Quantifying the noise in bursty gene expression under regulation by small RNAs

TL;DR

This paper develops an analytical approach to quantify noise in gene expression regulated by small RNAs, revealing how regulation influences protein variability and levels in stochastic gene expression models.

Contribution

It introduces an approximate analytical solution for gene expression noise under sRNA regulation, enhancing understanding of stochastic effects in post-transcriptional regulation.

Findings

Regulation by sRNAs amplifies gene expression noise.

sRNA regulation reduces average protein levels.

Stochasticity in regulation leads to higher protein production.

Abstract

Gene expression is a fundamental process in a living system. The small RNAs (sRNAs) is widely observed as a global regulator in gene expression. The inherent nonlinearity in this regulatory process together with the bursty production of messenger RNA (mRNA), sRNA and protein make the exact solution for this stochastic process intractable. This is particularly the case when quantifying the protein noise level, which has great impact on multiple cellular processes. Here we propose an approximate yet reasonably accurate solution for the gene expression noise with infrequent burst and strong regulation by sRNAs. This analytical solution allows us to better analyze the noise and stochastic deviation of protein level. We find that the regulation amplifies the noise, reduces the protein level. The stochasticity in the regulation generates more proteins than what if the stochasticity is removed from the system. The sRNA level is most important to the relationship between the noise and stochastic deviation. The results provide analytical tools for more general studies of gene expression and strengthen our quantitative understandings of post-transcriptional regulation in controlling gene expression processes.