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

Journal Article · · Journal of Chemical Physics
DOI: https://doi.org/10.1063/1.5017955 · OSTI ID:1512929
 [1];  [1]
  1. Univ. of Delaware, Newark, DE (United States)
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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

Research Organization:
Univ. of Delaware, Newark, DE (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE
Grant/Contract Number:
SC0010549
OSTI ID:
1512929
Alternate ID(s):
OSTI ID: 1414491
Journal Information:
Journal of Chemical Physics, Vol. 147, Issue 23; ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)Copyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 3 works
Citation information provided by
Web of Science

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Cited By (1)

Generalizing Parallel Replica Dynamics: Trajectory Fragments, Asynchronous Computing, and PDMPs text January 2018

Figures / Tables (20)

Algorithm 1(p. 5)figure Algorithm 1
FIG. 1(p. 7)figure FIG. 1
Table-I(p. 7)table Table-I
FIG. 2(p. 8)figure FIG. 2
FIG. 3(p. 8)figure FIG. 3
FIG. 4(p. 8)figure FIG. 4
FIG. 5(p. 9)figure FIG. 5
TABLE II(p. 9)table TABLE II
TABLE III(p. 9)table TABLE III
TABLE IV(p. 9)table TABLE IV
TABLE V(p. 9)table TABLE V
FIG. 6(p. 10)figure FIG. 6
FIG. 7(p. 10)figure FIG. 7
FIG. 8(p. 11)figure FIG. 8
TABLE VI(p. 11)table TABLE VI
TABLE VII(p. 11)table TABLE VII
TABLE VIII(p. 11)table TABLE VIII
FIG. 10(p. 12)figure FIG. 10
FIG. 9(p. 12)figure FIG. 9
TABLE IX(p. 12)table TABLE IX

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