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Title: Mesoscopic-microscopic spatial stochastic simulation with automatic system partitioning

Abstract

The reaction-diffusion master equation (RDME) is a model that allows for efficient on-lattice simulation of spatially resolved stochastic chemical kinetics. Compared to off-lattice hard-sphere simulations with Brownian dynamics or Green’s function reaction dynamics, the RDME can be orders of magnitude faster if the lattice spacing can be chosen coarse enough. However, strongly diffusion-controlled reactions mandate a very fine mesh resolution for acceptable accuracy. It is common that reactions in the same model differ in their degree of diffusion control and therefore require different degrees of mesh resolution. This renders mesoscopic simulation inefficient for systems with multiscale properties. Mesoscopic-microscopic hybrid methods address this problem by resolving the most challenging reactions with a microscale, off-lattice simulation. However, all methods to date require manual partitioning of a system, effectively limiting their usefulness as “black-box” simulation codes. In this paper, we propose a hybrid simulation algorithm with automatic system partitioning based on indirect a priori error estimates. We demonstrate the accuracy and efficiency of the method on models of diffusion-controlled networks in 3D.

Authors:
 [1];  [1]; ORCiD logo [2]
  1. Uppsala Univ. (Sweden)
  2. Univ. of California, Santa Barbara, CA (United States)
Publication Date:
Research Org.:
Univ. of California, Santa Barbara, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1512930
Alternate Identifier(s):
OSTI ID: 1413550
Grant/Contract Number:  
SC0008975
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

Hellander, Stefan, Hellander, Andreas, and Petzold, Linda. Mesoscopic-microscopic spatial stochastic simulation with automatic system partitioning. United States: N. p., 2017. Web. doi:10.1063/1.5002773.
Hellander, Stefan, Hellander, Andreas, & Petzold, Linda. Mesoscopic-microscopic spatial stochastic simulation with automatic system partitioning. United States. doi:10.1063/1.5002773.
Hellander, Stefan, Hellander, Andreas, and Petzold, Linda. Fri . "Mesoscopic-microscopic spatial stochastic simulation with automatic system partitioning". United States. doi:10.1063/1.5002773. https://www.osti.gov/servlets/purl/1512930.
@article{osti_1512930,
title = {Mesoscopic-microscopic spatial stochastic simulation with automatic system partitioning},
author = {Hellander, Stefan and Hellander, Andreas and Petzold, Linda},
abstractNote = {The reaction-diffusion master equation (RDME) is a model that allows for efficient on-lattice simulation of spatially resolved stochastic chemical kinetics. Compared to off-lattice hard-sphere simulations with Brownian dynamics or Green’s function reaction dynamics, the RDME can be orders of magnitude faster if the lattice spacing can be chosen coarse enough. However, strongly diffusion-controlled reactions mandate a very fine mesh resolution for acceptable accuracy. It is common that reactions in the same model differ in their degree of diffusion control and therefore require different degrees of mesh resolution. This renders mesoscopic simulation inefficient for systems with multiscale properties. Mesoscopic-microscopic hybrid methods address this problem by resolving the most challenging reactions with a microscale, off-lattice simulation. However, all methods to date require manual partitioning of a system, effectively limiting their usefulness as “black-box” simulation codes. In this paper, we propose a hybrid simulation algorithm with automatic system partitioning based on indirect a priori error estimates. We demonstrate the accuracy and efficiency of the method on models of diffusion-controlled networks in 3D.},
doi = {10.1063/1.5002773},
journal = {Journal of Chemical Physics},
number = 23,
volume = 147,
place = {United States},
year = {2017},
month = {12}
}

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