Naples

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            [[Image:NAPOLI_ariprova.jpg|center|420px]]
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== '''University of Naples "Federico II"''' ==
+
 +
                                                                                                         
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                            [[Image:UniversitàFedericoII.jpg]]
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== '''About Us''' ==
 +
[[Image:TUTTI3.jpg]][[Image:capi2.jpg]]                                                     
 +
 +
'''Students''':
 +
 +
*[[Giovanni Russo]]                                                         
 +
*[[Lucia Marucci]]
 +
*[[Velia Siciliano]]
 +
*[[Irene Cantone]]                                                                   
 +
*[[Roberta Bergamasco]][https://2007.igem.org/User_talk:Robertina]                           
 +
*[[Maria Aurelia Ricci]]                                             
 +
*[[Mafalda Graziano]]
 +
 +
'''Instructors''' 
 +
 +
*Diego di Bernardo
 +
*Maria Pia Cosma
 +
*Mario di Bernardo
 +
 +
'''Advisor'''
 +
 +
*[[Giulia Cuccato]]
 +
 +
 +
[[more pictures]]
 +
 +
[[images from Naples]]
----
----
-
== '''Tigem''' ==
+
== '''Tigem'''==
-
        [[Image:Tigem.jpg]]
+
-
The Telethon Institute of Genetics and Medicine (TIGEM) was created by the Italian Telethon Foundation in 1994. TIGEM mission is the understanding of the pathogenic mechanisms of genetic diseases with the aim of developing preventive and therapeutic strategies.TIGEM currently hosts 17 research groups, and a total of more than 120 persons, including students, postdoctoral fellows, staff scientists, technicians, and administrators and offers training programs in medical human genetics and Synthetic Biology in cooperation with the University of Naples Federico II.
+
[[Image:Tigem.jpg|left|460px]] The Telethon Institute of Genetics and Medicine (TIGEM)[http://www.tigem.it]
 +
was created by the Italian Telethon Foundation in 1994. TIGEM's purpose is understanding pathogenic mechanisms of genetic diseases. The final aim is developing preventive and therapeutic strategies. The research centre currently hosts 17 research groups, and a total of more than 120 people, including students, postdoctoral fellows, technicians, and administrators. It offers training programs in human medical genetics and Synthetic Biology in collaboration with the University of Naples Federico II.
-
== About Us ==  [[Image:flaskshaker.gif]]                                                                   
 
-
*Students:
+
----
-
**Giovanni Russo                                                         
+
 
-
**Lucia Marucci
+
== '''University of Naples "Federico II"'''== 
-
**Velia Siciliano
+
[[Image:Uni.jpg|left|110px]]
-
**Irene Cantone                                                                   
+
 
-
**Roberta Bergamasco                           
+
 
-
**Maria Aurelia Ricci                                             
+
-
**Mafalda Graziano
+
University of '''Naples''' "Federico II"[http://www.international.unina.it/] was founded by the King of "Sacro Romano Impero" Federico II of Svevia. It is considered one of the oldest University of Europe. It consists of 13 departments divided in three areas: Sciences and Technologies, Humanistic and Social, Medicine.
 +
 
 +
 
 +
 
 +
 
 +
----
 +
 
 +
=='''Our Project - [[YeSOil]]: A Yeast Sensor for real Extra Virgin Olive oil'''==               
 +
[[image:logo2.jpg|center|160px]]
 +
                                       
 +
The aim of our project is to engineer a synthetic biological network in yeast. This system will help in evaluating the quality of olive oil, one of the wordly famous product of Italy [http://en.wikipedia.org/wiki/Italy]. Detection of oil quality is now possible only through expensive and bulky machines. In order to render this process easy and cheap we will modify Saccharomyces cerevisiae cells so that they will act as sensors and indicators of different oleate concentrations.
 +
 
 +
 
 +
 
 +
 
 +
==='''System Model''' === 
 +
 
 +
After some brainstorming we had this idea!!!
 +
 +
[[image:Circuito3.jpg|centre|thumb|580px|YeSOil circuit]]
 +
 
 +
The whole circuit is based on the reaction of the transcription factor for PHO4p which is activated when there is a low oleic acid concentration,
 +
i.e. ''extra virgin olive oil'', while PHO80 gene is activated when the oleic acid concentration is high, i.e. ''not edible oil''.
 +
When PHO4p is activated PHO8, which is integrated with GFP, is expressed: cells turn green indicating that the oil is ''extra virgin''. When PHO80 is transcribed by the ''not edible oil promoter'' it creates a complex with PHO85: PHO80-PHO85. PHO80PHO85 phosforilates PHO4p inhibiting the trascription of PHO8. As PHO80 is integrated with RFP, when it is expressed cells turn red, indicating that the oil is not edible. When the level of oleic acid concentration is between extra virgin and not edible, the output will be a mix of green and red fluorescence: yellow-orange.
 +
 
 +
The input of the system will be the level of oleic acid that will drive expression from appropriate promoters responsive to oleic acid cloned upstream of Pho80Pho85 and Pho4.
 +
 
 +
                 
 +
       
 +
 
 +
 
 +
 
 +
We recall that, for 100 gr of the oil, oil will be classified as:
 +
*''extra virgin'', if the oleic acid conentration is less than 0.8 gr
 +
*''virgin'', if oleic the acid concentration is less than 2 gr
 +
*''not edible'', if the oleic acid concentration is greater than 3-4 gr
 +
 
 +
 
 +
Now, we need to convert gr in mol and we found that:
 +
 
 +
*the oil is ''extra virgin'' if the oleic acid concentration is less than 2.8 mM
 +
*the oil is ''virgin'' if the oleic acid concentration is less than 7.1 mM
 +
*the oil is ''not edible'' if the oleic acid concentration is greater than 7.1 mM
 +
 
 +
 
 +
 
 +
 
 +
[[Image:tfOre.jpg|right|420px]]
-
*Instructors 
 
-
**Diego di Bernardo
 
-
**Maria Pia Cosma
 
-
**Mario di Bernardo
 
-
*Advisor
 
-
**Giulia Cuccato
 
-
==Our Project==
 
-
The aim of our project is to engineer a synthetic biological network and modifying Saccharomyces cerevisiae cells so that we would be able to change colour at differents oleate concentrations.
 
-
                          [[Image:yeastRed.jpg]]        [[Image:SGFP.gif]]
 
Oleate is the principal olive oil element and acidity indicator.
Oleate is the principal olive oil element and acidity indicator.
-
The olive oil is defined "extra vergine" if it has an acidity lower than 0.8 %,"vergine" with an acidity lower than 2% and not commestible if has an acidity higher than 3%.
+
The olive oil is defined "extra vergine" if it has an acidity lower than 0.8 %,
 +
"vergine" with an acidity lower than 2% and not edible if has an acidity higher than 3%.[http://en.wikipedia.org/wiki/Olive_oil]
Oleate induces the transcription of genes involved in peroxisome biogenesis and stimulates the
Oleate induces the transcription of genes involved in peroxisome biogenesis and stimulates the
-
proliferation of these organelles in Saccharomyces cerevisiae. Fatty acid-mediated induction is based on a dramatic increase in transcription of several genes encoding peroxisomal functions due to the presence of an oleate response element (ORE) in their promoters.This upstream activating sequence is minimally defined by an inverted repeat of CGG triplets separated by a 15-18-nucleotide spacer. It constitutes the binding target for the transcription factors Oaf1p and Pip2p.
+
proliferation of these organelles in Saccharomyces cerevisiae.  
 +
Fatty acid-mediated induction is based on a dramatic increase in transcription of several genes encoding peroxisomal functions due to the presence of an oleate response element (ORE) in their promoters.This upstream activating sequence is minimally defined by an inverted repeat of CGG triplets separated by a 15-18-nucleotide spacer.  
 +
It constitutes the binding target for the transcription factors Oaf1p and Pip2p.
-
== Modelling ==
 
-
==== Introduction ====
 
-
Over the past decades progress in measurement of rates and interactions of molecular and cellular processes has initiated a revolution in understanding of dynamical phenomena in cells. Generally speaking a ''dynamical phenomenon'' is a process that changes over time. Living cells are inherently dynamic! Indeed, to sustain the characteristic features of life (growth, cell division...) they need to axtract and trasform energy from their surroundings. This implies that cells function thermodinamically as open ''systems''. So, we have encountered a new keyword: system. The most general definition for system is the following: ''a set of functional elements joint together to perform a specific task''. Cells are astoundingly complex systems: they contain networks of thousands of biochemical interactions.
 
-
System-level understanding, the approach of systems biology, requires a ''shift'' in the notion of ''what to look for''. An understanding of genes and proteins is very important, but now the focus is on understanding system' structure and dynamics. Biologists use ''cartoons'' to capture the complexity of the networks, but because a system is not just an assembly of genes and proteins, its properties cannot be fully understood by drawing these diagrams. They, of course, represent a first step in our modeling, but can be compared to ''static roadmap'', whereas what we really seek to know are the traffic patterns, why they energe, how to control them. So, we will use a typical approach from systems and control theory.
 
-
==== Basic assumptions ====
 
-
We realized a reaction network as a system of ODEs. Clearly we need to guess working hypothesis:
 
-
*Component concentrations don't vary with respect to time
 
-
Whether it is a good assumption depends on time and space scales. In a yeast cell molecular diffution is sufficiently fast to mix proteins in less than one minute
 
-
*Component concentrations are continuos functions of time
 
-
This is true if the number of molecules of each species in the reaction volume is sufficiently large
 
-
Other important assumptions are the following:
 
-
*Transcription factor timescales are much larger than protein-protein interactions timescales
 
-
*Input changes are very rapid
 
-
We will use this assumptions to simplify our modeling
 
-
ODEs are very useful to represent molecular interaction. Indeed, applying a simple set of rules we can represent arbitrarily complex reaction networks as a set of coupled ODEs!
 
-
==== Our model ====
 
-
After some ''brainstorming'' we found the ''best parts'' for our project. They are showed above:
 
-
          [[Image:igemmodeling1.jpg]]          [[Image:igemmodeling2.jpg]]
+
----
-
Notice that our network will respond to medium oleate concentrations (''vergine'' oil) and to high oleate concentrations (non commestible oil), while for small oleate concentrations (''extra vergine'' oil) it doesn't respond.
+
==='''Mathematical Model'''===
-
In the left picture the genes are represented; red link indicates a protein-protein interactions, while green link indicates a transcription factor interaction. It's clear that Pho80Pho85 inhibits Pho4, so if Pho80Pho85 is active then Pho8 is low even if Pho4 is active (design task). If Pho80Pho85 is inactive and Pho4 is active, then Pho8 becomes active. If Pho80Pho85 and Pho4 are inactive, than Pho8 is inactive. The inhibition of Pho4 by Pho80Pho85 is at protein-protein level: if Pho80Pho85 is active, Pho4 is phosphorylated and its concentration becomes rapidly small.
+
-
The ''outputs'' of the system will be Pho80Pho85 and Pho8 and we will associate them ''red'' or ''yellow'' lights!
+
-
==== Mathematical model ====
+
Over the past decades progress in measurement of rates and interactions of molecular and cellular processes has initiated a revolution in understanding of dynamical phenomena in cells. Generally speaking a ''dynamical phenomenon'' is a process that changes over time. Living cells are inherently dynamic! Indeed, to sustain the characteristic features of life (growth, cell division...) they need to extract and transform energy from their surroundings. This implies that cells function thermodinamically as open ''systems''. So, we have encountered a new keyword: system. The most general definition for system is the following: ''a set of functional elements joint together to perform a specific task''. Cells are astoundingly complex systems: they contain networks of thousands of biochemical interactions.
-
We modeled the transcriptions factor interactions as Hill-like functions, while the protein-protein interactions are represented through a typical ''prey-predator'' model.
+
System-level understanding, the approach of systems biology, requires a ''shift'' in the notion of ''what to look for''. An understanding of genes and proteins is very important, but now the focus is on understanding system' structure and dynamics. Biologists use ''cartoons'' to capture the complexity of the networks, but because a system is not just an assembly of genes and proteins, its properties cannot be fully understood by drawing these diagrams. They, of course, represent a first step in our modeling, but can be compared to ''static roadmap'', whereas what we really seek to know are the traffic patterns, why they emerge, how to control them. So, we will use a typical approach from systems and control theory.
-
All the hypotesis were used to get the ODE model.
+
-
For the transcription factor level we have the following equations:
+
-
                            [[Image:igemmodeling3.jpg]]
+
-
In the equations SA and SB indicates the inputs, i.e. if an oleate level is ''passed''. Notice that the second assumpion is used to obtain this equations form. [Pho4free]is the Pho4 concentration (''the output from protein-protein interaction'') that partecipates to transcription factor interaction. To get this value we write the equation for protein-protein interaction:
 
-
                            [[Image:igemmodeling4.jpg]]
 
-
Using the first assumption we can obtain an expression for [Pho4free] in terms of [Pho80Pho85] and [PhO4] using steady state solution. The third equation in the ODEs system will become:
+
[[Image:assunzioni.png|110px]]  [[Basic assumptions]]  [[Image:modello matematico.jpg|100px]]  [[Mathematical model]]  [[Image:ing.gif|100px]]    [[System analysis and simulations]]
-
                          [[Image:igemmodeling5.jpg]]
+
-
====System analysis and simulations====
 
-
The system was studied analytically and numerically. Both studies showed an inaspected structural stability property in the parameter range of physical interest. Indeed, we get an analytic expression for the Jacobian matrix (a diagonal matrix) whose determinant becomes 0 only if almost one of basical diluition factor becomes 0. After a careful study we found the reason of this behaviour in the modelling process, particularly in the use of the second assumption. Matlab, Mathematica and Matcont were used to complete the study with simulations and bifurcation diagrams that confirmed the previous analytic study.
 
-
== Yeast Strain ==
+
----
-
Yeast strain used is W303.
+
 
-
All DNA manipulations and subcloning were done in Escherichia coli.
+
==='''Yeast Strain''' ===
 +
Yeast strain used is [[W303]].
 +
 
 +
All DNA manipulations and subcloning were done in [[Escherichia coli]]
 +
 
 +
----
 +
 
 +
=== '''Materials & Methods''' ===
 +
We have adopted a strategy of parallel cloning.
 +
 
 +
A reporter gene is cloned in parallel into a vector containing one and two tandem copies of the oleate response elements from the FOX3 promoter.
 +
 
 +
'''Background'''
 +
 
 +
[[References]]
 +
 
 +
'''Cloning Strategies in E.coli'''
 +
*[[Biobrick Vector choice]]
 +
*[[Biobrick Restriction Enzyme]]                                                 
 +
*[[Biobrick Primers Design]]
 +
 
 +
 
 +
'''Cloning Strategies in S.cerevisiae'''
 +
*[[Vector choice]]
 +
*[[Restriction Enzyme]]
 +
*[[Primers Design]]
 +
 
 +
 
 +
 
 +
'''Yeast integration'''
 +
*[[Integration strategies]]
 +
*[[Primers Design For Yeast Integration]]
 +
 
 +
 
 +
 
 +
'''Cloning Process'''
 +
*[[Yeast DNA extraction]]
 +
*[[PCR]]
 +
*[[Agarose Gel Electrophoresis]]
 +
*[[PCR Purification]]
 +
*[[Digestion]]
 +
*[[Extraction from Gel]]
 +
*[[Ligation]]
 +
*[[E.coli Transformation]]
 +
*[[Mini and Midi prep]]
 +
*[[Transformation]]
 +
 
 +
 
 +
----
 +
 
 +
=== '''Experimental Results'''===
 +
*[[Luciferase assay]]
 +
*[[Cloning in BioBrick vectors]]
 +
 
 +
 
 +
 
 +
----
 +
 
 +
== '''Thanks to...''' ==
 +
 
 +
[[Image:Synbiocomm.jpg]]    [[Image:Bandiera_comunita_europea.jpg]]
-
== Materials & Methods ==
+
'''We are partly funded by the European Union SYNBIOCOMM project'''
-
We have adopted a strategy of parallel cloning .Two different reporter genes (luciferase and B-galattosidasy)are cloned in parallel into a vector containing four different promoters.
+
-
* Cloning Strategies
+
-
**Vector choice 
+
-
**Restriction Enzyme
+
-
**Primers Design
+

Latest revision as of 18:49, 25 October 2007

NAPOLI ariprova.jpg


Contents

About Us

TUTTI3.jpgCapi2.jpg

Students:

Instructors

  • Diego di Bernardo
  • Maria Pia Cosma
  • Mario di Bernardo

Advisor


more pictures

images from Naples

Tigem

Tigem.jpg
The Telethon Institute of Genetics and Medicine (TIGEM)[http://www.tigem.it]

was created by the Italian Telethon Foundation in 1994. TIGEM's purpose is understanding pathogenic mechanisms of genetic diseases. The final aim is developing preventive and therapeutic strategies. The research centre currently hosts 17 research groups, and a total of more than 120 people, including students, postdoctoral fellows, technicians, and administrators. It offers training programs in human medical genetics and Synthetic Biology in collaboration with the University of Naples Federico II.


University of Naples "Federico II"

Uni.jpg


University of Naples "Federico II"[http://www.international.unina.it/] was founded by the King of "Sacro Romano Impero" Federico II of Svevia. It is considered one of the oldest University of Europe. It consists of 13 departments divided in three areas: Sciences and Technologies, Humanistic and Social, Medicine.



Our Project - YeSOil: A Yeast Sensor for real Extra Virgin Olive oil

Logo2.jpg

The aim of our project is to engineer a synthetic biological network in yeast. This system will help in evaluating the quality of olive oil, one of the wordly famous product of Italy [http://en.wikipedia.org/wiki/Italy]. Detection of oil quality is now possible only through expensive and bulky machines. In order to render this process easy and cheap we will modify Saccharomyces cerevisiae cells so that they will act as sensors and indicators of different oleate concentrations.



System Model

After some brainstorming we had this idea!!!

YeSOil circuit

The whole circuit is based on the reaction of the transcription factor for PHO4p which is activated when there is a low oleic acid concentration, i.e. extra virgin olive oil, while PHO80 gene is activated when the oleic acid concentration is high, i.e. not edible oil. When PHO4p is activated PHO8, which is integrated with GFP, is expressed: cells turn green indicating that the oil is extra virgin. When PHO80 is transcribed by the not edible oil promoter it creates a complex with PHO85: PHO80-PHO85. PHO80PHO85 phosforilates PHO4p inhibiting the trascription of PHO8. As PHO80 is integrated with RFP, when it is expressed cells turn red, indicating that the oil is not edible. When the level of oleic acid concentration is between extra virgin and not edible, the output will be a mix of green and red fluorescence: yellow-orange.

The input of the system will be the level of oleic acid that will drive expression from appropriate promoters responsive to oleic acid cloned upstream of Pho80Pho85 and Pho4.




We recall that, for 100 gr of the oil, oil will be classified as:

  • extra virgin, if the oleic acid conentration is less than 0.8 gr
  • virgin, if oleic the acid concentration is less than 2 gr
  • not edible, if the oleic acid concentration is greater than 3-4 gr


Now, we need to convert gr in mol and we found that:

  • the oil is extra virgin if the oleic acid concentration is less than 2.8 mM
  • the oil is virgin if the oleic acid concentration is less than 7.1 mM
  • the oil is not edible if the oleic acid concentration is greater than 7.1 mM



TfOre.jpg


Oleate is the principal olive oil element and acidity indicator. The olive oil is defined "extra vergine" if it has an acidity lower than 0.8 %, "vergine" with an acidity lower than 2% and not edible if has an acidity higher than 3%.[http://en.wikipedia.org/wiki/Olive_oil] Oleate induces the transcription of genes involved in peroxisome biogenesis and stimulates the proliferation of these organelles in Saccharomyces cerevisiae. Fatty acid-mediated induction is based on a dramatic increase in transcription of several genes encoding peroxisomal functions due to the presence of an oleate response element (ORE) in their promoters.This upstream activating sequence is minimally defined by an inverted repeat of CGG triplets separated by a 15-18-nucleotide spacer. It constitutes the binding target for the transcription factors Oaf1p and Pip2p.




Mathematical Model

Over the past decades progress in measurement of rates and interactions of molecular and cellular processes has initiated a revolution in understanding of dynamical phenomena in cells. Generally speaking a dynamical phenomenon is a process that changes over time. Living cells are inherently dynamic! Indeed, to sustain the characteristic features of life (growth, cell division...) they need to extract and transform energy from their surroundings. This implies that cells function thermodinamically as open systems. So, we have encountered a new keyword: system. The most general definition for system is the following: a set of functional elements joint together to perform a specific task. Cells are astoundingly complex systems: they contain networks of thousands of biochemical interactions. System-level understanding, the approach of systems biology, requires a shift in the notion of what to look for. An understanding of genes and proteins is very important, but now the focus is on understanding system' structure and dynamics. Biologists use cartoons to capture the complexity of the networks, but because a system is not just an assembly of genes and proteins, its properties cannot be fully understood by drawing these diagrams. They, of course, represent a first step in our modeling, but can be compared to static roadmap, whereas what we really seek to know are the traffic patterns, why they emerge, how to control them. So, we will use a typical approach from systems and control theory.


Assunzioni.png Basic assumptions Modello matematico.jpg Mathematical model Ing.gif System analysis and simulations


Yeast Strain

Yeast strain used is W303.

All DNA manipulations and subcloning were done in Escherichia coli

Materials & Methods

We have adopted a strategy of parallel cloning.

A reporter gene is cloned in parallel into a vector containing one and two tandem copies of the oleate response elements from the FOX3 promoter.

Background

References

Cloning Strategies in E.coli


Cloning Strategies in S.cerevisiae


Yeast integration


Cloning Process


Experimental Results


Thanks to...

Synbiocomm.jpg Bandiera comunita europea.jpg

We are partly funded by the European Union SYNBIOCOMM project

Retrieved from "http://2007.igem.org/wiki/index.php/Naples"