A new class of highly efficient exact stochastic simulation algorithms for chemical reaction networks

We introduce an alternative formulation of the exact stochastic simulation algorithm (SSA) for sampling trajectories of the chemical master equation for a well-stirred system of coupled chemical reactions. Our formulation is based on factored-out, partial reaction propensities. This novel exact SSA,...

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Veröffentlicht in:The Journal of chemical physics Jg. 130; H. 24; S. 244104
Hauptverfasser: Ramaswamy, Rajesh, González-Segredo, Nélido, Sbalzarini, Ivo F
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 28.06.2009
Schlagworte:
Algorithms
Colloids - chemistry
Computer Simulation
Escherichia coli - genetics
Escherichia coli - metabolism
Escherichia coli Proteins - genetics
Escherichia coli Proteins - metabolism
Heat-Shock Proteins - genetics
Heat-Shock Proteins - metabolism
Models, Biological
Models, Chemical
Stochastic Processes
ISSN:1089-7690
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Zusammenfassung:We introduce an alternative formulation of the exact stochastic simulation algorithm (SSA) for sampling trajectories of the chemical master equation for a well-stirred system of coupled chemical reactions. Our formulation is based on factored-out, partial reaction propensities. This novel exact SSA, called the partial-propensity direct method (PDM), is highly efficient and has a computational cost that scales at most linearly with the number of chemical species, irrespective of the degree of coupling of the reaction network. In addition, we propose a sorting variant, SPDM, which is especially efficient for multiscale reaction networks.
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ISSN:1089-7690
DOI:10.1063/1.3154624