Symmetry-based model reduction for approximate stochastic analysis

Kirill Batmanov (1) , Celine Kuttler (1) , François Lemaire (2) , Cédric Lhoussaine (1) , Cristian Versari (1)

1 BioComputing - Programming Languages for Biological Modeling and Simulation
2 CALFOR - Calcul Formel

For models of cell-to-cell communication, with many reactions and species per cell, the computational cost of stochastic simulation soon becomes intractable. Deterministic methods, while computationally more efficient, may fail to contribute reliable approximations for those models. In this paper, we suggest a reduction for models of cell-to-cell communication, based on symmetries of the underlying reaction network. To carry out a stochastic analysis that otherwise comes at an excessive computational cost, we apply a moment closure (MC) approach. We illustrate with a community effect, that allows synchronization of a group of cells in animal development. Comparing the results of stochastic simulation with deterministic and MC approximation, we show the assets of our approach. The reduction presented here is potentially applicable to a broad range of highly regular systems.

Mots clés: model reduction; stochastic analysis; moment closure; model symmetry; cell-to-cell communication; community effect

Domaines: Bio-informatique [q-bio.QM]; Bio-Informatique, Biologie Systémique [q-bio.QM]

Kirill Batmanov, Celine Kuttler, François Lemaire, Cédric Lhoussaine, Cristian Versari. Symmetry-based model reduction for approximate stochastic analysis. Computational Methods in Systems Biology 2012 (CMSB 2012), Oct 2012, London, United Kingdom. pp.49-68. ⟨hal-00713386⟩