Team:Grenoble/Modeling/Introduction

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<math> $ \frac{d[TetR]}{dt} = \frac{k_{pLac}.[pLac]_{tot}}{1 + (\frac{[lacI]}{K_{pLac} + \frac{K_{pLac}.[IPTG]}{K_{lacI-IPTG}}.})^\beta} - \delta_{TetR}.[TetR] $</math>
 
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<h1>Introduction</h1>
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<h1>Overview</h1>
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To model the system, we divided it into two modules:
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To model the system, we divided it into <a href="https://2012.igem.org/Team:Grenoble/Biology/Introduction#scheme">three modules</a>:
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<ul><ul><a href="https://2012.igem.org/Team:Grenoble/Modeling/Signaling"><img src="https://static.igem.org/mediawiki/2012/4/49/1_mod.png" alt="" style="font-size: 1.1em;"/>The signaling module </a>
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<a href="https://2012.igem.org/Team:Grenoble/Modeling/Signaling" style="font-size: 1.2em;"><img src="https://static.igem.org/mediawiki/2012/4/49/1_mod.png" alt="" />Signaling module </a>
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In this part we did a classic deterministic model. The goal was to answer the question: what is the sensitivity of our detector? This analysis will enable us to know if our amplification module will be able to enhance the incoming signal enough to drive the other modules. We didn't do a stochastic model, as the number of false positives and negatives depend on the robustness of the biological system that we were not able to assess.
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In this part we used a deterministic model to determine the sensitivity of the sensor. This analysis enabled us to know that the amplification module is required for the incoming signal to drive the subsequent modules.
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<ul><ul><a href="https://2012.igem.org/Team:Grenoble/Modeling/Amplification"><img src="https://static.igem.org/mediawiki/2012/1/1e/2_mod.png" alt="" style="font-size: 1.1em;"/>The amplification module </a>
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<a href="https://2012.igem.org/Team:Grenoble/Modeling/Amplification" style="font-size: 1.2em;"><img src="https://static.igem.org/mediawiki/2012/1/1e/2_mod.png" alt="" />Internal amplification module</a>
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-We first did a classic deterministic model to be able to evaluate the sensitivity of the amplification loop. Then, we studied the temporal evolution to know how long we would have to wait for one bacterium to become green. Eventually, we did a study at the steady states to understand why our system would work.
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We first used deterministic model to evaluate the sensitivity of the amplification loop and determine the response time. A steady state analysis was performed to understand how the system works.
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<a href="https://2012.igem.org/Team:Grenoble/Modeling/Amplification/Quorum" style="font-size: 1.2em;"><img src="https://static.igem.org/mediawiki/2012/5/57/3_mod.png" alt="" />External amplification and communication</a>
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-Then, we studied the communication between the bacteria to evaluate the time we would have to wait to actually be able to get the signal.
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Then, we studied the communication between the bacteria to evaluate the time collective response time of a bacterial population as a whole.
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-Because we know that the production of protein is not always turned on or turned off, this can lead to false positives. We could evaluate the false positives by using a stochastic model.
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Because we know that the production of protein is not always turned on or turned off, this can lead to false positives/negatives. We also evaluated the false positives rate of our sensor using a stochastic model.  
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Latest revision as of 02:54, 27 September 2012

iGEM Grenoble 2012

iGEM Grenoble iGEM
iGEM 2012
main page
Project

Overview

To model the system, we divided it into three modules:

Signaling module

In this part we used a deterministic model to determine the sensitivity of the sensor. This analysis enabled us to know that the amplification module is required for the incoming signal to drive the subsequent modules.

Internal amplification module

We first used deterministic model to evaluate the sensitivity of the amplification loop and determine the response time. A steady state analysis was performed to understand how the system works.

External amplification and communication

Then, we studied the communication between the bacteria to evaluate the time collective response time of a bacterial population as a whole.

Because we know that the production of protein is not always turned on or turned off, this can lead to false positives/negatives. We also evaluated the false positives rate of our sensor using a stochastic model.

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