Team:Evry/Modeling/vf

From 2013.igem.org

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<h1>Aperçu de la partie modélisation</h1>
<h1>Aperçu de la partie modélisation</h1>
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<p> La partie modélisation s'organise autour de 3 axes principaux:
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<h2>Modeling Parts</h2>
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<ul>
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<table width="100%">
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    <li>La cartographie de l'intestin et des interactions: <span style="font-style:italic;">Quels seraient les effets du traitement ?</span>
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<tr width="100%">
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    <li>La croissance de la population de bactéries chélatant le fer : <span style="font-style:italic;">Nos bactéries peuvent-elles coloniser le duodénum ? </span>
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<td align="center"><span style="color:#7B0000"><b>Flush treatment model</b></span></td>
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    <li>Evaluation des risques : <span style="font-style:italic;">prévision, contrôle, ...</span>
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<td align="center"></td>
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</ul></p>
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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">
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<p>
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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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</p>
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</td>
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</tr>
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</table>
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<br/><br/>
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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"></td>
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<td align="center"><span style="color:#7B0000"><b>Enterobactin production model</b></span></td>
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</tr>
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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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</p>
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</td>
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<td align="center">
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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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</td>
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</tr>
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</table>
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<br/><br/>
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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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</td>
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<td align="center">
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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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</p>
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</td>
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</tr>
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</table>
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<br/><br/>
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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"></td>
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<td align="center"><span style="color:#7B0000"><b>Population scale model</b></span></td>
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</tr>
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<tr width="100%">
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<td align="center">
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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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<td align="center">
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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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</td>
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</tr>
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</table>

Revision as of 03:20, 29 October 2013

Iron coli project

Aperçu de la partie modélisation

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.