Approximation and inference methods for stochastic biochemical kinetics - a tutorial review

TL;DR

This tutorial review introduces modeling, approximation, and inference techniques for stochastic biochemical kinetics, emphasizing their applications, advantages, limitations, and recent advances in analyzing complex biological systems governed by the Chemical Master Equation.

Contribution

It provides a comprehensive overview of state-of-the-art methods for modeling and inference in stochastic biochemical systems, including new comparisons and recent methodological developments.

Findings

Comparison of approximation methods via numerical case study

Discussion of advantages and disadvantages of various methods

Review of recent Bayesian inference techniques for experimental data

Abstract

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.