ArticleJournal of the Royal Society, Interface2012
The two-regime method for optimizing stochastic reaction-diffusion simulations.
Article in Journal of the Royal Society, Interface, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- The Brownian dynamics simulator PyRID for reacting and interacting particles written in Python.Cell reports methods · 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
- A novel stochastic simulation approach enables exploration of mechanisms for regulating polarity site movement.PLoS computational biology · 2021Article
- Incorporating domain growth into hybrid methods for reaction-diffusion systems.Journal of the Royal Society, Interface · 2021Article
- A multiscale model of complex endothelial cell dynamics in early angiogenesis.PLoS computational biology · 2021Article
- Following the footprints of variability during filopodial growth.European biophysics journal : EBJ · 2020Article
- The blending region hybrid framework for the simulation of stochastic reaction-diffusion processes.Journal of the Royal Society, Interface · 2020Article
- Hierarchical algorithm for the reaction-diffusion master equation.The Journal of chemical physics · 2020Article
- Coarse-graining molecular dynamics: stochastic models with non-Gaussian force distributions.Journal of mathematical biology · 2020Article
- Stochastic self-tuning hybrid algorithm for reaction-diffusion systems.The Journal of chemical physics · 2019Article
- Potential based, spatial simulation of dynamically nested particles.BMC bioinformatics · 2019Article
- An implicit lipid model for efficient reaction-diffusion simulations of protein binding to surfaces of arbitrary topology.The Journal of chemical physics · 2019Article
- Multiscale Stochastic Reaction-Diffusion Algorithms Combining Markov Chain Models with Stochastic Partial Differential Equations.Bulletin of mathematical biology · 2019Article
- Multi-resolution dimer models in heat baths with short-range and long-range interactions.Interface focus · 2019Article
- The auxiliary region method: a hybrid method for coupling PDE- and Brownian-based dynamics for reaction-diffusion systems.Royal Society open science · 2018Article
- Spatially extended hybrid methods: a review.Journal of the Royal Society, Interface · 2018Review
- Mesoscopic-microscopic spatial stochastic simulation with automatic system partitioning.The Journal of chemical physics · 2017Article
- Coarse-graining and hybrid methods for efficient simulation of stochastic multi-scale models of tumour growth.Journal of computational physics · 2017Article
- Reaction rates for reaction-diffusion kinetics on unstructured meshes.The Journal of chemical physics · 2017Article
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3 authors.
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Abstract
Spatial organization and noise play an important role in molecular systems biology. In recent years, a number of software packages have been developed for stochastic spatio-temporal simulation, ranging from detailed molecular-based approaches to less detailed compartment-based simulations. Compartment-based approaches yield quick and accurate mesoscopic results, but lack the level of detail that is characteristic of the computationally intensive molecular-based models. Often microscopic detail is only required in a small region (e.g. close to the cell membrane). Currently, the best way to achieve microscopic detail is to use a resource-intensive simulation over the whole domain. We develop the two-regime method (TRM) in which a molecular-based algorithm is used where desired and a compartment-based approach is used elsewhere. We present easy-to-implement coupling conditions which ensure that the TRM results have the same accuracy as a detailed molecular-based model in the whole simulation domain. Therefore, the TRM combines strengths of previously developed stochastic reaction-diffusion software to efficiently explore the behaviour of biological models. Illustrative examples and the mathematical justification of the TRM are also presented.
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