Corrigendum: Approximation and inference methods for stochastic biochemical kinetics—a tutorial review (2017 J. Phys. A: Math. Theor. 50 093001)
Corrigendum: Approximation and inference methods for stochastic biochemical kinetics—a tutorial review (2017 J. Phys. A: Math. Theor. 50 093001)
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勘误表：随机生化动力学的近似和推理方法——教程回顾（2017 J. Phys. A: Math. Theor. 50 093001）
DOI：
10.1088/1751-8121/aab38b
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
R. Grima
中科院分区：
文献类型：
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作者：
David Schnoerr;G. Sanguinetti;R. Grima
分子数的随机涨落在生物系统中普遍存在。重要的例子包括活细胞中的基因表达和酶促过程。这些系统通常被建模为化学反应网络，其动力学由化学主方程控制。尽管它的结构简单，但对于大多数系统来说，化学主方程没有解析解。此外，随机模拟在计算上是昂贵的，使得系统分析和统计推断成为一项具有挑战性的任务。因此，近几十年来，人们在开发有效的近似和推理方法上花费了大量的努力。本文介绍了基本的建模概念以及最先进的方法的概述。首先，我们激励和介绍化学网络建模的确定性和随机性方法，并给出了模拟和精确解方法的概述。接下来，我们讨论了几种近似方法，包括化学Langevin方程，系统尺寸展开，矩封闭近似，时间尺度分离近似和混合方法。我们讨论了它们的各种属性，并回顾了这些方法的最新进展和剩余的挑战。我们提出了一个比较这些方法的数值案例研究的手段，并突出了各自的优点和缺点。最后，我们讨论了贝叶斯框架中的实验数据推断的问题，并回顾了最近的方法开发的文献。总之，这篇评论给出了一个独立的介绍随机化学动力学的建模，近似和推理方法。
Stochastic fluctuations of molecule numbers are ubiquitous in biological systems. Important examples include gene expression and enzymatic processes in living cells. Such systems are typically modelled as chemical reaction networks whose dynamics are governed by the chemical master equation. Despite its simple structure, no analytic solutions to the chemical master equation are known for most systems. Moreover, stochastic simulations are computationally expensive, making systematic analysis and statistical inference a challenging task. Consequently, significant effort has been spent in recent decades on the development of efficient approximation and inference methods. This article gives an introduction to basic modelling concepts as well as an overview of state of the art methods. First, we motivate and introduce deterministic and stochastic methods for modelling chemical networks, and give an overview of simulation and exact solution methods. Next, we discuss several approximation methods, including the chemical Langevin equation, the system size expansion, moment closure approximations, time-scale separation approximations and hybrid methods. We discuss their various properties and review recent advances and remaining challenges for these methods. We present a comparison of several of these methods by means of a numerical case study and highlight some of their respective advantages and disadvantages. Finally, we discuss the problem of inference from experimental data in the Bayesian framework and review recent methods developed the literature. In summary, this review gives a self-contained introduction to modelling, approximations and inference methods for stochastic chemical kinetics.