Team:Paris/Modeling

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==Roadmap==
 
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===Design a modular Matlab modelisation of our system===
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= Our train of thoughts... =  
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Using the following approach:
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We hereby propose different and complementary approaches to model the biological system. We found interesting to explain two of the paths that we chose to follow in order to understand and predict our system. It is important to note that both models aim at different goals in the process of understanding our system.
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* 3 modules corresponding to the FIFO Temporal Order function, the Oscillation's one and the Sync function.
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Furthermore, we wished to describe our thought process, the way these models interact, their respective roles.
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* Gather the 3 modules using a blackbox approach: each function is a box having its own inputs and outputs and does not interact with the others except if one box.
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An overall description of the way we model our biological system can be found below :
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* Divide each box in smaller parts that we can quickly try in the wet lab to figure out precisely some missing parameters.
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<center>[[Team:Paris/Modeling/History|Read more !]]</center>
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===Implement the same models in BIOCHAM===
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= BOB (Based On Bibliography) Approach =
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It will allow easier:
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[[Image:BOB.jpg|250px|thumb]]
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* Exploration of the paramaters space
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* Evalution of the robustness
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* Changes (to a stochastic model for example)
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===MGS===
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Our first approach is quite rough and simple but effective. The goal here was to guess the behavior of our Bacteri'OClock, considering the overall system. Since it is a preliminary approach, we could not yet fill the model with data from the wet lab. This is why our work is mainly based on a bibliographic work, which allows us to use parameters and data from scientific articles.
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* Understand the syntax!
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* Develop a 3D model to simulate diffusion even if the medium will in fact be shook.
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The key points of this approach:
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* Simplicity for itself is not that important. In fact, what we were looking for was understandability at first.
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* We used linear equations as much as possible: wherever it had been proved in a paper than an interaction could be efficiently modeled with a elementary expression, we kept it.
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* Too many parameters in a model mean less relevancy. In addition, the more parameters you have, the hardest it is to tune the system in order to have the behavior you are looking for.
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<center>[[Team:Paris/Modeling/BOB|Read more]]</center>
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= APE (APE Parameters Estimation) Approach=
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[[Image:APE.jpg|250px|thumb]]
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The second approach was motivated by our will to characterize our system in the most precise way. What is at stake here is to determine the "real parameters" that govern the dynamics of our system.
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* Each step is taken into account at a fundamental kinetic processes level or at a more global level by a function describing the complexation, which is a simple way to take into account multiple interactions and more especially cooperative binding.
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<center> >> [[Team:Paris/Modeling/hill_approach|Explanations and description]] </center>
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* Specific experiments focused on finding relevant parameters have been designed and planned.
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<center> >> [[Team:Paris/Modeling/estimation|Estimation]] </center>
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= Old but still usefull pages =
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*[[Team:Paris/Modeling/Bibliography|Bibliographic References]]
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*[[Team:Paris/Modeling/linear_approach|Preliminary approach]]
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*[[Team:Paris/Modeling/Roadmap|Roadmap]]

Latest revision as of 04:46, 30 October 2008

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Contents

Our train of thoughts...

We hereby propose different and complementary approaches to model the biological system. We found interesting to explain two of the paths that we chose to follow in order to understand and predict our system. It is important to note that both models aim at different goals in the process of understanding our system. Furthermore, we wished to describe our thought process, the way these models interact, their respective roles. An overall description of the way we model our biological system can be found below :

Read more !

BOB (Based On Bibliography) Approach

BOB.jpg

Our first approach is quite rough and simple but effective. The goal here was to guess the behavior of our Bacteri'OClock, considering the overall system. Since it is a preliminary approach, we could not yet fill the model with data from the wet lab. This is why our work is mainly based on a bibliographic work, which allows us to use parameters and data from scientific articles.

The key points of this approach:

  • Simplicity for itself is not that important. In fact, what we were looking for was understandability at first.
  • We used linear equations as much as possible: wherever it had been proved in a paper than an interaction could be efficiently modeled with a elementary expression, we kept it.
  • Too many parameters in a model mean less relevancy. In addition, the more parameters you have, the hardest it is to tune the system in order to have the behavior you are looking for.


Read more

APE (APE Parameters Estimation) Approach

APE.jpg

The second approach was motivated by our will to characterize our system in the most precise way. What is at stake here is to determine the "real parameters" that govern the dynamics of our system.

  • Each step is taken into account at a fundamental kinetic processes level or at a more global level by a function describing the complexation, which is a simple way to take into account multiple interactions and more especially cooperative binding.
>> Explanations and description
  • Specific experiments focused on finding relevant parameters have been designed and planned.
>> Estimation

Old but still usefull pages

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