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Team:Evry/Modeling
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Revision as of 15:47, 28 October 2013
Modeling Overview
Following the different remarks made by the team, judges, and other people, we reworked entirely 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 an article;
- Each subsequence page presents the different simulations and answers obtained with the model; as in the "Result" section of an article;
- This page is now an entry point to our work, presenting out modeling philosophy, main results and pointers to 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 the 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.
Introduction:
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 Flux model using a flux balance analysis approach.
Models using ODEs:
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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. |
| Flush treatment 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. 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 enhance bacterial growth in the proximal area of the jejunum. This is why we chose to deliver a sticky gel with our bacteria and optimize its growth. |
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| Enterobactin production model |
Models using other methods:
| Flux model | Population scale |
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| Flux model | Population scale |
Tools:
When working on a scientific project, it is always good to properly define and clarify the tools being used. These pages contain the theorical background for our models:
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| Programming methods | Logistic functions | Chemical reasoning |
