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Title: Scaling methods for accelerating kinetic Monte Carlo simulations of chemical reaction networks

Abstract

Various kinetic Monte Carlo algorithms become inefficient when some of the population sizes in a system are large, which gives rise to a large number of reaction events per unit time. In this work, we present a new acceleration algorithm based on adaptive and heterogeneous scaling of reaction rates and stoichiometric coefficients. The algorithm is conceptually related to the commonly used idea of accelerating a stochastic simulation by considering a subvolume λΩ (0 < λ < 1) within a system of interest, which reduces the number of reaction events per unit time occurring in a simulation by a factor 1/λ at the cost of greater error in unbiased estimates of first moments and biased overestimates of second moments. Our new approach offers two unique benefits. First, scaling is adaptive and heterogeneous, which eliminates the pitfall of overaggressive scaling. Second, there is no need for an a priori classification of populations as discrete or continuous (as in a hybrid method), which is problematic when discreteness of a chemical species changes during a simulation. The method requires specification of only a single algorithmic parameter, N c, a global critical population size above which populations are effectively scaled down to increase simulation efficiency.more » The method, which we term partial scaling, is implemented in the open-source BioNetGen software package. We demonstrate that partial scaling can significantly accelerate simulations without significant loss of accuracy for several published models of biological systems. Finally, these models characterize activation of the mitogen-activated protein kinase ERK, prion protein aggregation, and T-cell receptor signaling.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1544696
Alternate Identifier(s):
OSTI ID: 1529038
Report Number(s):
LA-UR-19-22745
Journal ID: ISSN 0021-9606
Grant/Contract Number:  
89233218CNA000001; Center for Nonlinear Studies
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 150; Journal Issue: 24; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Lin, Yen Ting, Feng, Song, and Hlavacek, William Scott. Scaling methods for accelerating kinetic Monte Carlo simulations of chemical reaction networks. United States: N. p., 2019. Web. doi:10.1063/1.5096774.
Lin, Yen Ting, Feng, Song, & Hlavacek, William Scott. Scaling methods for accelerating kinetic Monte Carlo simulations of chemical reaction networks. United States. doi:10.1063/1.5096774.
Lin, Yen Ting, Feng, Song, and Hlavacek, William Scott. Mon . "Scaling methods for accelerating kinetic Monte Carlo simulations of chemical reaction networks". United States. doi:10.1063/1.5096774.
@article{osti_1544696,
title = {Scaling methods for accelerating kinetic Monte Carlo simulations of chemical reaction networks},
author = {Lin, Yen Ting and Feng, Song and Hlavacek, William Scott},
abstractNote = {Various kinetic Monte Carlo algorithms become inefficient when some of the population sizes in a system are large, which gives rise to a large number of reaction events per unit time. In this work, we present a new acceleration algorithm based on adaptive and heterogeneous scaling of reaction rates and stoichiometric coefficients. The algorithm is conceptually related to the commonly used idea of accelerating a stochastic simulation by considering a subvolume λΩ (0 < λ < 1) within a system of interest, which reduces the number of reaction events per unit time occurring in a simulation by a factor 1/λ at the cost of greater error in unbiased estimates of first moments and biased overestimates of second moments. Our new approach offers two unique benefits. First, scaling is adaptive and heterogeneous, which eliminates the pitfall of overaggressive scaling. Second, there is no need for an a priori classification of populations as discrete or continuous (as in a hybrid method), which is problematic when discreteness of a chemical species changes during a simulation. The method requires specification of only a single algorithmic parameter, Nc, a global critical population size above which populations are effectively scaled down to increase simulation efficiency. The method, which we term partial scaling, is implemented in the open-source BioNetGen software package. We demonstrate that partial scaling can significantly accelerate simulations without significant loss of accuracy for several published models of biological systems. Finally, these models characterize activation of the mitogen-activated protein kinase ERK, prion protein aggregation, and T-cell receptor signaling.},
doi = {10.1063/1.5096774},
journal = {Journal of Chemical Physics},
number = 24,
volume = 150,
place = {United States},
year = {2019},
month = {6}
}

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