Document Detail

A hierarchical exact accelerated stochastic simulation algorithm.
MedLine Citation:
PMID:  23231214     Owner:  NLM     Status:  MEDLINE    
A new algorithm, "HiER-leap" (hierarchical exact reaction-leaping), is derived which improves on the computational properties of the ER-leap algorithm for exact accelerated simulation of stochastic chemical kinetics. Unlike ER-leap, HiER-leap utilizes a hierarchical or divide-and-conquer organization of reaction channels into tightly coupled "blocks" and is thereby able to speed up systems with many reaction channels. Like ER-leap, HiER-leap is based on the use of upper and lower bounds on the reaction propensities to define a rejection sampling algorithm with inexpensive early rejection and acceptance steps. But in HiER-leap, large portions of intra-block sampling may be done in parallel. An accept/reject step is used to synchronize across blocks. This method scales well when many reaction channels are present and has desirable asymptotic properties. The algorithm is exact, parallelizable and achieves a significant speedup over the stochastic simulation algorithm and ER-leap on certain problems. This algorithm offers a potentially important step towards efficient in silico modeling of entire organisms.
David Orendorff; Eric Mjolsness
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  The Journal of chemical physics     Volume:  137     ISSN:  1089-7690     ISO Abbreviation:  J Chem Phys     Publication Date:  2012 Dec 
Date Detail:
Created Date:  2012-12-12     Completed Date:  2013-05-30     Revised Date:  2013-07-11    
Medline Journal Info:
Nlm Unique ID:  0375360     Medline TA:  J Chem Phys     Country:  United States    
Other Details:
Languages:  eng     Pagination:  214104     Citation Subset:  IM    
Department of Computer Science, University of California, Irvine, California 92697, USA.
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MeSH Terms
Markov Chains
Models, Chemical*
Reproducibility of Results
Stochastic Processes
Grant Support

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

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