ArticleThe Journal of chemical physics2019
Stochastic self-tuning hybrid algorithm for reaction-diffusion systems.
Article in The Journal of chemical physics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A novel stochastic simulation approach enables exploration of mechanisms for regulating polarity site movement.PLoS computational biology · 2021Article
- Geant4-DNA Modeling of Water Radiolysis beyond the Microsecond: An On-Lattice Stochastic Approach.International journal of molecular sciences · 2021Article
- Hybrid models of chemotaxis with application to leukocyte migration.Journal of mathematical biology · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
Many biochemical phenomena involve reactants with vastly different concentrations, some of which are amenable to continuum-level descriptions, while the others are not. We present a hybrid self-tuning algorithm to model such systems. The method combines microscopic (Brownian) dynamics for diffusion with mesoscopic (Gillespie-type) methods for reactions and remains efficient in a wide range of regimes and scenarios with large variations of concentrations. Its accuracy, robustness, and versatility are balanced by redefining propensities and optimizing the mesh size and time step. We use a bimolecular reaction to demonstrate the potential of our method in a broad spectrum of scenarios: from almost completely reaction-dominated systems to cases where reactions rarely occur or take place very slowly. The simulation results show that the number of particles present in the system does not degrade the performance of our method. This makes it an accurate and computationally efficient tool to model complex multireaction systems.
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Registered trials
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