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首页> 外文期刊>Journal of the Royal Society Interface >Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art
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Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art

机译：随机生化反应网络的仿真和推理算法：从基本概念到最先进的

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摘要

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 Matlabw implementations. As a result, this review provides a practical and accessible introduction to computational methods for stochastic models within the life sciences community.
机译：随机性是细胞内方法的关键特征，例如基因调控和化学信号传导。因此，表征生化系统中随机效应对于了解生物的复杂动态至关重要。生物化反应系统的数学理想化必须能够捕获随机现象。虽然具有稳健的理论来描述这种随机模型，但探索这些模型的计算挑战可能是实践中的重大负担，因为现实模型是分析棘手的。确定随机生物化学反应网络的预期行为和可变性需要其演化的许多概率模拟。使用生化反应网络模型来帮助解释来自生物学实验的时间课程数据是由于确定观察概率的似函数的难以造成的难以造成的挑战。这些计算挑战已经有超过四十年的积极研究的主题。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理问题相关的主要历史发展和最先进的计算技术提供了可访问的讨论。描述了特别重要的方法的详细算法，并与Matlabw实现辅成。因此，本综述提供了对生命科学社区内随机模型的计算方法进行了实际和可访问的介绍。

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