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Automatic reduction of stochastic rules-based models in a nutshell

Ferdinanda Camporesi 1, 2 Jérôme Feret 1, * Heinz Koeppl 3 Tatjana Petrov 3 
* Corresponding author
1 ABSTRACTION - Abstract Interpretation and Static Analysis
DI-ENS - Département d'informatique - ENS Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR 8548
Abstract : Molecular biological models usually suffer from a large combinatorial explosion. Indeed, proteins form complexes and modify each other, which leads to the formation of a huge number of distinct chemical species. Thus we cannot generate explicitly the quantitative semantics of these models, and it is even harder to compute their properties. In this extended abstract, we summarize a framework for reducing the combinatorial complexity of models of biochemical networks. We use rules-based languages to describe the interactions between proteins. Then we compile these models into continuous-time Markov chains. Finally, we use backward bisimulations in order to reduce the dimension of the state space of these Markov chains. More specifically, these backward bisimulations are defined thanks to an abstraction of the control flow of information within chemical species and thanks to an algorithm which detects which protein sites have the same capabilities of interaction.
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https://hal.inria.fr/inria-00527548
Contributor : Jérôme Feret Connect in order to contact the contributor
Submitted on : Tuesday, October 19, 2010 - 3:32:29 PM
Last modification on : Thursday, March 17, 2022 - 10:08:35 AM

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Ferdinanda Camporesi, Jérôme Feret, Heinz Koeppl, Tatjana Petrov. Automatic reduction of stochastic rules-based models in a nutshell. International Conference of Numerical Analysis and Applied Mathematics - ICNAAM 2010, Sep 2010, Rhodos, Greece. pp.1330-1334, ⟨10.1063/1.3497965⟩. ⟨inria-00527548⟩

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