).|600px]]<br />
<br />
==Final Model==<br />
<br />
We have so far presented all the parts that are needed in order to model and simulate the behavior of a biological automaton with the ability to memorize and recognize the chemical that it is exposed to. By following the details presented in the previous section, we have all the necessary information to fully understand the interior of the black boxes that were presented in Fig. 2 and Fig. 3. Our overall system model is presented in Fig. 9.<br />
<br />
[[Image:ETHZFullsystem.png|center|thumb|<b>Fig. 9</b>: Final model of the EducatETH <i>E. coli</i> system.|900px]]<br />
<br />
==Mathematical Model==<br />
<br />
Based on the modeling done so far, we can derive the equations that govern the behavior of our system. The model is governed by sets of coupled ordinary differential equations which are presented below. We use a simple notation for the different elements of the equations. Namely: <br />
* All concentrations are given in brackets (for example [cI]). <br />
* All decay constants are described by a variable d followed by the name of the protein they refer to. <br />
* The production of the proteins is described by a basic constant production level that models the leak of the production system, and a factor of l and c<sub>max</sub> that describe the maximum production of a protein, given in [M]. <br />
* Depending on whether the DNA for a protein is implemented on a low or a high copy plasmid, we distinguish between l<sub>lo</sub> and l<sub>hi</sub>, respectively.<br />
<br />
For a more basic introduction into how we transferred our model into equations, see the section [[ETHZ/Modeling_Basics|Modeling Basics]].<br><br />
<br />
===Constitutively produced proteins===<br />
<br />
The equations for the constitutively produced proteins are very simple, since there is no dependence on other proteins. They are designed so that the protein concentration reaches the value l<sub>hi</sub>*c<sub>max</sub>/d at steady state.<br />
<br />
[[Image:Constitutive_braced.png|330px]]<br />
<br />
===Allosteric regulation===<br />
<br />
These equations describe the formation of complexes between the inducers and sensor proteins. We do not use differential equations, but describe directly the concentrations of the complexes. This is a valid assumption, provided that we always wait a sufficient time, and the system reaches a steady state.<br />
We describe the total amount of proteins with the index 't', while we use the index '*' for proteins that build a complex with their respective inducer. For example: <br />
* [TetR<sub>t</sub>] describes the total amount of TetR that is available. <br />
* [TetR<sub>*</sub>] describes the proteins that are available as a complex with aTc, and<br />
* [TetR] gives the concentration of free TetR proteins.<br />
<br />
[[Image:Eq04.png|208px]]<br />
<br />
===Learning and memory subsystem===<br />
<br />
The learning and memory subsystem is the core of the system that we are trying to model and implement. It is characterized by the feedback between its state variables/proteins cI and p22cII. Its behavior is further complicated by the variation of the production of the aforementioned proteins because of the inputs. The following equations describe the concentrations of the memory proteins as a system of coupled differential equations. The equations consist of two major production parts and a decay part. <br />
* The first production part models the production of either cI or p22cII during the learning phase, and corresponds to the model in Fig. 5.<br />
* The second production part describes the inner toggle switch that was shown in Fig. 6.<br />
<br />
[[Image:Toggle_braced.png|770px]]<br />
<br />
===Reporting subsystem===<br />
<br />
The equations for the reporting subsystem finally describe the production of the florescence proteins depending on the inputs and memory proteins as modeled in Figure 8. Note that both inputs and memory proteins act repressively on the production of the florescence proteins. So e.g. YFP is only produced when there is both no cI and all TetR is bound in a complex together with aTc.<br />
<br />
[[Image:Reporter_braced.png|778px]]<br />
<br />
The systems of equations presented above describe and predict the behavior of our system. We have simulated the behavior of our system at steady states, and the results can be seen in the section [[ETHZ/Simulation|Simulations]]. In order to increase the accuracy of our results, we conducted an extensive literature survey, in order to isolate and find the parameters of our system. Since this is a burden for every team undertaking a complicated project in synthetic biology, we are presenting our full table of parameters in the [[ETHZ/Parameters|Parameters]] page.</div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/BiologyETHZ/Biology2007-10-23T08:27:06Z<p>LiNan: Move away the "Application" part</p>
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<center>[[ETHZ | Main Page]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Model | System Modeling]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Simulation | Simulations]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Biology | System Implementation]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Biology/Lab | Lab Notes]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Meet_the_team | Meet the Team]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Internal | Team Notes]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Pictures | Pictures!]]</center><br><br />
<br />
<center><font size = '+1'><b> .:: educatETH <i>E.coli</i> - Biology Perspective ::. </b></font></center><br><br />
<br />
<p>On this page, you can find an analysis of the function of our system, its biological design, and a list of the parts that make up the system. Are you interested in constructing educatETH <i>E.coli</i> in your lab? Then under [https://2007.igem.org/ETHZ/Biology/Lab Lab Notes], you can find the ingredients and equipment we used, the electronic version of our lab notebook and a presentation of all the difficulties that we encountered. If you are also interested in how educatETH ''E.coli'' was modeled and simulated outside the lab, please visit the [[ETHZ/Model | modeling]] and [[ETHZ/Simulation | simulations]] web pages. </p><br />
<br />
__NOTOC__<br />
<br />
= Introduction =<br />
<br />
<p> educatETH <i>E.coli</i> is a system which can distinguish between [http://openwetware.org/wiki/ATc anhydrotetracycline (aTc)] and [http://openwetware.org/wiki/IPTG Isopropyl-beta-D-thiogalactopyranoside (IPTG)] based on a previous training phase conducted with the same chemicals and the help of [http://partsregistry.org/Acyl-HSLs AHL]. It is composed of three subsystems: the subsystem of constitutively produced proteins, the learning subsystem and the reporting subsystem. The constitutively produced proteins (LacI, TetR and LuxR) control the learning subsystem. At the core of the latter there exists a modified and extended version of the Elowitz toggle switch found in [1]. The main difference is reflected in the use of double promoters, so that the toggle switch only changes its state when both, one of the two chemicals (aTc/IPTG), and AHL are present. As AHL is only present during the training phase, the toggle maintains its state during testing, and thus can “memorize”. AHL can therefore be seen as a training- or learning substance. In the reporting subsystem, four reporters ([http://partsregistry.org/Featured_Parts:Fluorescent_proteins fluorescent proteins]) allow supervision of both the chemical the system was trained with and if the system recognizes the chemical it is being exposed to in the testing phase as one it has been previously trained with or not.</p><br><br />
<br />
== The Complete System ==<br />
<br />
<p>The biological design of educatETH <i>E.coli</i> is presented in Fig. 1 [[Image:Biol_system_stand20.10.png|thumb|left|300px|'''Fig. 1:''' Gene interaction network of educatETH ''E.coli'' ]] and below, we clarify the function of all depicted components. (Are you interested in how the complex system of Fig. 1 was modeled? Then visit the [[ETHZ/Model| System Modeling]]!)</p><br><br />
<br />
==== Constitutive Subsystem ====<br />
<br />
<p>The constitutively produced proteins of the system are LacI, TetR and LuxR. The LuxR part has a special function: when AHL is present, it forms a LuxR-AHL complex which acts on the learning subsystem (more on this later). For now, we will consider that AHL is absent and therefore LuxR cannot act on any subsystems. The TetR and LacI parts behave similarly: more specifically, the TetR protein in the absence of aTc inhibits the production of P22 cII and LacI in the absence of IPTG inhibits the production of cI. When aTc is present, however, the P22 cII production is no longer inhibited (and thus p22 cII is produced). Correspondingly, cI is produced when IPTG is present.</p><br />
<br />
==== Learning Subsystem ====<br />
<br />
<p>The learning subsystem is a toggle switch with two operator sites. The upper part of the toggle (cI production) has operator sites for the LuxR-AHL complex and P22 cII (which has in turn been induced by aTc). The LuxR-AHL complex induces the cI production, whereas P22 cII inhibits it. The lower part of the toggle (P22 cII production) has operator sites for the LuxR-AHL complex and cI (which has been induced by IPTG). Similarly with the upper part, the LuxR-AHL complex induces the P22 cII production and cI inhibits it. Therefore, the switch always requires the presence of the LuxR-AHL complex in order for it to operate. Its state depends on the presence of P22 cII and cI into the system, which in return was caused through the exposure of the system to aTc and IPTG.</p><br />
<br />
==== Reporting Subsystem ====<br />
<br />
<p>There are four reporters in the system. CFP (more precisely: enhanced CFP, that is eCFP) and YFP (more precisely: enhanced YFP, that is eYFP) are active during the training phase of the system and show which chemical the system is exposed to during training, whereas all four reporters (the latter and GFP and RFP) are active during the testing phase and show if the system is exposed to the same chemical as in training or not. <br />
More specifically, the YFP protein production is regulated with help of two operator sites controlled by cI and aTc. cI inhibits YFP production and aTc induces it. Therefore, YFP is produced when the system is exposed to aTc. In a similar manner, the CFP production is produced when the system is exposed to IPTG. <br />
The GFP production is regulated with help of two operator sites controlled by lacI and .</p><br><br />
<br />
== System Phases ==<br />
<br />
<p>The system operation is divided into two main phases: the training phase and the testing phase. The training phase itself is also subdivided into two phases: seeing and memorizing. During seeing, the system is first exposed to one of the two chemicals it is designed to recognize (aTc and IPTG). AHL is then added and the system’s internal toggle switch reaches a steady state. During memorizing, the chemical used during seeing is removed and only AHL is retained. This maintains the toggle switch to its acquired steady state, which is reported with YFP (if aTc was seen) or CFP (if IPTG was seen). During the testing phase, the system is exposed to any of the two chemicals (aTc or IPTG), with AHL present. By comparing its toggle switch state with the effect of the newly introduced chemical, the system shows a different response if it has previously been exposed to this chemical and reports with the same XFP as in the training phase (YFP for aTc, CFP for IPTG) or if it recognizes a different chemical and reports with a different XFP (GFP for trained with aTc and recognizing IPTG, RFP for trained with IPTG and recognizing aTc).The following table presents all possible paths that may be taken by the system during all phases of operation according to the external stimuli. </p><br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:center; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;" <br />
|+ '''System phases''' <br />
! <br />
!width="44" style="background:#446084; color:white"| aTc<br />
!width="44" style="background:#446084; color:white"| IPTG<br />
!width="44" style="background:#446084; color:white"| AHL<br />
!width="44" style="background:#446084; color:white"| p22cII<br />
!width="44" style="background:#446084; color:white"| cI<br />
! style="background:#446084; color:white"| Reporting <br />
|- <br />
|colspan="7" style="background:#96c9cf;" align="center"|'''Start''' <br />
|- <br />
| no input<br />
| no<br />
| no<br />
| no<br />
| no<br />
| no<br />
| no<br />
|- <br />
| colspan="7" style="background:#96c9cf;" align="center"| '''Learning'''<br />
|- <br />
| Trained with aTc<br />
| yes<br />
| no<br />
| no<br />
| yes<br />
| no<br />
| YFP<br />
|- <br />
| Trained with IPTG<br />
| no<br />
| yes<br />
| no<br />
| no<br />
| yes<br />
| CFP<br />
|- <br />
| colspan="7" style="background:#96c9cf;" align="center"| '''Memorizing'''<br />
|- <br />
| Trained with aTc<br />
| yes<br />
| no<br />
| yes<br />
| yes<br />
| no<br />
| YFP<br />
|- <br />
| Trained with IPTG<br />
| no<br />
| yes<br />
| yes<br />
| no<br />
| yes<br />
| CFP<br />
|- <br />
| colspan="7" style="background:#96c9cf;" align="center"| '''Recognition'''<br />
|-<br />
| Trained with aTc<br>Tested with aTc<br />
| yes<br />
| no<br />
| yes<br />
| yes<br />
| no<br />
| YFP<br />
|- <br />
| Trained with aTc<br>Tested with IPTG<br />
| no<br />
| yes<br />
| yes<br />
| yes<br />
| no<br />
| GFP<br />
|- <br />
| Trained with IPTG<br>Tested with IPTG<br />
| no<br />
| yes<br />
| yes<br />
| no<br />
| yes<br />
| CFP<br />
|- <br />
| Trained with IPTG<br>Tested with aTc<br />
| yes<br />
| no<br />
| yes<br />
| no<br />
| yes<br />
| RFP<br />
|- <br />
<br />
|}<br />
<br />
== System Parts ==<br />
<br />
<p>educatETH <i>E.coli</i> consists of 11 parts that can be synthesized independently (want to know how this is done in the lab? Then visit our [https://2007.igem.org/ETHZ/Biology/Lab In the Lab] page!) Please note that four of them (4,5 and 8,9) form together two functional system units. They have been separated to ensure comparable part lengths and thus enable easier introduction into plasmids. A detailed list of the used [[ETHZ/Biology/parts | parts can be found here]] (due to many pictures, it takes some time to load).</p><br><br />
<br />
<br />
<br />
== References ==<br />
<p><br />
[http://www.nature.com/nature/journal/v403/n6767/abs/403335a0.html &#91;1&#93; Elowitz MB and Leibler S] <i>"A synthetic oscillatory network of transcriptional regulators"</i>, Nature 403:335-338, 2000<br /><br />
<br />
== To Do ==<br />
<br />
=== New ===<br />
<p><ul><br />
<li> <span style=color:blue;>Update and correct parts in parts list. Write better in a table</span><br />
<li> <span style=color:blue;>Update and correct full system scheme</span><br />
<li> <span style=color:red;>What is the proof of concept mentioned?</span><br />
<li> <span style=color:red;>Are you sure about the reporters? This is not what I understood. I though YFP and CFP were also during testing. Yes! I'm still updating the text :-)</span><br />
<li> <span style=color:red;>I don't think it is wise to make p22cII in P22 cII, since we have in the first way all over the place. Stefan: The problem is that the correct notation is P22 cII and I already used it in all the part describtions. But I could also change it back, no worries!</span><br />
<li> <span style=color:red;>Try to improve table with system phases. It doesn't look so nice...</span><br />
<li> <span style=color:red;>In the memorizing phase, is there color or not? - Nope, there is no color - There are colors, however they will disappear within 2-3 hours.</span><br />
<li> <span style=color:red;>We need to put the complete spaghetti system as well.</span><br />
<li> Check my terminology (operator sites etc)<br />
<li> Put Stefan's updated part on epigenetics<br />
<li> Fill in table completely, make it more reading-friendly<br />
</ul></p><br></div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/SimulationETHZ/Simulation2007-10-23T08:18:13Z<p>LiNan: Sensitivity Eq modification</p>
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<br />
=Introduction=<br />
<br />
As has been presented in the [[ETHZ/Model | Modeling]] page, we have created a model of our system that can be described as a finite state machine. In order to examine the behavior of our system thoroughly, we have to simulate it through all the difference phases that can be reached. We carry out our simulations based on the protocol presented in Fig. 1.<br />
<br />
[[Image:ETHzFlowdiagram2.png|center|thumb|<b>Fig. 1</b>: Flow diagram. This figure shows the protocol with which the final system should be tested, as well as the test results in the form of the reported colors. There are three phases the system has to go through: (1) a training or learning phase in which the system learns an input and stores it in its memory, (2) a memory phase in which the system has to keep the content of its memory and, (3) a recognition phase where the output of the system depends on the content of its memory as well as on the current input. |500px]]<br />
<br />
== Simulation of Test Cases ==<br />
To test all the possible conditions and transitions in our system, we run timing simulations for all 4 different branches in the flow diagram of our protocol, and we plot the concentrations of the inducers, memory proteins and reporter proteins. During each learning, memorizing and recognizing phase, we wait for the steady state of the system to be reached. The three phases of the system are thus separated sufficiently in time. In all the cases, we follow the following procedure:<br />
* During the first 1000 minutes of the simulation, we do not add any inducers to the system, and as a result, we check for the baseline production of proteins.<br />
* Between 1000 and 2000 minutes, we add the inducer that should be learned, and we let the system reach steady state.<br />
* After 2000 minutes, we add the inducer AHL for memory formation, and run the simulation for an additional 1000 minutes, to reach again steady state.<br />
* Between 3000 and 4000 minutes, we test the final behavior of the system, and check if it reacts in the desired way, by showing the appropriate color.<br />
<br />
The parameters of the system are crucial if one wants to have accurate and realistic simulations. We present the parameters used to simulate our system, in the section [[ETHZ/Parameters | Parameters]].<br />
<br />
<p>[[Image:ETHZTest1correct.png|left|thumb|<b>Fig. 2</b>: Test case 1 - Learn and recognize IPTG. The simulated behavior of the system, when IPTG is presented both during the learning phase, and the recognition phase. In both the learning and recognition phase, the system reports by producing CFP, which matches the desired behavior. |420px]]<br />
[[Image:ETHZTest2correct.png|thumb|<b>Fig. 3</b>: Test case 2 - Learn IPTG and get tested for aTc. The simulated behavior of the system, when IPTG is presented during the learning phase, and aTc is presented during the recognition phase. The system reports by producing RFP during the recognition phase, which matches the desired behavior. |420px]]<br />
<br />
[[Image:ETHZTest3correct.png|left|thumb|<b>Fig. 4</b>: Test case 3 - Learn and recognize aTc. The simulated behavior of the system, when aTc is presented both during the learning phase and the recognition phase. In both the learning and recognition phase the system reports by producing YFP, which matches the desired behavior. |420px]]<br />
<br />
[[Image:ETHZTest4correct.png|right|thumb|<b>Fig. 5</b>: Test case 4 - Learn aTc and get tested for IPTG. The simulated behavior of the system, when aTc is presented during the learning phase, and IPTG is presented during the recognition phase. The system reports by producing GFP during the recognition phase, which matches the desired behavior. |420px]]</p><br />
<br />
==Sensitivity Analysis==<br />
<br />
In order for one to ensure a biological system will operate as it was modeled, he/she should understand the sensitivity of the system's behavior to all involved parameters. Due to the uncertainty in parameter retrieval, we would like our system to have reduced sensitivity to the parameters that are affected mostly by noise. That way, we can have a guaranteed stable performance, even under non-simulated conditions.<br />
<br />
[[Image:ETHZSensitivity.png|right|thumb|<b>Fig. 5</b>: Sensitivity analysis |420px]]<br />
<br />
We simulated the behavior of our system throughout all its phases, while sweeping the parameters by 5%, one at a time. More specifically for the case presented in Fig. 1, we carried out the following procedure:<br />
For each parameter, we did the timing simulation as shown in Fig. 2. Once we increased the parameter by 5%, and once we decreased the parameter by 5%. Since in test case 1 (Fig. 2) we are interested in having a high concentration of CFP during the recognition phase with respect to the base line, we divide the steady state CFP concentration at 4000 minutes by the baseline concentration at 1000 minutes. Finally, as shown in the following equation, we define the sensitivity by subtracting the resulting factor that we get with an increased parameter, from the resulting factor that we get by a decreased parameter.<br />
<br />
[[Image:ETHZSensititivityequation.png|center|400px]]<br />
<br />
<!---<math>Sensitivity = \left | \frac{[ CFP^{105%}_{recognition} ] }{\left [ CFP^{105%}_{baseline}\right ] }- \frac{\left [ CFP^{95%}_{recognition} \right ] }{\left [ CFP^{95%}_{baseline} \right ] }\right |</math>---><br />
<br />
We can summarize that, if there is no difference in the concentration of the desired output during the recognition phase with respect to baseline when sweeping a parameter by +/-5%, the sensitivity of that parameter is equal to zero. Otherwise the sensitivity gives us a measure of how much the concentration changes.<br />
<br />
We quantified the sensitivity for all parameters and test cases. The results are plotted in Fig. 5.<br />
<br />
The conclusions that can be derived from our sensitivity analysis are the following:<br />
* Our system is most sensitive to the parameter "a", which describes the base production. This is not a surprising result, since we depend on having a good switch, and our signal-to-noise ratio, or output to baseline production level, depends highly on the fact that we can reliably switch off the production of the memory proteins cI and p22cII. For a more detailed analysis of the switching behavior please see our [[ETHZ/Biology | biological implementation]] page.<br />
* Other sensitive parameters are those that are directly related to the production and decay of the memory proteins cI and p22cII. This can be explained by the fact that our system prefers symmetry between the parameter sets of cI and p22cII, in order to support good switching behavior where the same concentration of p22cII leads to the same repression of cI and vice versa. We can especially see that those parameters seem to be more sensitive for the test cases 1 and 2, where cI has to be stored inside the memory. The reason for this behavior is due to the missing symmetry between the parameters for cI and p22cII, we currently have a bias inside the system towards the production of p22cII.</div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/SimulationETHZ/Simulation2007-10-23T00:57:05Z<p>LiNan: /* Introduction */</p>
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=Introduction=<br />
<br />
As has been presented in the [[ETHZ/Model | Modeling]] page, we have created a model of our system that can be described as a finite state machine. In order to examine the behavior of our system thoroughly, we have to simulate it through all the difference phases that can be reached. We carry out our simulations based on the protocol presented in Fig. 1.<br />
<br />
[[Image:ETHzFlowdiagram2.png|center|thumb|<b>Fig. 1</b>: Flow diagram. This figure shows the protocol with which the final system should be tested, as well as the test results in the form of the reported colors. There are three phases the system has to go through: (1) a training or learning phase in which the system learns an input and stores it in its memory, (2) a memory phase in which the system has to keep the content of its memory and, (3) a recognition phase where the output of the system depends on the content of its memory as well as on the current input. |500px]]<br />
<br />
== Simulation of Test Cases ==<br />
To test all the possible conditions and transitions in our system, we run timing simulations for all 4 different branches in the flow diagram of our protocol, and we plot the concentrations of the inducers, memory proteins and reporter proteins. During each learning, memorizing and recognizing phase, we wait for the steady state of the system to be reached. The three phases of the system are thus separated sufficiently in time. In all the cases, we follow the following procedure:<br />
* During the first 1000 minutes of the simulation, we do not add any inducers to the system, and as a result, we check for the baseline production of proteins.<br />
* Between 1000 and 2000 minutes, we add the inducer that should be learned, and we let the system reach steady state.<br />
* After 2000 minutes, we add the inducer AHL for memory formation, and run the simulation for an additional 1000 minutes, to reach again steady state.<br />
* Between 3000 and 4000 minutes, we test the final behavior of the system, and check if it reacts in the desired way, by showing the appropriate color.<br />
<br />
The parameters of the system are crucial if one wants to have accurate and realistic simulations. We present the parameters used to simulate our system, in the section [[ETHZ/Parameters | Parameters]].<br />
<br />
<p>[[Image:ETHZTest1correct.png|left|thumb|<b>Fig. 2</b>: Test case 1 - Learn and recognize IPTG. The simulated behavior of the system, when IPTG is presented both during the learning phase, and the recognition phase. In both the learning and recognition phase, the system reports by producing CFP, which matches the desired behavior. |420px]]<br />
[[Image:ETHZTest2correct.png|thumb|<b>Fig. 3</b>: Test case 2 - Learn IPTG and get tested for aTc. The simulated behavior of the system, when IPTG is presented during the learning phase, and aTc is presented during the recognition phase. The system reports by producing RFP during the recognition phase, which matches the desired behavior. |420px]]<br />
<br />
[[Image:ETHZTest3correct.png|left|thumb|<b>Fig. 4</b>: Test case 3 - Learn and recognize aTc. The simulated behavior of the system, when aTc is presented both during the learning phase and the recognition phase. In both the learning and recognition phase the system reports by producing YFP, which matches the desired behavior. |420px]]<br />
<br />
[[Image:ETHZTest4correct.png|right|thumb|<b>Fig. 5</b>: Test case 4 - Learn aTc and get tested for IPTG. The simulated behavior of the system, when aTc is presented during the learning phase, and IPTG is presented during the recognition phase. The system reports by producing GFP during the recognition phase, which matches the desired behavior. |420px]]</p><br />
<br />
==Sensitivity Analysis==<br />
<br />
In order for one to ensure a biological system will operate as it was modeled, he/she should understand the sensitivity of the system's behavior to all involved parameters. Due to the uncertainty in parameter retrieval, we would like our system to have reduced sensitivity to the parameters that are affected mostly by noise. That way, we can have a guaranteed stable performance, even under non-simulated conditions.<br />
<br />
[[Image:ETHZSensitivity.png|right|thumb|<b>Fig. 5</b>: Sensitivity analysis |420px]]<br />
<br />
We simulated the behavior of our system throughout all its phases, while sweeping the parameters by 5%, one at a time. More specifically for the case presented in Fig. 1, we carried out the following procedure:<br />
For each parameter, we did the timing simulation as shown in Fig. 2. Once we increased the parameter by 5%, and once we decreased the parameter by 5%. Since in test case 1 (Fig. 2) we are interested in having a high concentration of CFP during the recognition phase with respect to the base line, we divide the steady state CFP concentration at 4000 minutes by the baseline concentration at 1000 minutes. Finally, as shown in the following equation, we define the sensitivity by subtracting the resulting factor that we get with an increased parameter, from the resulting factor that we get by a decreased parameter.<br />
<br />
[[Image:ETHZSensititivityequation.png|center|320px]]<br />
<br />
We can summarize that, if there is no difference in the concentration of the desired output during the recognition phase with respect to baseline when sweeping a parameter by +/-5%, the sensitivity of that parameter is equal to zero. Otherwise the sensitivity gives us a measure of how much the concentration changes.<br />
<br />
We quantified the sensitivity for all parameters and test cases. The results are plotted in Fig. 5.<br />
<br />
The conclusions that can be derived from our sensitivity analysis are the following:<br />
* Our system is most sensitive to the parameter "a", which describes the base production. This is not a surprising result, since we depend on having a good switch, and our signal-to-noise ratio, or output to baseline production level, depends highly on the fact that we can reliably switch off the production of the memory proteins cI and p22cII. For a more detailed analysis of the switching behavior please see our [[ETHZ/Biology | biological implementation]] page.<br />
* Other sensitive parameters are those that are directly related to the production and decay of the memory proteins cI and p22cII. This can be explained by the fact that our system prefers symmetry between the parameter sets of cI and p22cII, in order to support good switching behavior where the same concentration of p22cII leads to the same repression of cI and vice versa. We can especially see that those parameters seem to be more sensitive for the test cases 1 and 2, where cI has to be stored inside the memory. The reason for this behavior is due to the missing symmetry between the parameters for cI and p22cII, we currently have a bias inside the system towards the production of p22cII.</div>LiNanhttp://2007.igem.org/wiki/index.php/ETHZ/BiologyETHZ/Biology2007-10-22T14:26:26Z<p>LiNan: Kill "Engineering Perspective" link</p>
<hr />
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<br />
<center>[[ETHZ | Main Page]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Model | System Modeling]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Simulation | Simulations]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Biology | System Implementation]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Biology/Lab | Lab Notes]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Meet_the_team | Meet the Team]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Internal | Team Notes]] &nbsp;&nbsp;&nbsp;&nbsp; [[ETHZ/Pictures | Pictures!]]</center><br><br />
<br />
<center><font size = '+1'><b> .:: educatETH <i>E.coli</i> - Biology Perspective ::. </b></font></center><br><br />
<br />
<p>In this page, you can find an analysis of the function of our system, its biological design, and a list of the parts that it consists of. Are you interested in constructing educatETH <i>E.coli</i> in your lab? Then under [https://2007.igem.org/ETHZ/Biology/Lab Lab Notes], you can find the ingredients and equipment we used, the electronic version of our lab notebook and a presentation of all the difficulties that we encountered. If you are also interested in how educatETH ''E.coli'' was modeled and simulated outside the lab, please visit the [[ETHZ/Model | modeling]] and [[ETHZ/Simulation | simulations]] web pages. </p><br />
<br />
__NOTOC__<br />
<br />
= Introduction =<br />
<br />
<p> educatETH <i>E.coli</i> is a system which can distinguish between [http://openwetware.org/wiki/ATc anhydrotetracycline (aTc)] and [http://openwetware.org/wiki/IPTG Isopropyl-beta-D-thiogalactopyranoside (IPTG)] based on a previous training phase conducted with the same chemicals and the help of [http://partsregistry.org/Acyl-HSLs AHL]. It is composed of three subsystems: the subsystem of constitutively produced proteins, the learning subsystem and the reporting subsystem. The constitutively produced proteins (LacI, TetR and LuxR) control the learning subsystem. At the core of the latter there exists a modified and extended version of the Elowitz toggle switch found in [1]. The main difference is reflected in the use of double promoters, so that the toggle switch only changes its state when both, one of the two chemicals (aTc/IPTG), and AHL are present. As AHL is only present during the training phase, the toggle maintains its state during testing, and thus can “memorize”. AHL can therefore be seen as a training- or learning substance. In the reporting subsystem, four reporters ([http://partsregistry.org/Featured_Parts:Fluorescent_proteins fluorescent proteins]) allow supervision of both the chemical the system was trained with and of if the system recognizes the chemical it is being exposed to in the testing phase as one it has been trained with or not.</p><br><br />
<br />
== The complete system ==<br />
<br />
<p>The biological design of educatETH <i>E.coli</i> is presented in Fig. 1 [[Image:Biol_system_stand20.10.png|thumb|left|300px|'''Fig. 1:''' Gene interaction network of educatETH ''E.coli'' ]] and in the following, we will clarify the function of all depicted components. (Are you interested in how the complex system of Fig. 1 was modeled? Then visit the [[ETHZ/Model| System Modeling]]!)</p><br><br />
<br />
==== Constitutive subsystem ====<br />
<br />
<p>The constitutively produced proteins of the system are LacI, TetR and LuxR. The LuxR part has a special function: when AHL is present, it forms a LuxR-AHL complex which acts on the learning subsystem (more on this later). At the moment, we will consider that AHL is absent and therefore LuxR cannot act on any subsystems. The TetR and LacI parts behave similarly: more specifically, the TetR protein in the absence of aTc inhibits the production of P22 cII and LacI in the absence of IPTG inhibits the production of cI. When aTc is present, however, the P22 cII production is no longer inhibited (and thus p22 cII is produced). Respectively, cI is produced when IPTG is present.</p><br />
<br />
==== Learning subsystem ====<br />
<br />
<p>The learning subsystem is a toggle switch with two operator sites. The upper part of the toggle (cI production) has operator sites for the LuxR-AHL complex and P22 cII (which has in turn been induced by aTc). The LuxR-AHL complex induces the cI production, whereas P22 cII inhibits it. The lower part of the toggle (P22 cII production) has operator sites for the LuxR-AHL complex and cI (which has been induced by IPTG). Similarly with the upper part, the LuxR-AHL complex induces the P22 cII production and cI inhibits it. Therefore, the switch always requires the presence of the LuxR-AHL complex in order for it to operate. Its state depends on the presence of P22 cII and cI into the system, which in return was caused through the exposure of the system to aTc and IPTG.</p><br />
<br />
==== Reporting subsystem ====<br />
<br />
<p>There are four reporters in the system. CFP (more precisely: enhanced CFP, that is eCFP) and YFP (more precisely: enhanced YFP, that is eYFP) are active during the training phase of the system and show which chemical the system is exposed to during training, whereas all four reporters (the latter and GFP and RFP) are active during the testing phase and show if the system is exposed to the same chemical as in training or not. <br />
More specifically, the YFP protein production is regulated with help of two operator sites controlled by cI and aTc. cI inhibits YFP production and aTc induces it. Therefore, YFP is produced when the system is exposed to aTc. In a similar manner, the CFP production is produced when the system is exposed to IPTG. <br />
The GFP production is regulated with help of two operator sites controlled by lacI and .</p><br><br />
<br />
== System phases ==<br />
<br />
<p>The system operation is divided into two main phases: the training phase and the testing phase. The training phase itself is also subdivided into two phases: seeing and memorizing. During seeing, the system is first exposed to one of the two chemicals it is designed to recognize (aTc and IPTG). AHL is then added and the system’s internal toggle switch reaches a steady state. During memorizing, the chemical used during seeing is removed and only AHL is retained. This maintains the toggle switch to its acquired steady state, which is reported with YFP (if aTc was seen) or CFP (if IPTG was seen). During the testing phase, the system is exposed to any of the two chemicals (aTc or IPTG), with AHL present. By comparing its toggle switch state with the effect of the newly introduced chemical, the system shows a different response if it has previously been exposed to this chemical and reports with the same XFP as in the training phase (YFP for aTc, CFP for IPTG) or if it recognizes a different chemical and reports with a different XFP (GFP for trained with aTc and recognizing IPTG, RFP for trained with IPTG and recognizing aTc).The following table presents all possible paths that may be taken by the system during all phases of operation according to the external stimuli. </p><br />
<br />
{| class="wikitable" border="1" cellspacing="0" cellpadding="2" style="text-align:center; margin: 1em 1em 1em 0; background: #f9f9f9; border: 1px #aaa solid; border-collapse: collapse;" <br />
|+ '''System phases''' <br />
! <br />
!width="44" style="background:#446084; color:white"| aTc<br />
!width="44" style="background:#446084; color:white"| IPTG<br />
!width="44" style="background:#446084; color:white"| AHL<br />
!width="44" style="background:#446084; color:white"| p22cII<br />
!width="44" style="background:#446084; color:white"| cI<br />
! style="background:#446084; color:white"| Reporting <br />
|- <br />
|colspan="7" style="background:#96c9cf;" align="center"|'''Start''' <br />
|- <br />
| no input<br />
| no<br />
| no<br />
| no<br />
| no<br />
| no<br />
| no<br />
|- <br />
| colspan="7" style="background:#96c9cf;" align="center"| '''Learning'''<br />
|- <br />
| Trained with aTc<br />
| yes<br />
| no<br />
| no<br />
| yes<br />
| no<br />
| YFP<br />
|- <br />
| Trained with IPTG<br />
| no<br />
| yes<br />
| no<br />
| no<br />
| yes<br />
| CFP<br />
|- <br />
| colspan="7" style="background:#96c9cf;" align="center"| '''Memorizing'''<br />
|- <br />
| Trained with aTc<br />
| yes<br />
| no<br />
| yes<br />
| yes<br />
| no<br />
| YFP<br />
|- <br />
| Trained with IPTG<br />
| no<br />
| yes<br />
| yes<br />
| no<br />
| yes<br />
| CFP<br />
|- <br />
| colspan="7" style="background:#96c9cf;" align="center"| '''Recognition'''<br />
|-<br />
| Trained with aTc<br>Tested with aTc<br />
| yes<br />
| no<br />
| yes<br />
| yes<br />
| no<br />
| YFP<br />
|- <br />
| Trained with aTc<br>Tested with IPTG<br />
| no<br />
| yes<br />
| yes<br />
| yes<br />
| no<br />
| GFP<br />
|- <br />
| Trained with IPTG<br>Tested with IPTG<br />
| no<br />
| yes<br />
| yes<br />
| no<br />
| yes<br />
| CFP<br />
|- <br />
| Trained with IPTG<br>Tested with aTc<br />
| yes<br />
| no<br />
| yes<br />
| no<br />
| yes<br />
| RFP<br />
|- <br />
<br />
|}<br />
<br />
<br />
== System parts ==<br />
<br />
<p>educatETH <i>E.coli</i> consists of 11 parts that can be synthesized independently (want to know how this is done in the lab? Then visit our [https://2007.igem.org/ETHZ/Biology/Lab In the Lab] page!) Please note that four of them (4,5 and 8,9) form together two functional system units. They have been separated to ensure comparable part lengths and thus enable easier introduction into plasmids. A detailed list of the used [[ETHZ/Biology/parts | parts can be found here]] (due to many pictures, it takes some time to load).</p><br><br />
<br />
== Applications ==<br />
<br />
<p>Besides being a nice model for describing learning behavior or epigenetics, our system-structure can also be used for potent real world applications:</p><br />
<br />
==== Multipurpose Cell Lines ====<br />
<br />
<p>Our system can be trained to behave in a specific way by setting its inducible toggle switch to one of its two states. Let’s say this specific states trigger specific and different events such as enzyme synthesis, transcriptional regulation, virion production or cell death. In this case one can view the bacterial cell line containing this system as a multipurpose cell line. Put another way: One has a cell line and adds a certain chemical to train it to the desired behavior instead of constructing two independent cell lines. This means, one applies an “input engineering” instead of a “DNA engineering” approach. If one extends this idea to several inducible toggle switches being harbored in the same cell line, the number of possible phenotypes increases to 2<sup>n</sup>, whereas n equals the number of toggle switches. For example, if one would have 5 toggle switches inside a cell line, 32 different behavior patterns would be possible.</p><br />
<br />
==== Intelligent Biosensors and Self-Adaptation ====<br />
<br />
<p>As a direct application of the multipurpose cell lines mentioned above, we constructed a system capable of sensing different chemicals and producing different fluorescent proteins. Since the cells can be trained to produce one of several specific fluorescent protein types when a certain chemical is present, one can also view those cells as intelligent biosensors, able to change their properties in a training phase.<br> It can also be possible that the environment (and its chemicals) itself is the training phase and hence that the biosensors are adapting themselves to this environment and hence the desired state.<br> Eventually, the intelligent biosensors are not limited to dedect chemicals. Also temperature, pH or light etc. could be detected with an appropriate system.</p><br><br />
<br />
== References ==<br />
<p><br />
[http://www.nature.com/nature/journal/v403/n6767/abs/403335a0.html &#91;1&#93; Elowitz MB and Leibler S] <i>"A synthetic oscillatory network of transcriptional regulators"</i>, Nature 403:335-338, 2000<br /><br />
<br />
== To Do ==<br />
<br />
=== New ===<br />
<p><ul><br />
<li> <span style=color:blue;>Update and correct parts in parts list. Write better in a table</span><br />
<li> <span style=color:blue;>Update and correct full system scheme</span><br />
<li> <span style=color:red;>What is the proof of concept mentioned?</span><br />
<li> <span style=color:red;>Are you sure about the reporters? This is not what I understood. I though YFP and CFP were also during testing. Yes! I'm still updating the text :-)</span><br />
<li> <span style=color:red;>I don't think it is wise to make p22cII in P22 cII, since we have in the first way all over the place. Stefan: The problem is that the correct notation is P22 cII and I already used it in all the part describtions. But I could also change it back, no worries!</span><br />
<li> <span style=color:red;>Try to improve table with system phases. It doesn't look so nice...</span><br />
<li> <span style=color:red;>In the memorizing phase, is there color or not? - Nope, there is no color - There are colors, however they will disappear within 2-3 hours.</span><br />
<li> <span style=color:red;>We need to put the complete spaghetti system as well.</span><br />
<li> Check my terminology (operator sites etc)<br />
<li> Put Stefan's updated part on epigenetics<br />
<li> Fill in table completely, make it more reading-friendly<br />
</ul></p><br></div>LiNan