Document Detail


Discrete-time stochastic modeling and simulation of biochemical networks.
MedLine Citation:
PMID:  18499525     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Since inherent randomness in chemically reacting systems is evident, stochastic modeling and simulation are exceedingly important for investigating complex biological networks. Within the most common stochastic approach a network is modeled by a continuous-time Markov chain governed by the chemical master equation. We show how the continuous-time Markov chain can be converted to a stochastically identical discrete-time Markov chain and obtain a discrete-time version of the chemical master equation. Simulating the discrete-time Markov chain is equivalent to the Gillespie algorithm but requires less effort in that it eliminates the generation of exponential random variables. Thus, exactness as possessed by the Gillespie algorithm is preserved while the simulation can be performed more efficiently.
Authors:
Werner Sandmann
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Publication Detail:
Type:  Journal Article     Date:  2008-04-10
Journal Detail:
Title:  Computational biology and chemistry     Volume:  32     ISSN:  1476-928X     ISO Abbreviation:  Comput Biol Chem     Publication Date:  2008 Aug 
Date Detail:
Created Date:  2008-06-30     Completed Date:  2009-02-26     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101157394     Medline TA:  Comput Biol Chem     Country:  England    
Other Details:
Languages:  eng     Pagination:  292-7     Citation Subset:  IM    
Affiliation:
University of Bamberg, Feldkirchenstr. 21, D-96045, Bamberg, Germany. werner.sandmann@uni-bamberg.de
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Biochemical Processes*
Computational Biology / methods*
Computer Simulation*
Markov Chains
Models, Biological*
Stochastic Processes
Time Factors

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


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