Team:Groningen/project

From 2011.igem.org

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The aim of our project is to design  a genetic device working in E.coli  able to count and memorize the occurrences of an input signal. This functionality is achieved by utilization of auto-inducing loops, that act as [https://2011.igem.org/Team:Groningen/project_circuit memory units], and an engineered riboregulator, acting as an [https://2011.igem.org/Team:Groningen/project_circuit AND gate]. Each increase of the counter will result in a different state of the system that will subsequently lead to a defined output signal.
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{{HeaderGroningen2011}}
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The design of the device is modular. This means, that both  input and output signals can be changed freely, without disturbing the functionality of the memory system. Also, the design allows implementation of any number of memory units, as the AND gate design enables to extend the system in a hassle-free way.Therefore possible applications of our system range from a memory system of a Turing complete machine, through bacteria used as biosensors measuring the number of occurrences of an event in a process, to bacteria that can perform multi-step bioconversions sequentially all by themselves and without the need of changing them to another strain.
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==1, 2, 3, ... not as easy as you might think!==
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<br>The ability to count might appear as an easy task at first sight. But just hold on for a minute and think about what you are actually doing while you are counting. <br>
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Our research is strongly dependent on [https://2011.igem.org/Team:Groningen/modeling_overview modeling support]. We have created a genetic algorithm that will enable us to predict and calculate parameters of the parts used in the design. The framework of the model will be made usable for other researchers via a cloud-based application. We have also generated a bi-stability model, that furthermore includes predictions about the stability of information stored in the bi-stable switches.
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Let's assume you are counting the red traffic lights you encounter while being already late for work. First, you have to be able to perceive the red light. The light signal your eyes receive will be processed and you will count to ‘one’ in your mind. Already at this point it is becoming tricky. Imagine, the traffic light will be red for five minutes. Even when you are waiting in front of the traffic light for three minutes (while getting more, and more stressed…) it will still be the first one you encountered today. This means, you have to be able to remember and recognize the traffic light as the first one, even after stearing at it for several minutes. Now, the traffic light switches to green, you drive on, but, importantly, you have to keep in mind, that you already encountered one red traffic light today! When you reach the next crossing with a red traffic light, you will process the signal again, and this time count ‘two’. <br>
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However, we are human beings with highly developed sensory organs whose received signals are processed with the help of billions of neurons. With our project, we want to face the challenge to enable an one-cellular organism to perform this highly complex task. <br>
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From the example above it became clear, that counting requires several working elements (see also [http://www.ncbi.nlm.nih.gov/pubmed?term=Smolke%20DNA%20that%20counts%20science Smolke et al.]):
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*A signal detector and processor.
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*A time delay mechanism, to extend the time between signal detection and output generation, to make it longer than the signal duration. This element substitutes in some sense our ability to distinguish between the different input signal, e.g. remembering the first red traffic light as the first input signal, no matter how long it will stay red.
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*A memory unit that will store the information about how many signals the system has received already.
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*An output element, that will tell us how many signal we have recieved up to now.
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<br>
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===Our project: Count coli===
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Of course we do not want to simply rebuild already existing technical counters within a biological system. We intend to design a genetic system that will enable us to count what is not technically feasible at the moment. Think of cell cycles or steps in cell differentiation. We believe that our system can be useful for research in the field of aging or developmental biology. Considering the modular character of the system, the possibility to exchange an output signal visible for the researcher, to an output 'visible' for a cell - e.g. the production of a protein crucial to go one step further in cell cycle - broadens the area of applications even further. Counting of practically any signal a cell can receive from their environment can be realized, as long as an analogous signal detector is implementable. <br>
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[[File:GroningenComic.png|thumb|center|700px|Illustration of our system Count coli]]
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A synthetic gene network able to count was presented by Friedland et al. in[http://www.ncbi.nlm.nih.gov/pubmed/19478183 2009]. Our design combines the characteristics of modularity and extensibility of this known system but additionally exhibits two improvements: We implemented a specific output signal for every state of the system. Unlike the Friedland system, which gives only one visible output when the system reached its end state, our design will provide also information about the intermediate states. Furthermore, we designed a 'reset button' that will allow setting back the system to the initial 'zero' state independet from the current state of the system. <br>
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We believe, that Count coli will be an useful and important foundational biological sytem, that can be used as an integrated part of more complex biological systems in the future of iGEM.
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<br>
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====Cumulus====
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In addition we want to adress a problem in synthetic biology that was pointed out by Drew Endy in [http://www.ncbi.nlm.nih.gov/pubmed?term=foundations%20engineering%20biology%20endy 2005]. The parts we are working with to build sythetic biological systems are not well characterized. This fact still hampers the quick and uncomplicated design of engineered biological systems. We adressed this issue by designing a computer application that will allow scientists to share data, models, and computational power while characterizing the parts they are working with.

Latest revision as of 23:31, 21 September 2011


1, 2, 3, ... not as easy as you might think!


The ability to count might appear as an easy task at first sight. But just hold on for a minute and think about what you are actually doing while you are counting.
Let's assume you are counting the red traffic lights you encounter while being already late for work. First, you have to be able to perceive the red light. The light signal your eyes receive will be processed and you will count to ‘one’ in your mind. Already at this point it is becoming tricky. Imagine, the traffic light will be red for five minutes. Even when you are waiting in front of the traffic light for three minutes (while getting more, and more stressed…) it will still be the first one you encountered today. This means, you have to be able to remember and recognize the traffic light as the first one, even after stearing at it for several minutes. Now, the traffic light switches to green, you drive on, but, importantly, you have to keep in mind, that you already encountered one red traffic light today! When you reach the next crossing with a red traffic light, you will process the signal again, and this time count ‘two’.
However, we are human beings with highly developed sensory organs whose received signals are processed with the help of billions of neurons. With our project, we want to face the challenge to enable an one-cellular organism to perform this highly complex task.
From the example above it became clear, that counting requires several working elements (see also [http://www.ncbi.nlm.nih.gov/pubmed?term=Smolke%20DNA%20that%20counts%20science Smolke et al.]):

  • A signal detector and processor.
  • A time delay mechanism, to extend the time between signal detection and output generation, to make it longer than the signal duration. This element substitutes in some sense our ability to distinguish between the different input signal, e.g. remembering the first red traffic light as the first input signal, no matter how long it will stay red.
  • A memory unit that will store the information about how many signals the system has received already.
  • An output element, that will tell us how many signal we have recieved up to now.


Our project: Count coli

Of course we do not want to simply rebuild already existing technical counters within a biological system. We intend to design a genetic system that will enable us to count what is not technically feasible at the moment. Think of cell cycles or steps in cell differentiation. We believe that our system can be useful for research in the field of aging or developmental biology. Considering the modular character of the system, the possibility to exchange an output signal visible for the researcher, to an output 'visible' for a cell - e.g. the production of a protein crucial to go one step further in cell cycle - broadens the area of applications even further. Counting of practically any signal a cell can receive from their environment can be realized, as long as an analogous signal detector is implementable.

Illustration of our system Count coli

A synthetic gene network able to count was presented by Friedland et al. in[http://www.ncbi.nlm.nih.gov/pubmed/19478183 2009]. Our design combines the characteristics of modularity and extensibility of this known system but additionally exhibits two improvements: We implemented a specific output signal for every state of the system. Unlike the Friedland system, which gives only one visible output when the system reached its end state, our design will provide also information about the intermediate states. Furthermore, we designed a 'reset button' that will allow setting back the system to the initial 'zero' state independet from the current state of the system.
We believe, that Count coli will be an useful and important foundational biological sytem, that can be used as an integrated part of more complex biological systems in the future of iGEM.

Cumulus

In addition we want to adress a problem in synthetic biology that was pointed out by Drew Endy in [http://www.ncbi.nlm.nih.gov/pubmed?term=foundations%20engineering%20biology%20endy 2005]. The parts we are working with to build sythetic biological systems are not well characterized. This fact still hampers the quick and uncomplicated design of engineered biological systems. We adressed this issue by designing a computer application that will allow scientists to share data, models, and computational power while characterizing the parts they are working with.

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