ArticleBulletin of mathematical biology2019
Multiscale Stochastic Reaction-Diffusion Algorithms Combining Markov Chain Models with Stochastic Partial Differential Equations.
Article in Bulletin of mathematical biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- From homogeneity to heterogeneity: Refining stochastic simulations of gene regulation.Computational and structural biotechnology journal · 2025Article
- Multi-Grid Reaction-Diffusion Master Equation: Applications to Morphogen Gradient Modelling.Bulletin of mathematical biology · 2024Article
- Close agreement between deterministic versus stochastic modeling of first-passage time to vesicle fusion.Biophysical journal · 2022Article
- Incorporating domain growth into hybrid methods for reaction-diffusion systems.Journal of the Royal Society, Interface · 2021Article
- Incorporating age and delay into models for biophysical systems.Physical biology · 2021Article
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2 authors.
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Abstract
Two multiscale algorithms for stochastic simulations of reaction-diffusion processes are analysed. They are applicable to systems which include regions with significantly different concentrations of molecules. In both methods, a domain of interest is divided into two subsets where continuous-time Markov chain models and stochastic partial differential equations (SPDEs) are used, respectively. In the first algorithm, Markov chain (compartment-based) models are coupled with reaction-diffusion SPDEs by considering a pseudo-compartment (also called an overlap or handshaking region) in the SPDE part of the computational domain right next to the interface. In the second algorithm, no overlap region is used. Further extensions of both schemes are presented, including the case of an adaptively chosen boundary between different modelling approaches.
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