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Mesoscopic-microscopic spatial stochastic simulation with automatic system partitioning.
J Chem Phys 2017; 147(23):234101JC

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+Show Affiliations

Department of Information Technology, Uppsala University, P.O.Box 337, SE-75105 Uppsala, Sweden.Department of Information Technology, Uppsala University, P.O.Box 337, SE-75105 Uppsala, Sweden.Department of Computer Science, University of California, Santa Barbara, California 93106-5070, USA.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

29272930

Citation

Hellander, Stefan, et al. "Mesoscopic-microscopic Spatial Stochastic Simulation With Automatic System Partitioning." The Journal of Chemical Physics, vol. 147, no. 23, 2017, p. 234101.
Hellander S, Hellander A, Petzold L. Mesoscopic-microscopic spatial stochastic simulation with automatic system partitioning. J Chem Phys. 2017;147(23):234101.
Hellander, S., Hellander, A., & Petzold, L. (2017). Mesoscopic-microscopic spatial stochastic simulation with automatic system partitioning. The Journal of Chemical Physics, 147(23), p. 234101. doi:10.1063/1.5002773.
Hellander S, Hellander A, Petzold L. Mesoscopic-microscopic Spatial Stochastic Simulation With Automatic System Partitioning. J Chem Phys. 2017 Dec 21;147(23):234101. PubMed PMID: 29272930.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Mesoscopic-microscopic spatial stochastic simulation with automatic system partitioning. AU - Hellander,Stefan, AU - Hellander,Andreas, AU - Petzold,Linda, PY - 2017/12/24/entrez PY - 2017/12/24/pubmed PY - 2017/12/24/medline SP - 234101 EP - 234101 JF - The Journal of chemical physics JO - J Chem Phys VL - 147 IS - 23 N2 - 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. SN - 1089-7690 UR - https://www.unboundmedicine.com/medline/citation/29272930/Mesoscopic_microscopic_spatial_stochastic_simulation_with_automatic_system_partitioning_ L2 - https://dx.doi.org/10.1063/1.5002773 DB - PRIME DP - Unbound Medicine ER -