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Jeudi 5 Avril 2012 - Gautier Stoll (Institut Curie - Paris) Print
Written by Pierre PETERLONGO   

Boolean Kinetic Monte-Carlo: application of Gillespie algorithm to Boolean modeling of signaling network

10h30 Salle Aurigny
We propose a refinement of Boolean modeling of signaling network that is intrinsically continuous in time. It is based on continuous time Markov process applied on a Boolean state space. In order to describe the temporal evolution, we explicitly specify the transition rates for each node. For that purpose, we built a grammar that can be seen as a generalization of Boolean equations. The values of transition rates have a natural interpretation: it is the inverse of the time for the transition to occur. Mathematically, this approach can be translated in a set of ordinary differential equations on probability distributions; therefore, it can be seen as an approach in between quantitative and qualitative.We developed a C++ software that is able to simulate such a system by applying Kinetic Monte-Carlo (or Gillespie algorithm) in the Boolean state space. This software computes temporal evolution of probability distributions and can also estimate stationary distributions.Applications of Boolean Kinetic Monte-Carlo have been demonstrated for two published qualitative models: p53 signaling and cell-cycle regulation. Our approach allows to describe kinetic phenomena which were difficult to handle in the original models. In particular, transient effects are represented by time dependent probability distributions, interpretable in terms of cell populations.
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