Team:Grenoble/Modeling/Introduction

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Démonstration
 
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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 <a href="https://2012.igem.org/Team:Grenoble/Biology/Introduction#scheme">two modules</a>:
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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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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" style="font-size: 1.2em;"><img src="https://static.igem.org/mediawiki/2012/1/1e/3_mod.png" alt="" />External amplification and communication</a>
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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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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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Because we know that the production of protein is not always turned on or turned off, this can lead to false positives/negatives. We could evaluate the false positives 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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