Post-transcriptional regulation of noise in protein distributions during gene expression

TL;DR

This paper presents a stochastic model to analyze how post-transcriptional regulation influences noise in protein levels during gene expression, providing insights into noise modulation and sources in cellular protein distributions.

Contribution

It introduces a new stochastic model for post-transcriptional regulation and offers analytical solutions to understand its impact on protein noise levels.

Findings

Different mechanisms of post-transcriptional regulation affect protein noise.

The model can discriminate sources of intrinsic noise based on protein distribution data.

Analytical solutions reveal how regulation modulates variability in gene expression.

Abstract

The intrinsic stochasticity of gene expression can lead to large variability of protein levels across a population of cells. Variability (or noise) in protein distributions can be modulated by cellular mechanisms of gene regulation; in particular, there is considerable interest in understanding the role of post-transcriptional regulation. To address this issue, we propose and analyze a stochastic model for post-transcriptional regulation of gene expression. The analytical solution of the model provides insight into the effects of different mechanisms of post-transcriptional regulation on the noise in protein distributions. The results obtained also demonstrate how different sources of intrinsic noise in gene expression can be discriminated based on observations of regulated protein distributions.