Induction level determines signature of gene expression noise in cellular systems

TL;DR

This paper develops a stochastic framework to analyze how gene induction levels influence the dominant sources and signatures of gene expression noise, accounting for cell division and providing insights into experimental variability.

Contribution

It introduces a theoretical model that explicitly incorporates cell division to distinguish noise sources in gene expression based on induction levels.

Findings

Induction level determines the dominant gene expression noise source.

Analytical solutions quantify noise contributions from protein synthesis steps.

Simulations suggest independent modeling of transcription factor binding and promoter activation.

Abstract

Noise in gene expression, either due to inherent stochasticity or to varying inter- and intracellular environment, can generate significant cell-to-cell variability of protein levels in clonal populations. We present a theoretical framework, based on stochastic processes, to quantify the different sources of gene expression noise taking cell division explicitly into account. Analytical, time-dependent solutions for the noise contributions arising from the major steps involved in protein synthesis are derived. The analysis shows that the induction level of the activator or transcription factor is crucial for the characteristic signature of the dominant source of gene expression noise and thus bridges the gap between seemingly contradictory experimental results. Furthermore, on the basis of experimentally measured cell distributions, our simulations suggest that transcription factor binding and promoter activation can be modelled independently of each other with sufficient accuracy.