Golightly Andrew, Gillespie Colin S
School of Mathematics Statistics, Newcastle University, Newcastle upon Tyne, UK.
Methods Mol Biol. 2013;1021:169-87. doi: 10.1007/978-1-62703-450-0_9.
A growing realization of the importance of stochasticity in cell and molecular processes has stimulated the need for statistical models that incorporate intrinsic (and extrinsic) variability. In this chapter we consider stochastic kinetic models of reaction networks leading to a Markov jump process representation of a system of interest. Traditionally, the stochastic model is characterized by a chemical master equation. While the intractability of such models can preclude a direct analysis, simulation can be straightforward and may present the only practical approach to gaining insight into a system's dynamics. We review exact simulation procedures before considering some efficient approximate alternatives.
对细胞和分子过程中随机性重要性的日益认识,激发了对纳入内在(和外在)变异性的统计模型的需求。在本章中,我们考虑反应网络的随机动力学模型,从而得到感兴趣系统的马尔可夫跳跃过程表示。传统上,随机模型由化学主方程来表征。虽然这类模型的难解性可能妨碍直接分析,但模拟可能很直接,并且可能是深入了解系统动力学的唯一实用方法。在考虑一些有效的近似替代方法之前,我们先回顾精确模拟程序。