Hybrid approaches for multiple-species stochastic reaction-diffusion models.
KAUST Grant NumberKUK-C1-013-04
Online Publication Date2015-07-10
Print Publication Date2015-10
Permanent link to this recordhttp://hdl.handle.net/10754/596791
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AbstractReaction-diffusion models are used to describe systems in fields as diverse as physics, chemistry, ecology and biology. The fundamental quantities in such models are individual entities such as atoms and molecules, bacteria, cells or animals, which move and/or react in a stochastic manner. If the number of entities is large, accounting for each individual is inefficient, and often partial differential equation (PDE) models are used in which the stochastic behaviour of individuals is replaced by a description of the averaged, or mean behaviour of the system. In some situations the number of individuals is large in certain regions and small in others. In such cases, a stochastic model may be inefficient in one region, and a PDE model inaccurate in another. To overcome this problem, we develop a scheme which couples a stochastic reaction-diffusion system in one part of the domain with its mean field analogue, i.e. a discretised PDE model, in the other part of the domain. The interface in between the two domains occupies exactly one lattice site and is chosen such that the mean field description is still accurate there. In this way errors due to the flux between the domains are small. Our scheme can account for multiple dynamic interfaces separating multiple stochastic and deterministic domains, and the coupling between the domains conserves the total number of particles. The method preserves stochastic features such as extinction not observable in the mean field description, and is significantly faster to simulate on a computer than the pure stochastic model.
CitationSpill F, Guerrero P, Alarcon T, Maini PK, Byrne H (2015) Hybrid approaches for multiple-species stochastic reaction–diffusion models. Journal of Computational Physics 299: 429–445. Available: http://dx.doi.org/10.1016/j.jcp.2015.07.002.
SponsorsThis publication was based on work supported in part by Award No KUK-C1-013-04, made by King Abdullah University of Science and Technology (KAUST). TA gratefully acknowledges the Spanish Ministry for Science and Innovation (MICINN) for funding under grant MTM2011-29342 and Generalitat de Catalunya for funding under grant 2009SGR345. PG acknowledges Wellcome Trust [WT098325MA] and Junta de Andalucía Project FQM 954.
JournalJournal of Computational Physics
PubMed Central IDPMC4554296
CollectionsPublications Acknowledging KAUST Support
Except where otherwise noted, this item's license is described as This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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