Team:Grenoble/Modeling/Amplification

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<h1>Amplification module</h1>
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<a href="https://2012.igem.org/wiki/index.php?title=Team:Grenoble/Modeling/Amplification/Possible"><img src="https://static.igem.org/mediawiki/2012/8/89/Is_it_possible.png" alt="" /></a>
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<a href="https://2012.igem.org/wiki/index.php?title=Team:Grenoble/Modeling/Amplification/ODE"><img src="https://static.igem.org/mediawiki/2012/e/ef/ODE.png" alt="" /></a>
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<a href="https://2012.igem.org/wiki/index.php?title=Team:Grenoble/Modeling/Amplification/Sensitivity"><img src="https://static.igem.org/mediawiki/2012/a/a4/Sensitivity_and_parameters.png" alt="" /></a>
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<a href="https://2012.igem.org/wiki/index.php?title=Team:Grenoble/Modeling/Amplification/Quorum"><img src="https://static.igem.org/mediawiki/2012/6/65/Quorum_Sensing.png" alt="" /></a>
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<a href="https://2012.igem.org/wiki/index.php?title=Team:Grenoble/Modeling/Amplification/Stochastic"><img src="https://static.igem.org/mediawiki/2012/a/ad/Stochastic_analysis.png" alt="" /></a>
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<h1>Introduction</h1>
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In this part we will model the amplification module. Our work in this module is subdivided in three main parts: A deterministic model of the reactions at the local scale, another version of the former taking into account some random noise/perturbations, and a model of the signal's diffusion in space.
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In this part we will model the amplification module. Our work in this module is subdivided in three main parts: a deterministic model of the reactions at the local scale, another version of the former taking into account random noise and perturbations, and a model including diffusion in space and time.
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In the deterministic model, we checked the sensitivity of our system, in the diffusion part we checked if our system had a fast answer. Eventually in the random perturbations model, we checked that it was robust to perturbations.
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In the deterministic model, we determine the sensitivity of our system and we link it to the signaling module. Then, in the diffusion part we check if our system has a fast answer. Eventually, in the random perturbations model, we check that it is robust to perturbations.
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<h1> Overview</h1>
<h1> Overview</h1>
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<span style="text-decoration:underline;">Remark:</span>
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We wanted to create a detector, thus we could have designed it like following:
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Where X is the molecule to detect, and Z the fluorescent signal. However, with this design, the communication between the bacteria (quorum sensing) wouldn’t have worked really well, we would have needed an important quantity of X at the initial time to be able to obtain an important diffusion that we could actually see. Indeed, the evolution of X would have been like following:
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When our biosensor detects a dipeptide specific to a pathogen,the bacterium activates the production of a green fluorescent
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protein (GFP). In our system the production of GFP begins when the production of an other protein, the adenylate cyclase (Ca) begins. Indeed, they are under the control of the same promotor, paraBAD (pBad), and thus they have exatly the same behavior:
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Thus, the next idea was to amplify X:
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The protein GFP is a reporter protein for adenylate cyclase. Thus, in the development, gfp will be omitted, and we will consider that the adenylate cyclase gives us the signal.
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<b>Why an amplification module?</b>
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When one bacterium detects the dipeptide, it will become green. However, if only one bacterium becomes green, we will not be able to observe the signal. That is why we decided to use the communication between the bacteria, called quorum sensing: if one bacterium becomes green, the surrounding bacteria will become green too, and thus we will be able to get the signal.
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Like this, as soon as it would be detected, by a bacterium, the bacterium would re-create some X, and the quorum sensing would work, as we would have this evolution of X:
 
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How to do this?
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First, we chose a molecule, which is produced by bacteria and diffuses from bacteria to the other bacterium. We chose cyclic AMP, whose production is catalyzed by the adenylate cyclase. Thus, we designed:
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Thanks to the quorum sensing if we detect X, we can easily measure it.
 
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Now, we had a last problem: the false positives. Indeed, we have a detector, so we don’t want to have a signal if there is nothing to detect. Thus, we decided to add a classic feed forward loop, because it is known to reduce the false positives. Finally, we got our system:
 
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However, with this design, the whole adenylate cyclase would have been used too quickly to produce enough cAMP to enable the communication between the bacteria. That is why we did this modification:
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X is a molecule that has the ability to be transmitted from bacterium to an other. It is a quorum-sensing molecule.
 
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Y, and Z are 2 genes. X is the transcription factor of Y.  Thus when it is introduced, the gene Y is expressed. Then, the molecule X and the protein Y together will be the transcription factor of Z. When Z is expressed it creates more X.
 
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As soon as some cAMP is produced, it will increase the production of adenylate cyclase, which will catalyse the production of even more cAMP, and so forth. However, if some adenylate cyclase was produced when it shouldn’t (because of the promotor let off for example), the system would have started. Thus, we needed to increase the robustness of our system to false positives which we did by adding a classic feed forward loop. The production of the aenylate cyclase now begins if and only if there is enough cAMP AND enough of an intermediary protein, here AraC. We finally got:
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<span class="gras">Conclusion:</span>
 
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Now that we designed our system, we wanted to really study the behavior of this topology before going further in this project.
 
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Now that we have the topology of the entire system we need to study precisely if it works, and how it works.
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Latest revision as of 02:57, 27 September 2012

iGEM Grenoble 2012

iGEM Grenoble iGEM
iGEM 2012
main page
Project

Amplification module


In this part we will model the amplification module. Our work in this module is subdivided in three main parts: a deterministic model of the reactions at the local scale, another version of the former taking into account random noise and perturbations, and a model including diffusion in space and time.

In the deterministic model, we determine the sensitivity of our system and we link it to the signaling module. Then, in the diffusion part we check if our system has a fast answer. Eventually, in the random perturbations model, we check that it is robust to perturbations.

Overview

Remark:

When our biosensor detects a dipeptide specific to a pathogen,the bacterium activates the production of a green fluorescent protein (GFP). In our system the production of GFP begins when the production of an other protein, the adenylate cyclase (Ca) begins. Indeed, they are under the control of the same promotor, paraBAD (pBad), and thus they have exatly the same behavior:



The protein GFP is a reporter protein for adenylate cyclase. Thus, in the development, gfp will be omitted, and we will consider that the adenylate cyclase gives us the signal.
Why an amplification module?

When one bacterium detects the dipeptide, it will become green. However, if only one bacterium becomes green, we will not be able to observe the signal. That is why we decided to use the communication between the bacteria, called quorum sensing: if one bacterium becomes green, the surrounding bacteria will become green too, and thus we will be able to get the signal.

How to do this?

First, we chose a molecule, which is produced by bacteria and diffuses from bacteria to the other bacterium. We chose cyclic AMP, whose production is catalyzed by the adenylate cyclase. Thus, we designed:



However, with this design, the whole adenylate cyclase would have been used too quickly to produce enough cAMP to enable the communication between the bacteria. That is why we did this modification:



As soon as some cAMP is produced, it will increase the production of adenylate cyclase, which will catalyse the production of even more cAMP, and so forth. However, if some adenylate cyclase was produced when it shouldn’t (because of the promotor let off for example), the system would have started. Thus, we needed to increase the robustness of our system to false positives which we did by adding a classic feed forward loop. The production of the aenylate cyclase now begins if and only if there is enough cAMP AND enough of an intermediary protein, here AraC. We finally got:



Now that we have the topology of the entire system we need to study precisely if it works, and how it works.

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