Team:Paris/Modeling
From 2008.igem.org
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* Simplicity for itself is not that important. In fact, what we were looking for was understandability at first. | * 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. | * 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 | + | * 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. |
Revision as of 19:25, 29 August 2008
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. BOB (Based On Bibliography) ApproachOur 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:
APE (Approach with Parameters Estimation)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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