An adaptive tau-leaping method for stochastic simulations of reaction-diffusion systems

Stochastic modelling is critical for studying many biochemical processes in a cell, in particular when some reacting species have low population numbers. For many such cellular processes the spatial distribution of the molecular species plays a key role. The evolution of spatially heterogeneous bioc...

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Main Authors: Jill M. A. Padgett, Silvana Ilie
Format: Article
Language:English
Published: AIP Publishing LLC 2016-03-01
Series:AIP Advances
Online Access:http://dx.doi.org/10.1063/1.4944952
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spelling doaj-b7043c8eaf774c379cfdafc2bc0ad3942020-11-24T23:39:30ZengAIP Publishing LLCAIP Advances2158-32262016-03-0163035217035217-1910.1063/1.4944952074603ADVAn adaptive tau-leaping method for stochastic simulations of reaction-diffusion systemsJill M. A. Padgett0Silvana Ilie1Department of Mathematics, Ryerson University, Toronto, ON, M5B 2K3, CanadaDepartment of Mathematics, Ryerson University, Toronto, ON, M5B 2K3, CanadaStochastic modelling is critical for studying many biochemical processes in a cell, in particular when some reacting species have low population numbers. For many such cellular processes the spatial distribution of the molecular species plays a key role. The evolution of spatially heterogeneous biochemical systems with some species in low amounts is accurately described by the mesoscopic model of the Reaction-Diffusion Master Equation. The Inhomogeneous Stochastic Simulation Algorithm provides an exact strategy to numerically solve this model, but it is computationally very expensive on realistic applications. We propose a novel adaptive time-stepping scheme for the tau-leaping method for approximating the solution of the Reaction-Diffusion Master Equation. This technique combines effective strategies for variable time-stepping with path preservation to reduce the computational cost, while maintaining the desired accuracy. The numerical tests on various examples arising in applications show the improved efficiency achieved by the new adaptive method.http://dx.doi.org/10.1063/1.4944952
collection DOAJ
language English
format Article
sources DOAJ
author Jill M. A. Padgett
Silvana Ilie
spellingShingle Jill M. A. Padgett
Silvana Ilie
An adaptive tau-leaping method for stochastic simulations of reaction-diffusion systems
AIP Advances
author_facet Jill M. A. Padgett
Silvana Ilie
author_sort Jill M. A. Padgett
title An adaptive tau-leaping method for stochastic simulations of reaction-diffusion systems
title_short An adaptive tau-leaping method for stochastic simulations of reaction-diffusion systems
title_full An adaptive tau-leaping method for stochastic simulations of reaction-diffusion systems
title_fullStr An adaptive tau-leaping method for stochastic simulations of reaction-diffusion systems
title_full_unstemmed An adaptive tau-leaping method for stochastic simulations of reaction-diffusion systems
title_sort adaptive tau-leaping method for stochastic simulations of reaction-diffusion systems
publisher AIP Publishing LLC
series AIP Advances
issn 2158-3226
publishDate 2016-03-01
description Stochastic modelling is critical for studying many biochemical processes in a cell, in particular when some reacting species have low population numbers. For many such cellular processes the spatial distribution of the molecular species plays a key role. The evolution of spatially heterogeneous biochemical systems with some species in low amounts is accurately described by the mesoscopic model of the Reaction-Diffusion Master Equation. The Inhomogeneous Stochastic Simulation Algorithm provides an exact strategy to numerically solve this model, but it is computationally very expensive on realistic applications. We propose a novel adaptive time-stepping scheme for the tau-leaping method for approximating the solution of the Reaction-Diffusion Master Equation. This technique combines effective strategies for variable time-stepping with path preservation to reduce the computational cost, while maintaining the desired accuracy. The numerical tests on various examples arising in applications show the improved efficiency achieved by the new adaptive method.
url http://dx.doi.org/10.1063/1.4944952
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