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Title: Parallel replica dynamics method for bistable stochastic reaction networks: Simulation and sensitivity analysis

Abstract

Stochastic reaction networks that exhibit bistable behavior are common in systems biology, materials science, and catalysis. Sampling of stationary distributions is crucial for understanding and characterizing the long-time dynamics of bistable stochastic dynamical systems. However, simulations are often hindered by the insufficient sampling of rare transitions between the two metastable regions. In this paper, we apply the parallel replica method for a continuous time Markov chain in order to improve sampling of the stationary distribution in bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions. Furthermore, it can be combined with the path-space information bounds for parametric sensitivity analysis. With the proposed methodology, we study three bistable biological networks: the Schlögl model, the genetic switch network, and the enzymatic futile cycle network. We demonstrate the algorithmic speedup achieved in these numerical benchmarks. More significant acceleration is expected when multi-core or graphics processing unit computer architectures and programming tools such as CUDA are employed

Authors:
 [1];  [1]
  1. Univ. of Delaware, Newark, DE (United States)
Publication Date:
Research Org.:
Univ. of Delaware, Newark, DE (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1512929
Alternate Identifier(s):
OSTI ID: 1414491
Grant/Contract Number:  
SC0010549
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 147; Journal Issue: 23; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Wang, Ting, and Plecháč, Petr. Parallel replica dynamics method for bistable stochastic reaction networks: Simulation and sensitivity analysis. United States: N. p., 2017. Web. doi:10.1063/1.5017955.
Wang, Ting, & Plecháč, Petr. Parallel replica dynamics method for bistable stochastic reaction networks: Simulation and sensitivity analysis. United States. doi:10.1063/1.5017955.
Wang, Ting, and Plecháč, Petr. Thu . "Parallel replica dynamics method for bistable stochastic reaction networks: Simulation and sensitivity analysis". United States. doi:10.1063/1.5017955. https://www.osti.gov/servlets/purl/1512929.
@article{osti_1512929,
title = {Parallel replica dynamics method for bistable stochastic reaction networks: Simulation and sensitivity analysis},
author = {Wang, Ting and Plecháč, Petr},
abstractNote = {Stochastic reaction networks that exhibit bistable behavior are common in systems biology, materials science, and catalysis. Sampling of stationary distributions is crucial for understanding and characterizing the long-time dynamics of bistable stochastic dynamical systems. However, simulations are often hindered by the insufficient sampling of rare transitions between the two metastable regions. In this paper, we apply the parallel replica method for a continuous time Markov chain in order to improve sampling of the stationary distribution in bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions. Furthermore, it can be combined with the path-space information bounds for parametric sensitivity analysis. With the proposed methodology, we study three bistable biological networks: the Schlögl model, the genetic switch network, and the enzymatic futile cycle network. We demonstrate the algorithmic speedup achieved in these numerical benchmarks. More significant acceleration is expected when multi-core or graphics processing unit computer architectures and programming tools such as CUDA are employed},
doi = {10.1063/1.5017955},
journal = {Journal of Chemical Physics},
number = 23,
volume = 147,
place = {United States},
year = {2017},
month = {12}
}

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