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.
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页数：23
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