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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Published in:The Journal of chemical physics Vol. 130; no. 24; p. 244104
Main Authors: Ramaswamy, Rajesh, González-Segredo, Nélido, Sbalzarini, Ivo F
Format: Journal Article
Language:English
Published: United States 28.06.2009
Subjects:
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
Online Access:Get more information
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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.
AbstractList 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.
Author González-Segredo, Nélido
Ramaswamy, Rajesh
Sbalzarini, Ivo F
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  givenname: Rajesh
  surname: Ramaswamy
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  organization: Institute of Theoretical Computer Science, ETH Zurich, CH-8092 Zurich, Switzerland. rajeshr@ethz.ch
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  givenname: Nélido
  surname: González-Segredo
  fullname: González-Segredo, Nélido
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  givenname: Ivo F
  surname: Sbalzarini
  fullname: Sbalzarini, Ivo F
BackLink https://www.ncbi.nlm.nih.gov/pubmed/19566139$$D View this record in MEDLINE/PubMed
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Snippet We introduce an alternative formulation of the exact stochastic simulation algorithm (SSA) for sampling trajectories of the chemical master equation for a...
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StartPage 244104
SubjectTerms 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
Title A new class of highly efficient exact stochastic simulation algorithms for chemical reaction networks
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