Stochastic chemical kinetics and the total quasi-steady-state assumption: Application to the stochastic simulation algorithm and chemical master equation

MacNamara, Shev, Bersani, Alberto M., Burrage, Kevin and Sidje, Roger B. (2008) Stochastic chemical kinetics and the total quasi-steady-state assumption: Application to the stochastic simulation algorithm and chemical master equation. Journal of Chemical Physics, 129 9: 095105-1-095105-13. doi:10.1063/1.2971036

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
UQ182486_OA.pdf Full text (open access) application/pdf 610.04KB 0

Author MacNamara, Shev
Bersani, Alberto M.
Burrage, Kevin
Sidje, Roger B.
Title Stochastic chemical kinetics and the total quasi-steady-state assumption: Application to the stochastic simulation algorithm and chemical master equation
Journal name Journal of Chemical Physics   Check publisher's open access policy
ISSN 0021-9606
1089-7690
Publication date 2008-09-03
Sub-type Article (original research)
DOI 10.1063/1.2971036
Open Access Status File (Publisher version)
Volume 129
Issue 9
Start page 095105-1
End page 095105-13
Total pages 13
Place of publication College Park, MD, United States
Publisher American Institute of Physics
Language eng
Abstract Recently the application of the quasi-steady-state approximation QSSA to the stochastic simulation algorithm SSA was suggested for the purpose of speeding up stochastic simulations of chemical systems that involve both relatively fast and slow chemical reactions Rao and Arkin, J.Chem. Phys. 118, 4999 2003 and further work has led to the nested and slow-scale SSA. Improved numerical efficiency is obtained by respecting the vastly different time scales characterizing the system and then by advancing only the slow reactions exactly, based on a suitable approximation to the fast reactions. We considerably extend these works by applying the QSSA to numerical methods for the direct solution of the chemical master equation CME and, in particular, to the finite state projection algorithm Munsky and Khammash, J. Chem. Phys. 124, 044104 2006, in conjunction with Krylov methods. In addition, we point out some important connections to the literature on the deterministic total QSSA tQSSA and place the stochastic analogue of the QSSA within the more general framework of aggregation of Markov processes. We demonstrate the new methods on four examples: Michaelis–Menten enzyme kinetics, double phosphorylation, the Goldbeter–Koshland switch, and the mitogen activated protein kinase cascade. Overall, we report dramatic improvements by applying the tQSSA to the CME solver.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Institute for Molecular Bioscience - Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 35 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 43 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Thu, 03 Sep 2009, 09:35:58 EST by Mr Andrew Martlew on behalf of School of Mathematics & Physics