
Jeudi 5 Avril 2012  Gautier Stoll (Institut Curie  Paris) 

Written by Pierre PETERLONGO

Boolean Kinetic MonteCarlo: application of Gillespie algorithm to Boolean modeling of signaling network10h30 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 MonteCarlo (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 MonteCarlo have been
demonstrated for two published qualitative models: p53 signaling and
cellcycle 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.

