Dynamics of gene expression and the regulatory inference problem

TL;DR

This paper develops stochastic models of gene expression dynamics to infer biophysical parameters from time-series data, enhancing understanding of gene regulation mechanisms.

Contribution

It introduces a novel approach to model gene expression dynamics and infer regulatory parameters from experimental time-series data.

Findings

Successful inference of transcription factor-DNA binding statistics

Quantitative modeling of gene expression dynamics

Validation on experimental RNA concentration data

Abstract

From the response to external stimuli to cell division and death, the dynamics of living cells is based on the expression of specific genes at specific times. The decision when to express a gene is implemented by the binding and unbinding of transcription factor molecules to regulatory DNA. Here, we construct stochastic models of gene expression dynamics and test them on experimental time-series data of messenger-RNA concentrations. The models are used to infer biophysical parameters of gene transcription, including the statistics of transcription factor-DNA binding and the target genes controlled by a given transcription factor.