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ETH Zurich - educatETH E.coli System


"All E.coli 's are equal, but some E.coli 's are more equal than others..." (freely adapted from "Animal Farm" by George Orwell)

... this is what George Orwell would have written, were he a synthetic biologist. In the E.coli colonies on petri dishes, all bacteria are equal; except for some special ones. Our project is about modeling and designing these special E.coli 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 ...


Fig. 1: 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)

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 E. coli.

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:

Intelligent Biosensors and Self-Adaptation

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. 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.

The main applications of our system however, lie in fully exploiting its memorizing potential, as can be understood from the following:

Multipurpose Cell Lines

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.

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 2n, 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.

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 double promoters to the 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 (stefan: this sentence is not so clear to me, can we formulate it differently?).

Link to Epigenetics

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.

Team Members

ETHZ iGEM2007 Team

The ETH Zurich team consists of good mixture between biologists and engineering students. We are:

For more information about us, visit our Meet the Team page.


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 Sven Panke's Lab, especially Andreas Meyer who was always there for us when we had a problem. Additionally, we would like to thank Alfredo Franco-Obregóns lab and Oralea Büchi for the help with the flow cytometry.

We would also like to acknowledge the financial support by EU, the ETH Zurich, and GeneArt:

Site Map

In this wiki, we will present you a detailed description of the proposed system: starting with the modeling of the system, we describe both, simulations and theoretical considerations of the system, as well as the actual implementation using bio-bricks accompanied by our lab notes. Additionally, you find some further information on the team, some more details about ideas we developed before we came up with the system we finally implemented, and some pictures documenting our work.

The site map of our wiki is the following:

Modeling Pages Biology Pages ETHZ Team Pages Links
Modeling of the learning system Biological implementation Team page The ETH Zurich 2005 project
Representation using flip-flops Biobricks/parts Pictures The ETH Zurich 2006 project
Representation using finite state machines Lab notes Brainstorming sessions
Model simulations and theoretical considerations
Parameters used in our simulations


[1] Bird A "Perceptions of epigenetics", Nature 447:396-398, 2007
[2] Ptashne M "On the use of the word ‘epigenetic’", Current Biology 17(7):R233-R236, 2007

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