Numerical Approach to Spatial Deterministic-Stochastic Models Arising in Cell Biology

被引：31

作者：

Schaff, James C. ^{[1
]}

Gao, Fei ^{[1
]}

Li, Ye ^{[1
]}

Novak, Igor L. ^{[1
]}

Slepchenko, Boris M. ^{[1
]}

机构：

[1] Univ Connecticut, Ctr Hlth, Dept Cell Biol, Richard D Berlin Ctr Cell Anal & Modeling, Farmington, CT 06030 USA

来源：

PLOS COMPUTATIONAL BIOLOGY | 2016年 / 12卷 / 12期

基金：

美国国家卫生研究院;

关键词：

REACTION-DIFFUSION SIMULATIONS; VIRTUAL CELL; SOFTWARE ENVIRONMENT; CHEMICAL-REACTIONS; MARKOV-PROCESSES; MASTER EQUATION; DYNAMICS; ALGORITHM; SYSTEMS; EVENTS;

D O I：

10.1371/journal.pcbi.1005236

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Hybrid deterministic-stochastic methods provide an efficient alternative to a fully stochastic treatment of models which include components with disparate levels of stochasticity. However, general-purpose hybrid solvers for spatially resolved simulations of reaction-diffusion systems are not widely available. Here we describe fundamentals of a general-purpose spatial hybrid method. The method generates realizations of a spatially inhomogeneous hybrid system by appropriately integrating capabilities of a deterministic partial differential equation solver with a popular particle-based stochastic simulator, Smoldyn. Rigorous validation of the algorithm is detailed, using a simple model of calcium 'sparks' as a testbed. The solver is then applied to a deterministic-stochastic model of spontaneous emergence of cell polarity. The approach is general enough to be implemented within biologist-friendly software frameworks such as Virtual Cell.

引用

页数：23

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