Stochastic Modeling and Statistical Inference of Intrinsic Noise in Gene Regulation System via Chemical Master Equation

TL;DR

This paper reviews the use of chemical master equations to model and infer intrinsic noise in gene regulation, highlighting methods for constructing, approximating, simulating, and analyzing these stochastic models at the single-cell level.

Contribution

It provides a comprehensive overview of CME-based modeling and inference techniques for intrinsic noise in gene regulation, including recent advances and challenges.

Findings

CME effectively captures stochastic gene regulation dynamics.

Various approximation and simulation methods are discussed.

Statistical inference techniques for parameter estimation are summarized.

Abstract

Intrinsic noise, the stochastic cell-to-cell fluctuations in mRNAs and proteins, has been observed and proved to play important roles in cellular systems. Due to the recent development in single-cell-level measurement technology, the studies on intrinsic noise are becoming increasingly popular among scholars. The chemical master equation (CME) has been used to model the evolutions of complex chemical and biological systems since 1940, and are often served as the standard tool for modeling intrinsic noise in gene regulation system. A CME-based model can capture the discrete, stochastic, and dynamical nature of gene regulation system, and may offer casual and physical explanation of the observed data at single-cell level. Nonetheless, the complexity of CME also pose serious challenge for researchers in proposing practical modeling and inference frameworks. In this article, we will review the existing works on the modelings and inference of intrinsic noise in gene regulation system within the framework of CME model. We will explore the principles in constructing a CME model for studying gene regulation system and discuss the popular approximations of CME. Then we will study the simulation simulation methods as well as the analytical and numerical approaches that can be used to obtain solution to a CME model. Finally we will summary the exiting statistical methods that can be used to infer the unknown parameters or structures in CME model using single-cell-level gene expression data.