Team:Paris/Modeling

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We had different approaches to model the biological system. We found interesting to explain at least two ways that we went throught. It is important to understand that both models aim at different goals in the process of understanding our system.
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= Our train of thoughts... =
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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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Furthermore, we wished to describe our thought process, the way these models interact, their respective roles. 
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An overall description of the way we model our biological system can be found below :
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<center>[[Team:Paris/Modeling/History|Read more !]]</center>
= BOB (Based On Bibliography) Approach =
= BOB (Based On Bibliography) Approach =
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[[Image:BOB.jpg|250px|thumb]]
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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 allow us to use parameters and datas from scientific articles.
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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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The keys points of this approach:
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The key points of this approach:
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* Simplicity for itself is not so much important, what we were looking for was understandability at first.
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* We used linear equations as much as possible: when it as already been proved in a paper than one interaction is efficiently modeled with an elementary expression, we tried to keep it.
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* Too many parameters in a model means less relevancy. In addition, the more parameters you have, the hardest it becomes to tune the system in order to have the behavior you are looking for.
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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.
<center>[[Team:Paris/Modeling/BOB|Read more]]</center>
<center>[[Team:Paris/Modeling/BOB|Read more]]</center>
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= APE (APE Parameters Estimation) Approach =
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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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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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* Each step is taken into account at a fundamental kinetic processes level or at a more global level by a Hill function which is a simple way to take into account multiple interactions and more especially cooperative binding.
 
* Specific experiments focused on finding relevant parameters have been designed and planned.
* Specific experiments focused on finding relevant parameters have been designed and planned.
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* Some noise parameters are included in the set of parameters to estimate, in order to try to translate the natural entropy into the model.
 
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<center>[[Team:Paris/Modeling/APE|Read more]]</center>
 
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[http://en.wikipedia.org/wiki/Recursive_acronym (Wondering what APE is standing for?)]
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<center> >> [[Team:Paris/Modeling/estimation|Estimation]] </center>
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= Depreciated pages =
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= Old but still usefull pages =
*[[Team:Paris/Modeling/Bibliography|Bibliographic References]]
*[[Team:Paris/Modeling/Bibliography|Bibliographic References]]
*[[Team:Paris/Modeling/linear_approach|Preliminary approach]]
*[[Team:Paris/Modeling/linear_approach|Preliminary approach]]
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*[[Team:Paris/Modeling/hill_approach|Hill approach]]
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*[[Team:Paris/Modeling/Roadmap|Roadmap]]
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*[[Team:Paris/Modeling/estimation|Estimation]]
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*[[Team:Paris/Modeling/Roadmap|Roadmad]]
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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