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Multivariate Normal Approximation for the Stochastic Simulation Algorithm: limit theorem and applications

Vincent Picard 1 Anne Siegel 1 Jérémie Bourdon 2
1 Dyliss - Dynamics, Logics and Inference for biological Systems and Sequences
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
2 Combi
LINA - Laboratoire d'Informatique de Nantes Atlantique
Abstract : Stochastic approaches in systems biology are being used increasingly to model the heterogeneity and the intrinsic stochasticity of living systems, especially at the single-cell level. The stochastic simulation algo-rithm – also known as the Gillespie algorithm – is currently the most widely used method to simulate the time course of a system of bio-chemical reactions in a stochastic way. In this article, we present a central limit theorem for the Gillespie stochastic trajectories when the living system has reached a steady-state, that is when the internal bio-molecules concentrations are assumed to be at equilibrium. It appears that the stochastic behavior in steady-state is entirely characterized by the stoichiometry matrix of the system and a single vector of reaction probabilities. We propose several applications of this result such as deriving multivariate confidence regions for the time course of the system and a constraints-based approach which extends the flux balance analysis framework to the stochastic case.
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Vincent Picard, Anne Siegel, Jérémie Bourdon. Multivariate Normal Approximation for the Stochastic Simulation Algorithm: limit theorem and applications. SASB - 5th International Workshop on Static Analysis and Systems Biology, 2014, Munchen, Germany. ⟨hal-01079768⟩

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