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Mesoscopic-microscopic spatial stochastic simulation with automatic system partitioning
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
University of California, Department of Computer Science.ORCID iD: 0000-0001-6251-6078
2017 (English)In: Journal of Chemical Physics, ISSN 0021-9606, E-ISSN 1089-7690, Vol. 147, no 23, article id 234101Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2017. Vol. 147, no 23, article id 234101
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Other Chemistry Topics
Identifiers
URN: urn:nbn:se:uu:diva-339766DOI: 10.1063/1.5002773ISI: 000418648200003PubMedID: 29272930OAI: oai:DiVA.org:uu-339766DiVA, id: diva2:1181560
Funder
NIH (National Institute of Health), R01-EB014877Swedish Research Council, 2015-03964eSSENCE - An eScience CollaborationAvailable from: 2018-02-09 Created: 2018-02-09 Last updated: 2018-02-09Bibliographically approved

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The full text will be freely available from 2018-12-21 09:05
Available from 2018-12-21 09:05

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Hellander, StefanHellander, Andreas

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