Team:Paris/Modeling

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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>
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= Our thought process =
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= BOB (Based On Bibliography) Approach =
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[[Image:BOB.jpg|250px|thumb]]
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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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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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* Furthermore, if you wish to have a look at our roadmap. Please click [[Team:Paris/Modeling/Roadmap|here]] !
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== I - Preliminary Approach ==
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The key points of this approach:
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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 introduced this approach as being rough and preliminary, since about every interaction is modelized by linear equations. Three elements motivated this approach :
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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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** Finally, we found really important to build a model on which we could work, awaiting for experimental 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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* 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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* 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) incrementally, and tried to take into account the fundamental kinetics processes.
 
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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 secondary 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 Parameters ==
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<center>[[Team:Paris/Modeling/BOB|Read more]]</center>
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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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= APE (APE Parameters Estimation) Approach=
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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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[[Image:APE.jpg|250px|thumb]]
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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 design specific experiments. As a consequence of the characterization of the promoters activity, some Hill functions could be obtained.
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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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** In a second step, we shall try to find a way to translate the natural noise to our model, given the numerical values obtained in the wet lab.
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*** Firstly, we could compute the standard deviation for each set of points at a given inducer concentration, and to normalize it according to the mean value of the set itself.
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*** The next stage is to get the mean of those normalized sd values at every inducer concentration.
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*** Since the β parameter of the Hill function has a linear influence, it is possible to translate this error directly on the parameter; for example: '''β_err = random('norm',β,err)''' where '''β''' is the estimated value of the parameter and '''err''' is the mean of the normalized standard deviations of the experimental values.
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*** For each cell in the model, we could use such noised values for Vmax parameter, in order to reproduce randomness estimated in the wet lab.
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== IV - Parameters & Bibliography ==
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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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* We were naturally inspired by the literature available. You can find [[Team:Paris/Modeling/Bibliography|here]] the references
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<center> >> [[Team:Paris/Modeling/hill_approach|Explanations and description]] </center>
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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 Preliminary Approach parameters]] and to [[Team:Paris/Modeling/Parameters|the Hill Approach parameters]].
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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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