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Accelerated stochastic and hybrid methods for spatial simulations of reaction-diffusion systems


Rossinelli, D; Bayati, B; Koumoutsakos, P (2008). Accelerated stochastic and hybrid methods for spatial simulations of reaction-diffusion systems. Chemical Physics Letters, 451(1-3):136 - 140.

Abstract

Spatial distributions characterize the evolution of reaction-diffusion models of several physical, chemical, and biological systems. We present two novel algorithms for the efficient simulation of these models: Spatial т-Leaping (Sт -Leaping), employing a unified acceleration of the stochastic simulation of reaction and diffusion, and Hybrid т-Leaping (Hт-Leaping), combining a deterministic diffusion approximation with a т-Leaping acceleration of the stochastic reactions. The algorithms are validated by solving Fisher’s equation and used to explore the role of the number of particles in pattern formation. The results indicate that the present algorithms have a nearly constant time complexity with respect to the number of events (reaction and diffusion), unlike the exact stochastic simulation algorithm which scales linearly.

Spatial distributions characterize the evolution of reaction-diffusion models of several physical, chemical, and biological systems. We present two novel algorithms for the efficient simulation of these models: Spatial т-Leaping (Sт -Leaping), employing a unified acceleration of the stochastic simulation of reaction and diffusion, and Hybrid т-Leaping (Hт-Leaping), combining a deterministic diffusion approximation with a т-Leaping acceleration of the stochastic reactions. The algorithms are validated by solving Fisher’s equation and used to explore the role of the number of particles in pattern formation. The results indicate that the present algorithms have a nearly constant time complexity with respect to the number of events (reaction and diffusion), unlike the exact stochastic simulation algorithm which scales linearly.

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23 citations in Web of Science®
25 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:Special Collections > SystemsX.ch
Special Collections > SystemsX.ch > Research, Technology and Development Projects > WingX
Special Collections > SystemsX.ch > Research, Technology and Development Projects
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2008
Deposited On:08 Dec 2010 13:57
Last Modified:05 Apr 2016 14:27
Publisher:Elsevier
ISSN:0009-2614
Publisher DOI:10.1016/j.cplett.2007.11.055
Related URLs:http://www.sciencedirect.com/science/article/B6TFN-4R6JP0V-2/2/759c4e47669d2f18db307eb0e8ebd51e (Publisher)
Permanent URL: http://doi.org/10.5167/uzh-39867

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