Team:Paris/Modeling

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=Roadmap=
 
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* Aims of the modeling part
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= Our train of thoughts... =
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* First approach proposed
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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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** Hill functions
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Furthermore, we wished to describe our thought process, the way these models interact, their respective roles. 
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** first model + score function
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An overall description of the way we model our biological system can be found below :
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** bibliography
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<center>[[Team:Paris/Modeling/History|Read more !]]</center>
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** findparam
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**experiments
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*Second approach
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**bibliography
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**equations
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**results
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**experiments
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* Continue the previous model
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**Synchronyzation
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**Estimation of the FIFO processes
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**Stochastic modeling (Gilespie)
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*Test of robustness
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**repressilator
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**comparison
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*Enhancing the system
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**Better FIFO behaviour
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**Other interactions to increase the robustness
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If you want to have a look at our modeling notebook: [[Team:Paris/Modeling/Roadmap|Notebook]]
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= BOB (Based On Bibliography) Approach =
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[[Image:BOB.jpg|250px|thumb]]
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= Our thought process =
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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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One can find many different approaches to model a biological system. We then found interesting to propose at least two distinct exemples of coherent models. It seems important to understand that both models aim at different goals in the process of understanding our system.
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The key points of this approach:
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== I - Linear 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 wish to present at first a rough and simple, though effective, approach. The goal here was to determine a possible behavior of our Bacteri'OClock, considering the overall system. We then wished to ground our model on studies, so as to find quickly parameters on which we could work, awaiting for the data we shall get from the wet lab.
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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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* We introduced this approach as being rough, since about every interaction is modelized by linear equations. Two elements motivated this approach :
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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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** Firstly, we argue that what with putting too many parameters, the model tends to loose relevance. We wanted to be able to control most of our parameters in the wet lab.
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** Secondly, we found in the literature that many author had already considered this kind of approach, and were able to obtain relevant results.
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* Let's see a detailed version of our [[Team:Paris/Modeling/linear_approach|Oscillatory Biological Model]] !
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== II - "Hill" Approach ==
 
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* This second approach was motivated by our will to understand our system in the most precise way. We decided to examine each part of our project (Oscillation, FIFO, Synchronization) independantly, and tried to take into account the fundamental kinetics processes.
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<center>[[Team:Paris/Modeling/BOB|Read more]]</center>
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* We hereby use mostly Hill function, hence the name of this approach. We analyzed in the most precise fashion every interaction that took place. The Hill functions are introduced to describe relationships between transcription factors and promoters, since we thought seondary to take into acount the translation phases.
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* Let's go see our [[Team:Paris/Modeling/hill_approach|Hill approach]], which we would like to study as deeply as possible !
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== III - Estimation of Paramaters ==
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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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* 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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** First of all, you can find [[Team:Paris/Modeling/estimation|here]] the description of how we intend to find relevent parameters for our models.
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** We will need many parameters to fully describe the system according to the asumptions of the previous models. A natural way to have access to their value, after looking them up in the litterature, is to devise specific experiments. As a consequence of the characterization of the promoters activity, some Hill functions could be obtained.
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** In a second step we shall try to find a way to qunatify the quality of a model, given the numerical values given by the wet lab. (to come soon...)
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== IV - Parameters & Bibliography ==
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<center> >> [[Team:Paris/Modeling/hill_approach|Explanations and description]] </center>
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* We were naturally inspired by the literature available. You can find [[Team:Paris/Modeling/Bibliography|here]] the references
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* Specific experiments focused on finding relevant parameters have been designed and planned.
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* Since we have diferent models, and since a large stock of parameters is attached to each approach, we propose here a direct access to [[Team:Paris/Modeling/linear approach#Parameters summary|the Linear Approach parameters]] and to [[Team:Paris/Modeling/Parameters|the Hill Approach parameters]].
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<center> >> [[Team:Paris/Modeling/estimation|Estimation]] </center>
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== V - Annexes ==
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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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