Document Detail


On the origins of approximations for stochastic chemical kinetics.
MedLine Citation:
PMID:  16268689     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
This paper considers the derivation of approximations for stochastic chemical kinetics governed by the discrete master equation. Here, the concepts of (1) partitioning on the basis of fast and slow reactions as opposed to fast and slow species and (2) conditional probability densities are used to derive approximate, partitioned master equations, which are Markovian in nature, from the original master equation. Under different conditions dictated by relaxation time arguments, such approximations give rise to both the equilibrium and hybrid (deterministic or Langevin equations coupled with discrete stochastic simulation) approximations previously reported. In addition, the derivation points out several weaknesses in previous justifications of both the hybrid and equilibrium systems and demonstrates the connection between the original and approximate master equations. Two simple examples illustrate situations in which these two approximate methods are applicable and demonstrate the two methods' efficiencies.
Authors:
Eric L Haseltine; James B Rawlings
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  The Journal of chemical physics     Volume:  123     ISSN:  0021-9606     ISO Abbreviation:  J Chem Phys     Publication Date:  2005 Oct 
Date Detail:
Created Date:  2005-11-04     Completed Date:  2007-07-11     Revised Date:  2007-12-03    
Medline Journal Info:
Nlm Unique ID:  0375360     Medline TA:  J Chem Phys     Country:  United States    
Other Details:
Languages:  eng     Pagination:  164115     Citation Subset:  IM    
Affiliation:
Division of Chemistry and Chemical Engineering 210-41, California Institute of Technology, Pasadena, California 91125, USA.
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Chemistry, Physical / methods*
Computer Simulation
Kinetics
Markov Chains
Models, Chemical
Models, Statistical
Probability
Stochastic Processes
Grant Support
ID/Acronym/Agency:
5T15LM007359/LM/NLM NIH HHS

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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