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无结构网格上反应-扩散动力学的反应速率。

Reaction rates for reaction-diffusion kinetics on unstructured meshes.

机构信息

Department of Computer Science, University of California, Santa Barbara, California 93106-5070, USA.

出版信息

J Chem Phys. 2017 Feb 14;146(6):064101. doi: 10.1063/1.4975167.

Abstract

The reaction-diffusion master equation is a stochastic model often utilized in the study of biochemical reaction networks in living cells. It is applied when the spatial distribution of molecules is important to the dynamics of the system. A viable approach to resolve the complex geometry of cells accurately is to discretize space with an unstructured mesh. Diffusion is modeled as discrete jumps between nodes on the mesh, and the diffusion jump rates can be obtained through a discretization of the diffusion equation on the mesh. Reactions can occur when molecules occupy the same voxel. In this paper, we develop a method for computing accurate reaction rates between molecules occupying the same voxel in an unstructured mesh. For large voxels, these rates are known to be well approximated by the reaction rates derived by Collins and Kimball, but as the mesh is refined, no analytical expression for the rates exists. We reduce the problem of computing accurate reaction rates to a pure preprocessing step, depending only on the mesh and not on the model parameters, and we devise an efficient numerical scheme to estimate them to high accuracy. We show in several numerical examples that as we refine the mesh, the results obtained with the reaction-diffusion master equation approach those of a more fine-grained Smoluchowski particle-tracking model.

摘要

反应扩散主方程是一种常用于研究活细胞中生化反应网络的随机模型。当分子的空间分布对系统的动态很重要时,就会应用该模型。一种可行的方法是使用非结构化网格对细胞的复杂几何形状进行离散化,以准确地解决问题。扩散被建模为在网格节点之间的离散跳跃,并且可以通过在网格上对扩散方程进行离散化来获得扩散跳跃率。当分子占据相同的体素时,就会发生反应。在本文中,我们开发了一种在非结构化网格中计算占据相同体素的分子之间的准确反应速率的方法。对于大的体素,这些速率可以通过柯林斯和金博尔推导出的反应速率很好地近似,但随着网格的细化,不存在这些速率的解析表达式。我们将计算准确反应速率的问题简化为一个纯粹的预处理步骤,仅取决于网格而不取决于模型参数,并设计了一种有效的数值方案来高精度地估计它们。我们在几个数值示例中表明,随着网格的细化,使用反应扩散主方程方法得到的结果与更细粒度的斯莫卢霍夫斯基粒子跟踪模型的结果非常接近。

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