Team:Evry/Modeling

From 2013.igem.org

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<h1> Model overview </h1>
 
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<h1>Modeling Overview</h1>
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<p>
<div class="jamboree">
<div class="jamboree">
   <header>New since European Jamboree</header>
   <header>New since European Jamboree</header>
   <p>
   <p>
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  coucou
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    Following the different remarks made by the team, judges, and other people, we entirely reworked the modeling section :
 +
 
 +
    <ul>
 +
On the structure:
 +
      <ul>
 +
      <li>Each header section presents the methods used in the model, and could be compared to a "Material and Methods" section of a scientific paper;</li>
 +
      <li>Each subsequence page presents the different simulations and answers obtained with the model; as in the "Result" section of a scientific paper;</li>
 +
      <li>This page is now an entry point to our work, presenting our modeling philosophy, main results and the relevant models.</li>
 +
      </ul>
 +
    </li>
 +
On the content:
 +
      <ul>
 +
      <li>The description of each model is now more precise, the equations better explained and the results analyzed in depths.</li>
 +
      <li>We highlighted more carefuly each assumption of the models and each parameter's value, giving sources when possible.</li>
 +
      <li>Finally, our efforts focused on better describing the links between the biology part of our projet and our models and between the different models.
 +
    <li>Additionally, we created a program that analyze automatically our TECAN data. That program could be reuse by any team that have a 96-wells plate reader for their BioBricks characterization.
 +
      </ul>
 +
    </ul>
   </p>
   </p>
</div>
</div>
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</p>
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<h2>Introduction:</h2>
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<h2>Modeling Parts</h2>
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<table width="100%">
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<tr width="100%">
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<td align="center"><span style="color:#7B0000"><b>Flush treatment model</b></span></td>
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<td align="center"></td>
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</tr>
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<tr width="100%">
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<td align="center"><a href="https://2013.igem.org/Team:Evry/flush_model"><img height="300px" src="https://static.igem.org/mediawiki/2013/1/1e/OverviewDuodenum.png"/></a>
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</td>
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<td align="center">
<p>
<p>
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In the begining, our goal was to chelate iron in the duodenum, using bacteria that would flush through the duodenum and produce the siderophores. The aim was to predict the sufficient quantity of produced siderophores to reduce the iron intestinal absorption. We first had in mind a flush strategy, meaning we prioritized an approach where the bacteria would start their iron sensing and siderophore production before entering the duodenum. This qualitative <b>Flush treatment model</b> showed us that it is theoratically possible to significantly reduce the patient's iron absorption.
+
At the begining of our project, we aimed to enable iron chelation in the duodenum using bacteria that would flush through the duodenum and produce the siderophores. Therefore we wanted to predict the minimal quantity of produced siderophores sufficient to reduce the iron intestinal absorption. We first had in mind a flush strategy, meaning we prioritized an approach where the bacteria would start their iron sensing and siderophore production before entering the duodenum. This qualitative <b>Flush treatment model</b> showed us that it is theoretically possible to <b>significantly reduce the patient's iron absorption</b>.  
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</p>
+
-
<p>
+
-
The conclusions were promising, encouraging and comforting regarding our strategy choice. Right afterwards, the aim was to detail the delay of siderophore production for a given bacterial production through an <b>Enterobactin production model</b>. This approach gave us more detail about timings. Unfortunately, the conclusions were in contradiction with the qualitative model because the delay is to big to be compatible with a flush strategy. This conclusion greatly influenced the biological part, especially in the design of the capsule. Because the iron absorption is split among the duodenum (60%) and the jejunum (40%), we decided to enhance growth in the proximal area of the jejunum. This is also why we chose to deliver a sticky gel with our bacteria and optimize its growth.
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</p>
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-
<p>
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As a final part in the modeling, we also wanted to know how much siderophore can be produced and how we can improve this. We answered this with a <b>Flux model</b>, a flux balance analysis approach.
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</p>
</p>
 +
</td>
 +
</tr>
 +
</table>
 +
 +
<br/><br/>
 +
<div align="center"><img src="https://static.igem.org/mediawiki/2013/4/4d/SeparateurVertical.png"/></div>
 +
<br/><br/>
-
<h2>Models using ODEs:</h2>
 
-
<p>
 
<table width="100%">
<table width="100%">
<tr width="100%">
<tr width="100%">
-
<td align="center"><br/><b>Flush treatment</b></td>
+
<td align="center"></td>
-
<td align="center"><b>Enterobactin production</b></td>
+
<td align="center"><span style="color:#7B0000"><b>Enterobactin production model</b></span></td>
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</tr>
+
-
<tr width="100%">
+
-
<td align="center"><img height="300px" src="https://static.igem.org/mediawiki/2013/1/1e/OverviewDuodenum.png"/></td>
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<td align="center"><img height="300px" src="https://static.igem.org/mediawiki/2013/c/c8/OverviewMetabolic.png"/></td>
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</tr>
</tr>
<tr width="100%">
<tr width="100%">
<td align="center">
<td align="center">
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<ul>
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<p>
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<li><a href="https://2013.igem.org/Team:Evry/Modeltr1">Disease model</a></li>
+
The conclusions were promising, encouraging and comforting regarding our strategy. Therefore we investigated in detail the delay in siderophore production for a given bacterial production through an <b>Enterobactin production model</b> that integrate our sensor, invertor and chelator systems. This investigation gave us more details on timings. Unfortunately, the conclusions were in contradiction with the qualitative model because the delay is too important to be compatible with a flush strategy. <b>This finding greatly influenced the biological part, especially the <a href="https://2013.igem.org/Team:Evry/Pill_design">capsule design</a></b>. Because iron absorption is split between the duodenum (60%) and the jejunum (40%), we decided to retain bacteria in duodenum and the proximal area of the jejunum.
-
<li><a href="https://2013.igem.org/Team:Evry/Modeltr2">Final model</a></li>
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</p>
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</ul>
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</td>
</td>
<td align="center">
<td align="center">
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<ul>
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<a href="https://2013.igem.org/Team:Evry/ent_prod"><img height="300px" src="https://static.igem.org/mediawiki/2013/c/c8/OverviewMetabolic.png"/></a>
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<li><a href="https://2013.igem.org/Team:Evry/Modelmeta1">Sensor model</a></li>
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<li><a href="https://2013.igem.org/Team:Evry/Modelmeta2">Inverter model</a></li>
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<li><a href="https://2013.igem.org/Team:Evry/Modelmeta3">Final model</a></li>
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</ul>
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</td>
</td>
</tr>
</tr>
</table>
</table>
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</p><br/>
 
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<h2>Models using other methods:</h2>
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<br/><br/>
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<p>
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<div align="center"><img src="https://static.igem.org/mediawiki/2013/4/4d/SeparateurVertical.png"/></div>
 +
<br/><br/>
 +
 
<table width="100%">
<table width="100%">
<tr width="100%">
<tr width="100%">
-
<td align="center"><b>Flux model</b></td>
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<td align="center"><span style="color:#7B0000"><b>Genome scale model</b></span></td>
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<td align="center"><b>Population scale</b></td>
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<td align="center"></td>
</tr>
</tr>
<tr width="100%">
<tr width="100%">
-
<td align="center"><img height="300px" src="https://static.igem.org/mediawiki/2013/6/65/OverviewFBA.png"/></td>
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<td align="center"><a href="https://2013.igem.org/Team:Evry/Metabolism_model"><img height="300px" src="https://static.igem.org/mediawiki/2013/6/65/OverviewFBA.png"/></a>
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<td align="center"><img height="300px" src="https://static.igem.org/mediawiki/2013/a/ae/OverviewPopulation.png"/></td>
+
</td>
 +
<td align="center">
 +
<p>
 +
We also wanted to know how much siderophore can be produced and how we can improve this. We answered this with a <b>Genome scale model</b>, using a flux balance analysis approach. We determine <b>what are the limiting metabolites</b> and <b>how we could improve our capsule</b>.
 +
</p>
 +
</td>
</tr>
</tr>
 +
</table>
 +
 +
<br/><br/>
 +
<div align="center"><img src="https://static.igem.org/mediawiki/2013/4/4d/SeparateurVertical.png"/></div>
 +
<br/><br/>
 +
 +
<table width="100%">
<tr width="100%">
<tr width="100%">
-
<td align="center"><a href="https://2013.igem.org/Team:Evry/Metabolism_model">Flux model</a></td>
+
<td align="center"></td>
-
<td align="center"><a href="https://2013.igem.org/Team:Evry/Modelpop">Population scale</a></td>
+
<td align="center"><span style="color:#7B0000"><b>Population scale model</b></span></td>
 +
</tr>
 +
<tr width="100%">
 +
<td align="center">
 +
<p>
 +
As a final modeling part, we wanted to know if our second treatment strategy was viable. We build a model that aims the same type of questions as the flush treatment model. The assumptions are also the same, but the method is really different : for more precision in the process description, we used a cellular automaton approach. This  model showed us that the new strategy can still <b>significantly reduce the patient's iron absorption</b>.
 +
</p>
 +
</td>
 +
<td align="center">
 +
<a href="https://2013.igem.org/Team:Evry/pop_scale"><img height="300px" src="https://static.igem.org/mediawiki/2013/a/ae/OverviewPopulation.png"/></a>
 +
</td>
</tr>
</tr>
</table>
</table>
-
</p><br/>
 
<h2>Tools:</h2>
<h2>Tools:</h2>
<p>
<p>
-
When working on a scientific project, it is always good to properly define and clarify the tools we are going to use. These pages contain the theorical background for our models:<br/>
+
When working on a scientific project, it is always good to properly define and clarify the tools being used. These pages contain the theoretical background for our models:<br/>
<table width="100%">
<table width="100%">
<tr width="100%">
<tr width="100%">
-
<td align="center"><a href="https://2013.igem.org/Team:Evry/Programming"><img src="https://static.igem.org/mediawiki/2013/7/7c/Tools-01.png" height="300px"/></a></td>
 
-
 
<td align="center"><a href="https://2013.igem.org/Team:Evry/LogisticFunctions"><img src="https://static.igem.org/mediawiki/2013/8/86/Tools-02.png" height="300px"/></a></td>
<td align="center"><a href="https://2013.igem.org/Team:Evry/LogisticFunctions"><img src="https://static.igem.org/mediawiki/2013/8/86/Tools-02.png" height="300px"/></a></td>
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</tr>
</tr>
<tr width="100%">
<tr width="100%">
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<td align="center"><b>Programming methods</b></span></td>
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<td align="center"><span style="color:#7B0000"><b>Logistic functions</b></span></td>
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<td align="center"><b>Logistic functions</b></span></td>
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<td align="center"><span style="color:#7B0000"><b>Chemical reasoning</b></span></td>
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<td align="center"><b>Chemical reasoning</b></span></td>
+
</table>
</table>
</p>
</p>
 +
 +
 +
 +
We also developed a data analysis software for plate reader machines, check out <a href="https://2013.igem.org/Team:Evry/data_analysis">this page</a> for more information.
</div>
</div>

Latest revision as of 04:00, 29 October 2013

Iron coli project

Modeling Overview

New since European Jamboree

Following the different remarks made by the team, judges, and other people, we entirely reworked the modeling section :

    On the structure:
    • Each header section presents the methods used in the model, and could be compared to a "Material and Methods" section of a scientific paper;
    • Each subsequence page presents the different simulations and answers obtained with the model; as in the "Result" section of a scientific paper;
    • This page is now an entry point to our work, presenting our modeling philosophy, main results and the relevant models.
    On the content:
    • The description of each model is now more precise, the equations better explained and the results analyzed in depths.
    • We highlighted more carefuly each assumption of the models and each parameter's value, giving sources when possible.
    • Finally, our efforts focused on better describing the links between the biology part of our projet and our models and between the different models.
    • Additionally, we created a program that analyze automatically our TECAN data. That program could be reuse by any team that have a 96-wells plate reader for their BioBricks characterization.

Modeling Parts

Flush treatment model

At the begining of our project, we aimed to enable iron chelation in the duodenum using bacteria that would flush through the duodenum and produce the siderophores. Therefore we wanted to predict the minimal quantity of produced siderophores sufficient to reduce the iron intestinal absorption. We first had in mind a flush strategy, meaning we prioritized an approach where the bacteria would start their iron sensing and siderophore production before entering the duodenum. This qualitative Flush treatment model showed us that it is theoretically possible to significantly reduce the patient's iron absorption.





Enterobactin production model

The conclusions were promising, encouraging and comforting regarding our strategy. Therefore we investigated in detail the delay in siderophore production for a given bacterial production through an Enterobactin production model that integrate our sensor, invertor and chelator systems. This investigation gave us more details on timings. Unfortunately, the conclusions were in contradiction with the qualitative model because the delay is too important to be compatible with a flush strategy. This finding greatly influenced the biological part, especially the capsule design. Because iron absorption is split between the duodenum (60%) and the jejunum (40%), we decided to retain bacteria in duodenum and the proximal area of the jejunum.





Genome scale model

We also wanted to know how much siderophore can be produced and how we can improve this. We answered this with a Genome scale model, using a flux balance analysis approach. We determine what are the limiting metabolites and how we could improve our capsule.





Population scale model

As a final modeling part, we wanted to know if our second treatment strategy was viable. We build a model that aims the same type of questions as the flush treatment model. The assumptions are also the same, but the method is really different : for more precision in the process description, we used a cellular automaton approach. This model showed us that the new strategy can still significantly reduce the patient's iron absorption.

Tools:

When working on a scientific project, it is always good to properly define and clarify the tools being used. These pages contain the theoretical background for our models:

Logistic functions Chemical reasoning

We also developed a data analysis software for plate reader machines, check out this page for more information.

Retrieved from "http://2013.igem.org/Team:Evry/Modeling"