Header

UZH-Logo

Maintenance Infos

A partial-propensity variant of the composition-rejection stochastic simulation algorithm for chemical reaction networks.


Ramaswamy, Rajesh; Sbalzarini, Ivo F (2010). A partial-propensity variant of the composition-rejection stochastic simulation algorithm for chemical reaction networks. The Journal of chemical physics, 132(4):044102.

Abstract

We present the partial-propensity stochastic simulation algorithm with composition-rejection sampling (PSSA-CR). It is an exact formulation of the stochastic simulation algorithm (SSA) for well-stirred systems of coupled chemical reactions. The new formulation is a partial-propensity variant [R. Ramaswamy, N. Gonzalez-Segredo, and I. F. Sbalzarini, J. Chem. Phys. 130, 244104 (2009)] of the composition- rejection SSA [A. Slepoy, A. P. Thompson, and S. J. Plimpton, J. Chem. Phys. 128, 205101 (2008)]. The computational cost of this new formulation is bounded by a constant for weakly coupled reaction networks, and it increases at most linearly with the number of chemical species for strongly coupled reaction networks. PSSA-CR thus combines the advantages of partial-propensity methods and the composition-rejection SSA, providing favorable scaling of the computational cost for all classes of reaction networks.

Abstract

We present the partial-propensity stochastic simulation algorithm with composition-rejection sampling (PSSA-CR). It is an exact formulation of the stochastic simulation algorithm (SSA) for well-stirred systems of coupled chemical reactions. The new formulation is a partial-propensity variant [R. Ramaswamy, N. Gonzalez-Segredo, and I. F. Sbalzarini, J. Chem. Phys. 130, 244104 (2009)] of the composition- rejection SSA [A. Slepoy, A. P. Thompson, and S. J. Plimpton, J. Chem. Phys. 128, 205101 (2008)]. The computational cost of this new formulation is bounded by a constant for weakly coupled reaction networks, and it increases at most linearly with the number of chemical species for strongly coupled reaction networks. PSSA-CR thus combines the advantages of partial-propensity methods and the composition-rejection SSA, providing favorable scaling of the computational cost for all classes of reaction networks.

Statistics

Citations

Dimensions.ai Metrics
21 citations in Web of Science®
22 citations in Scopus®
25 citations in Microsoft Academic
Google Scholar™

Altmetrics

Downloads

95 downloads since deposited on 08 Dec 2010
4 downloads since 12 months
Detailed statistics

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 > Research, Technology and Development Projects
Dewey Decimal Classification:570 Life sciences; biology
Date:2010
Deposited On:08 Dec 2010 13:14
Last Modified:17 Feb 2018 17:39
Publisher:UNSPECIFIED
ISSN:0021-9606
OA Status:Green
Publisher DOI:https://doi.org/10.1063/1.3297948
PubMed ID:20113014

Download

Download PDF  'A partial-propensity variant of the composition-rejection stochastic simulation algorithm for chemical reaction networks.'.
Preview
Filetype: PDF
Size: 449kB
View at publisher