Transcription and noise in negative feedback loops

TL;DR

This paper presents a stochastic analysis of negative feedback loops in genetic regulation, revealing how noise influences protein production and activity states, especially under strong repression coupling.

Contribution

It introduces a novel stochastic approach to analyze transcriptional noise in negative feedback loops, highlighting the effects of coupling strength on noise reduction and protein induction.

Findings

Bimodal activity depending on repression strength D

28% reduction in transcriptional noise variance in strong coupling

Protein levels are maintained by intrinsic noise even under strong repression

Abstract

Recently, several studies have investigated the transcription process associated to specific genetic regulatory networks. In this work, we present a stochastic approach for analyzing the dynamics and effect of negative feedback loops (FBL) on the transcriptional noise. First, our analysis allows us to identify a bimodal activity depending of the strength of self-repression coupling D. In the strong coupling region D>>1, the variance of the transcriptional noise is found to be reduced a 28 % more than described earlier. Secondly, the contribution of the noise effect to the abundance of regulating protein becomes manifest when the coefficient of variation is computed. In the strong coupling region, this coefficient is found to be independent of all parameters and in fair agreement with the experimentally observed values. Finally, our analysis reveals that the regulating protein is significantly induced by the intrinsic and external noise in the strong coupling region. In short, it indicates that the existence of inherent noise in FBL makes it possible to produce a basal amount of proteins even though the repression level D is very strong.