一种用于反应网络建模的派生马尔可夫过程。

A derived Markov process for modeling reaction networks.

作者信息

Holland John H

机构信息

Department of EECS and Department of Psychology, The University of Michigan, Ann Arbor, MI 48109, USA.

出版信息

Evol Comput. 2003 Winter;11(4):339-62. doi: 10.1162/106365603322519260.

DOI:10.1162/106365603322519260

PMID:14629862

Abstract

A reaction network arises when a set of reactants (chromosomes, chemicals, economic goods, or the like) recombine at specified rates to produce other reactants in the set. When the reactants are characterized in terms of "reactive regions" (schemata, active sites, building blocks), reaction networks can be modeled by classic stochastic urn models. The corresponding Markov processes are specified by matrices that, for realistic problems, are small enough to allow standard matrix operations and Monte Carlo estimates of important properties of the trajectory of the process, such as the expected time to first occurrence of some designated reactant.

摘要

当一组反应物（染色体、化学物质、经济商品等）以特定速率重新组合以产生该组中的其他反应物时，就会出现反应网络。当反应物根据“反应区域”（模式、活性位点、构件）来表征时，反应网络可以用经典的随机瓮模型来建模。相应的马尔可夫过程由矩阵指定，对于实际问题，这些矩阵足够小，以便进行标准矩阵运算和对过程轨迹的重要属性进行蒙特卡罗估计，例如首次出现某个指定反应物的预期时间。