期刊
JOURNAL OF CHEMICAL PHYSICS
卷 135, 期 23, 页码 -出版社
AMER INST PHYSICS
DOI: 10.1063/1.3668100
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资金
- Institute for Collaborative Biotechnologies through (U.S.) Army Research Office (USARO) [W911NF-09-0001]
- National Institutes of Health (NIH) [5R01EB007511-03]
- (U.S.) Department of Energy (DOE) [DE-FG02-04ER25621]
- National Science Fund (NSF) [DMS-1001012]
- California Institute of Technology [102-1080890]
- National Institute of General Medical Sciences [R01GM078992]
- National Institute of Biomedical Imaging and Bioengineering [82-1083250, R01EB007511]
- University of California at Santa Barbara [054281A20]
In recent years there has been substantial growth in the development of algorithms for characterizing rare events in stochastic biochemical systems. Two such algorithms, the state-dependent weighted stochastic simulation algorithm (swSSA) and the doubly weighted SSA (dwSSA) are extensions of the weighted SSA (wSSA) by H. Kuwahara and I. Mura [J. Chem. Phys. 129, 165101 (2008)]. The swSSA substantially reduces estimator variance by implementing system state-dependent importance sampling (IS) parameters, but lacks an automatic parameter identification strategy. In contrast, the dwSSA provides for the automatic determination of state-independent IS parameters, thus it is inefficient for systems whose states vary widely in time. We present a novel modification of the dwSSA-the state-dependent doubly weighted SSA (sdwSSA)-that combines the strengths of the swSSA and the dwSSA without inheriting their weaknesses. The sdwSSA automatically computes state-dependent IS parameters via the multilevel cross-entropy method. We apply the method to three examples: a reversible isomerization process, a yeast polarization model, and a lac operon model. Our results demonstrate that the sdwSSA offers substantial improvements over previous methods in terms of both accuracy and efficiency. (C) 2011 American Institute of Physics. [doi:10.1063/1.3668100]
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