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用于溶液和表面生物反应扩散系统的快速蒙特卡罗模拟方法

FAST MONTE CARLO SIMULATION METHODS FOR BIOLOGICAL REACTION-DIFFUSION SYSTEMS IN SOLUTION AND ON SURFACES.

作者信息

Kerr Rex A, Bartol Thomas M, Kaminsky Boris, Dittrich Markus, Chang Jen-Chien Jack, Baden Scott B, Sejnowski Terrence J, Stiles Joel R

机构信息

HHMI Janelia Farm Research Campus, Ashburn, VA 20147 and Computational Neurobiology Laboratory, The Salk Institute, La Jolla, CA 92037. This author was supported by grants NIH R01 GM069630, NIH P01-NS044306, NSF PHY-0216576, and PHY-0225630 and by HHMI.

出版信息

SIAM J Sci Comput. 2008 Oct 13;30(6):3126. doi: 10.1137/070692017.

Abstract

Many important physiological processes operate at time and space scales far beyond those accessible to atom-realistic simulations, and yet discrete stochastic rather than continuum methods may best represent finite numbers of molecules interacting in complex cellular spaces. We describe and validate new tools and algorithms developed for a new version of the MCell simulation program (MCell3), which supports generalized Monte Carlo modeling of diffusion and chemical reaction in solution, on surfaces representing membranes, and combinations thereof. A new syntax for describing the spatial directionality of surface reactions is introduced, along with optimizations and algorithms that can substantially reduce computational costs (e.g., event scheduling, variable time and space steps). Examples for simple reactions in simple spaces are validated by comparison to analytic solutions. Thus we show how spatially realistic Monte Carlo simulations of biological systems can be far more cost-effective than often is assumed, and provide a level of accuracy and insight beyond that of continuum methods.

摘要

许多重要的生理过程发生的时间和空间尺度远远超出了原子级真实模拟所能达到的范围,然而离散随机方法而非连续介质方法可能最适合描述有限数量的分子在复杂细胞空间中的相互作用。我们描述并验证了为新版MCell模拟程序(MCell3)开发的新工具和算法,该程序支持对溶液中、代表膜的表面上以及二者组合情况下的扩散和化学反应进行广义蒙特卡罗建模。引入了一种用于描述表面反应空间方向性的新语法,以及能够大幅降低计算成本的优化方法和算法(例如事件调度、可变时间和空间步长)。通过与解析解比较,验证了简单空间中简单反应的示例。因此,我们展示了生物系统的空间真实蒙特卡罗模拟如何能够比通常认为的更具成本效益,并提供超越连续介质方法的准确性和洞察力。

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First-passage Monte Carlo algorithm: diffusion without all the hops.首过蒙特卡罗算法:无所有跳跃的扩散。
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