Stochastic gene transcription with non-competitive transcription regulatory architecture

TL;DR

This paper develops a stochastic model for gene transcription with non-competitive regulatory architecture, providing analytical insights into how various parameters influence gene expression noise and matching experimental data.

Contribution

It introduces an analytical theory for non-competitive transcription regulation, revealing factors controlling gene expression noise and novel behaviors of the Fano factor.

Findings

Fano factor varies from sub- to super-Poissonian regimes.

Parameters like transcriptional reinitiation and repressors influence noise levels.

Identifies anomalous behaviors of variance at low activator concentrations.

Abstract

The transcription factors, such as activators and repressors, can interact with the promoter of gene either in a competitive or non-competitive way. In this paper, we construct a stochastic model with non-competitive transcriptional regulatory architecture and develop an analytical theory that re-establishes the experimental results with an improved data fitting. The analytical expressions in the theory allow us to study the nature of the system corresponding to any of its parameters, and hence enable us to find out the factors that govern the regulation of gene expression for that architecture. We notice that, along with transcriptional reinitiation and repressors, there are other parameters that can control the noisiness of this network. We also observe that, the Fano factor (at mRNA level) varies from sub-Poissonian regime to superPoissonian regime. In addition to the aforementioned properties, we observe some anomalous characteristics of the Fano factor (at mRNA level) and that of the variance of protein at lower activator concentrations in presence of repressor molecules. This model is useful to understand the architecture of interactions which may buffer the stochasticity inherent to gene transcription.