1 School of Mathematical Sciences, Queensland University of Technology , Brisbane, Queensland 4001 , Australia.
2 Mathematical Institute, University of Oxford , Oxford OX2 6GG , UK.
J R Soc Interface. 2019 Feb 28;16(151):20180943. doi: 10.1098/rsif.2018.0943.
Stochasticity is a key characteristic of intracellular processes such as gene regulation and chemical signalling. Therefore, characterizing stochastic effects in biochemical systems is essential to understand the complex dynamics of living things. Mathematical idealizations of biochemically reacting systems must be able to capture stochastic phenomena. While robust theory exists to describe such stochastic models, the computational challenges in exploring these models can be a significant burden in practice since realistic models are analytically intractable. Determining the expected behaviour and variability of a stochastic biochemical reaction network requires many probabilistic simulations of its evolution. Using a biochemical reaction network model to assist in the interpretation of time-course data from a biological experiment is an even greater challenge due to the intractability of the likelihood function for determining observation probabilities. These computational challenges have been subjects of active research for over four decades. In this review, we present an accessible discussion of the major historical developments and state-of-the-art computational techniques relevant to simulation and inference problems for stochastic biochemical reaction network models. Detailed algorithms for particularly important methods are described and complemented with Matlab implementations. As a result, this review provides a practical and accessible introduction to computational methods for stochastic models within the life sciences community.
随机性是细胞内过程（如基因调控和化学信号传递）的一个关键特征。因此，描述生化系统中的随机效应对于理解生物的复杂动态至关重要。生化反应系统的数学理想化必须能够捕捉随机现象。虽然有强大的理论来描述这些随机模型，但在实践中探索这些模型的计算挑战可能是一个巨大的负担，因为现实模型在分析上是不可处理的。确定随机生化反应网络的预期行为和可变性需要对其演化进行多次概率模拟。由于确定观测概率的似然函数的不可处理性，使用生化反应网络模型来协助解释生物实验的时程数据更是一个巨大的挑战。这些计算挑战已经成为 40 多年来的活跃研究课题。在这篇综述中，我们对与随机生化反应网络模型的模拟和推断问题相关的主要历史发展和最先进的计算技术进行了通俗易懂的讨论。特别重要方法的详细算法被描述，并配以 Matlab 实现。因此，这篇综述为生命科学领域的随机模型计算方法提供了实用和易懂的介绍。
