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

机译:细胞生物学中空间确定性随机模型的数值方法

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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.
机译:混合确定性随机方法为模型的完全随机处理提供了一种有效的替代方法,该模型包括具有不同随机性水平的组件。但是,用于反应扩散系统的空间分辨模拟的通用混合求解器并不广泛。在这里,我们描述了通用空间混合方法的基础。该方法通过将确定性偏微分方程求解器的功能与流行的基于粒子的随机仿真器Smoldyn进行适当集成,从而生成空间不均匀混合系统的实现。使用简单的钙“火花”模型作为测试平台,对算法进行了严格的验证。然后将求解器应用于细胞极性自发出现的确定性-随机模型。该方法足够通用,可以在对生物学家友好的软件框架(例如Virtual Cell)中实施。

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