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Team:Evry/Modeling
From 2013.igem.org
Modeling Overview
Following the different remarks made by the team, judges, and other people, we entirely reworked the modeling section :
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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.
- 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 | |
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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 Flush treatment model showed us that it is theoretically possible to significantly reduce the patient's iron absorption. |
| Enterobactin production model | |
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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 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. |
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| Genome scale model | |
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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 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 | |
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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 significantly reduce the patient's iron absorption. |
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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:
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| Logistic functions | Chemical reasoning |
