Gene regulatory networks: a primer in biological processes and statistical modelling

TL;DR

This paper provides an overview of gene regulatory networks, detailing biological processes and statistical models used to represent and simulate gene expression regulation for network inference testing.

Contribution

It offers a comprehensive introduction to biological mechanisms and statistical modeling approaches for gene regulatory networks, including simulation techniques for benchmarking.

Findings

Detailed description of gene expression regulation processes

Discussion of topological and stochastic modeling frameworks

Provision of simulation methods for network inference benchmarking

Abstract

Modelling gene regulatory networks not only requires a thorough understanding of the biological system depicted but also the ability to accurately represent this system from a mathematical perspective. Throughout this chapter, we aim to familiarise the reader with the biological processes and molecular factors at play in the process of gene expression regulation.We first describe the different interactions controlling each step of the expression process, from transcription to mRNA and protein decay. In the second section, we provide statistical tools to accurately represent this biological complexity in the form of mathematical models. Amongst other considerations, we discuss the topological properties of biological networks, the application of deterministic and stochastic frameworks and the quantitative modelling of regulation. We particularly focus on the use of such models for the simulation of expression data that can serve as a benchmark for the testing of network inference algorithms.