Team:Evry/Modeling

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<a href='https://2013.igem.org/Team:Evry/Modélisation' title='Vers la page française'> <img src='https://static.igem.org/mediawiki/2013/b/b9/Francais.jpg'/></a>
 
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<h1 align='center'>Model overview</h1>
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<a href="https://2013.igem.org/Team:Evry/Model1">Model 1</a>
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<a href="https://2013.igem.org/Team:Evry/Model2">Model 2</a>
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<p>The modelling part is planned around 3 main targets:
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    <li>Intestine mapping and interactions : <span style="font-style:italic;">What would the treatment look like?</span>
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    <li>Growth of an iron-chelating bacterial population : <span style="font-style:italic;">Can our bacteria live in the duodenum? </span>
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    <li>Risk assessment : <span style="font-style:italic;">forecast, control, ...</span>
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<h1>Modeling Overview</h1>
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<p>
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<div class="jamboree">
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  <header>New since European Jamboree</header>
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  <p>
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    Following the different remarks made by the team, judges, and other people, we entirely reworked the modeling section :
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    <ul>
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On the structure:
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      <ul>
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      <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>
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      <li>Each subsequence page presents the different simulations and answers obtained with the model; as in the "Result" section of a scientific paper;</li>
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      <li>This page is now an entry point to our work, presenting our modeling philosophy, main results and the relevant models.</li>
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      </ul>
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    </li>
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On the content:
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      <ul>
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      <li>The description of each model is now more precise, the equations better explained and the results analyzed in depths.</li>
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      <li>We highlighted more carefuly each assumption of the models and each parameter's value, giving sources when possible.</li>
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      <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.
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    <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.
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      </ul>
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    </ul>
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<h2>Modeling Parts</h2>
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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"><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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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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<table width="100%">
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<td align="center"></td>
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<td align="center"><span style="color:#7B0000"><b>Enterobactin production model</b></span></td>
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<tr width="100%">
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<td align="center">
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<p>
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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.
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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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<div align="center"><img src="https://static.igem.org/mediawiki/2013/4/4d/SeparateurVertical.png"/></div>
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<br/><br/>
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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>Genome scale 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/Metabolism_model"><img height="300px" src="https://static.igem.org/mediawiki/2013/6/65/OverviewFBA.png"/></a>
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<p>
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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>.
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<table width="100%">
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<td align="center"><span style="color:#7B0000"><b>Population scale model</b></span></td>
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<p>
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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>.
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</p>
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</td>
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<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>
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<h2>Tools:</h2>
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<p>
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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/>
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<table width="100%">
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<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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<td align="center"><a href="https://2013.igem.org/Team:Evry/ChemicalTools"><img src="https://static.igem.org/mediawiki/2013/0/0d/Tools-03.png" height="300px"/></a></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"><span style="color:#7B0000"><b>Chemical reasoning</b></span></td>
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</table>
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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.
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{{:Team:Evry/foot}}

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"