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


Ramaswamy, Rajesh; González-Segredo, Nélido; Sbalzarini, Ivo F (2009). A new class of highly efficient exact stochastic simulation algorithms for chemical reaction networks. The Journal of chemical physics, 130(24):244104.

Abstract

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.

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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25 citations in Web of Science®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:Special Collections > SystemsX.ch
Special Collections > SystemsX.ch > Research, Technology and Development Projects > LipidX
Special Collections > SystemsX.ch > Research, Technology and Development Projects > WingX
Special Collections > SystemsX.ch > Interdisciplinary PhD Projects
Dewey Decimal Classification:570 Life sciences; biology
Date:2009
Deposited On:08 Dec 2010 13:12
Last Modified:05 Apr 2016 14:27
Publisher:UNSPECIFIED
ISSN:0021-9606
Publisher DOI:https://doi.org/10.1063/1.3154624
PubMed ID:19566139
Permanent URL: https://doi.org/10.5167/uzh-39864

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