Dichotomous noise models of gene switches

TL;DR

This paper develops hybrid multi-scale models to analyze the impact of dichotomous gene switching noise on the dynamics of gene regulatory networks, highlighting its significance in cellular phenotypic diversity.

Contribution

It introduces an approximate framework connecting dichotomous noise models with the master equation, enabling efficient analysis of large-scale genetic circuits.

Findings

Genetic noise from gene switching significantly affects network behavior.

Hybrid schemes accurately capture steady state properties of gene circuits.

Dichotomous noise influences cell phenotype stability.

Abstract

Molecular noise in gene regulatory networks has two intrinsic components, one part being due to fluctuations caused by the birth and death of protein or mRNA molecules which are often present in small numbers and the other part arising from gene state switching, a single molecule event. Stochastic dynamics of gene regulatory circuits appears to be largely responsible for bifurcations into a set of multi-attractor states that encode different cell phenotypes. The interplay of dichotomous single molecule gene noise with the nonlinear architecture of genetic networks generates rich and complex phenomena. In this paper we elaborate on an approximate framework that leads to simple hybrid multi-scale schemes well suited for the quantitative exploration of the steady state properties of large-scale cellular genetic circuits.Through a path sum based analysis of trajectory statistics we elucidate the connection of these hybrid schemes to the underlying master equation and provide a rigorous justification for using dichotomous noise based models to study genetic networks. Numerical simulations of circuit models reveal that the contribution of the genetic noise of single molecule origin to the total noise is significant for a wide range of kinetic regimes.