Non-equilibrium dynamics of gene expression and the Jarzynski equality

TL;DR

This paper explores the application of nonequilibrium statistical mechanics, specifically the Jarzynski equality, to model gene expression dynamics driven by fluctuating transcription factors, offering new insights into gene regulation.

Contribution

It introduces a novel mapping between gene expression models and stochastic systems out of equilibrium, applying nonequilibrium theorems to biological gene regulation.

Findings

Demonstrates the applicability of the Jarzynski equality to gene expression dynamics

Provides a method to infer gene regulatory interactions from experimental data

Links stochastic gene expression models with principles of nonequilibrium physics

Abstract

In order to express specific genes at the right time, the transcription of genes is regulated by the presence and absence of transcription factor molecules. With transcription factor concentrations undergoing constant changes, gene transcription takes place out of equilibrium. In this paper we discuss a simple mapping between dynamic models of gene expression and stochastic systems driven out of equilibrium. Using this mapping, results of nonequilibrium statistical mechanics such as the Jarzynski equality and the fluctuation theorem are demonstrated for gene expression dynamics. Applications of this approach include the determination of regulatory interactions between genes from experimental gene expression data.