skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

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

Authors:
 [1];  [1]
  1. Department of Mathematical Sciences, University of Delaware, Newark, Delaware 19716, USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1414491
Grant/Contract Number:
SC0010549
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 147; Journal Issue: 23; Related Information: CHORUS Timestamp: 2018-02-14 20:22:52; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics
Country of Publication:
United States
Language:
English

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. 2017. "Parallel replica dynamics method for bistable stochastic reaction networks: Simulation and sensitivity analysis". United States. doi:10.1063/1.5017955.
@article{osti_1414491,
title = {Parallel replica dynamics method for bistable stochastic reaction networks: Simulation and sensitivity analysis},
author = {Wang, Ting and Plecháč, Petr},
abstractNote = {},
doi = {10.1063/1.5017955},
journal = {Journal of Chemical Physics},
number = 23,
volume = 147,
place = {United States},
year = 2017,
month =
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on December 21, 2018
Publisher's Accepted Manuscript

Save / Share:
  • Deposition of solid material from solution is ubiquitous in nature. However, due to the inherent complexity of such systems, this process is comparatively much less understood than deposition from a gas or vacuum. Further, the accurate atomistic modeling of such systems is computationally expensive, therefore leaving many intriguing long-timescale phenomena out of reach. We present an atomistic/continuum hybrid method for extending the simulation timescales of dynamics at solid/liquid interfaces. We demonstrate the method by simulating the deposition of Ag on Ag (001) from solution with a significant speedup over standard MD. The results reveal specific features of diffusive deposition dynamics,more » such as a dramatic increase in the roughness of the film.« less
  • Cited by 3
  • Traditional moment-closure methods need to assume that high-order cumulants of a probability distribution approximate to zero. However, this strong assumption is not satisfied for many biochemical reaction networks. Here, we introduce convergent moments (defined in mathematics as the coefficients in the Taylor expansion of the probability-generating function at some point) to overcome this drawback of the moment-closure methods. As such, we develop a new analysis method for stochastic chemical kinetics. This method provides an accurate approximation for the master probability equation (MPE). In particular, the connection between low-order convergent moments and rate constants can be more easily derived in termsmore » of explicit and analytical forms, allowing insights that would be difficult to obtain through direct simulation or manipulation of the MPE. In addition, it provides an accurate and efficient way to compute steady-state or transient probability distribution, avoiding the algorithmic difficulty associated with stiffness of the MPE due to large differences in sizes of rate constants. Applications of the method to several systems reveal nontrivial stochastic mechanisms of gene expression dynamics, e.g., intrinsic fluctuations can induce transient bimodality and amplify transient signals, and slow switching between promoter states can increase fluctuations in spatially heterogeneous signals. The overall approach has broad applications in modeling, analysis, and computation of complex biochemical networks with intrinsic noise.« less
  • Although molecular-dynamics simulations can be parallelized effectively to treat large systems (10{sup 6}{endash}10{sup 8} atoms), to date the power of parallel computers has not been harnessed to make analogous gains in {ital time} scale. I present a simple approach for infrequent-event systems that extends the time scale with high parallel efficiency. Integrating a replica of the system independently on each processor until the first transition occurs gives the correct transition-time distribution, and hence the correct dynamics. I obtain {gt}90{percent} efficiency simulating Cu(100) surface vacancy diffusion on 15 processors. {copyright} {ital 1998} {ital The American Physical Society}