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系统生物学中马尔可夫模型的家族树。

Family tree of Markov models in systems biology.

作者信息

Ullah M, Wolkenhauer O

机构信息

Systems Biology and Bioinformatics Group, University of Rostock, A.-Einstein-Str. 21, 18051 Rostock, Germany.

出版信息

IET Syst Biol. 2007 Jul;1(4):247-54. doi: 10.1049/iet-syb:20070017.

Abstract

Motivated by applications in systems biology, a probabilistic framework based on Markov processes is proposed to represent intracellular processes. The formal relationships between different stochastic models referred to in the systems biology literature are reviewed. As part of this review, a novel derivation of the differential Chapman-Kolmogorov equation for a general multidimensional Markov process made up of both continuous and jump processes, is presented. First, the definition of a time-derivative for a probability density is focused, but placing no restrictions on the probability distribution, in particular, it is not assumed to be to be confined to a region that has a surface (on which the probability is zero). In this derivation, the master equation gives the jump part of the Markov process and the Fokker-Planck equation gives the continuous part. As a result, a 'family tree' for stochastic models in systems biology is sketched, providing explicit derivations of their formal relationship and clarifying assumptions involved.

摘要

受系统生物学应用的推动,提出了一种基于马尔可夫过程的概率框架来表示细胞内过程。回顾了系统生物学文献中提到的不同随机模型之间的形式关系。作为该综述的一部分,给出了由连续过程和跳跃过程组成的一般多维马尔可夫过程的微分查普曼 - 柯尔莫哥洛夫方程的一种新颖推导。首先,重点关注概率密度的时间导数的定义,且不对概率分布施加任何限制,特别是不假定其局限于具有表面(概率在该表面上为零)的区域。在此推导中,主方程给出马尔可夫过程的跳跃部分,福克 - 普朗克方程给出连续部分。结果,勾勒出了系统生物学中随机模型的“家族树”,提供了它们形式关系的明确推导并阐明了其中涉及的假设。

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