Team:Paris/Modeling

From 2008.igem.org

(Difference between revisions)
(Programs)
 
(344 intermediate revisions not shown)
Line 1: Line 1:
-
{{Paris/Menu}}
+
{{:Team:Paris/MenuBackup}}
-
==Roadmap==
 
-
If you want to have a look at our roadmap : [[Team:Paris/Modeling/Roadmap|Roadmap]]
+
= 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 :
 +
<center>[[Team:Paris/Modeling/History|Read more !]]</center>
-
==Bibliography==
+
= BOB (Based On Bibliography) Approach =
 +
[[Image:BOB.jpg|250px|thumb]]
-
In order to choose a proper modeling approach for our system, we have decided to list all the chemical reactions we will take into account. Afterwards, we will find the needed parameters reading articles or devising the required experiments.
+
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.
-
An overview of the work that has to be done can be found here : [[Team:Paris/Modeling/Bibliography|Bibliography]]
+
The key points of this approach:
-
==Programs==
+
* 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.
-
If we want to use the promoters used for the formation of the flagella ( [[Team:Paris/Project|Description of the project]]), we will have to clearlydefined their dynamics. To do so, a rather huge experimental work will be undertaken,  consisting in providing the so-called 'Hill functions' for each promoters.
 
-
Therefore, we have written a little module which can estimate the parameters of the 'Hill functions', even with some noise and few data available.
+
<center>[[Team:Paris/Modeling/BOB|Read more]]</center>
-
The code can be found here : [[Team:Paris/Modeling/Programs|Programs]].
+
 +
= APE (APE Parameters Estimation) Approach=
 +
[[Image:APE.jpg|250px|thumb]]
 +
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.
-
The method we have employed is just based on a least-square optimization. Then, it could be generic enough for many applications and we would be glad to share the code if you feel it could be usefull.
+
* 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.
 +
 
 +
<center> >> [[Team:Paris/Modeling/hill_approach|Explanations and description]] </center>
 +
 
 +
* Specific experiments focused on finding relevant parameters have been designed and planned.
 +
 
 +
<center> >> [[Team:Paris/Modeling/estimation|Estimation]] </center>
 +
 
 +
= Old but still usefull pages =
 +
 
 +
*[[Team:Paris/Modeling/Bibliography|Bibliographic References]]
 +
*[[Team:Paris/Modeling/linear_approach|Preliminary approach]]
 +
*[[Team:Paris/Modeling/Roadmap|Roadmap]]

Latest revision as of 04:46, 30 October 2008

You are currently on the Wiki Museum
Go back to the normal Wiki


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