Stochastic fluctuations can reveal the feedback signs of gene regulatory networks at the single-molecule level

TL;DR

This paper analytically links stochastic fluctuations in gene expression to feedback network topology at the single-molecule level, enabling inference of network structure from single-cell data.

Contribution

It provides a novel analytical framework connecting stochastic fluctuations with feedback topology and introduces a method to infer network structure from single-cell gene expression data.

Findings

Analytical steady-state distribution derived for nonlinear feedback models

Feedback topology influences molecular fluctuation levels

Method successfully infers network structure from experimental data

Abstract

Understanding the relationship between spontaneous stochastic fluctuations and the topology of the underlying gene regulatory network is of fundamental importance for the study of single-cell stochastic gene expression. Here by solving the analytical steady-state distribution of the protein copy number in a general kinetic model of stochastic gene expression with nonlinear feedback regulation, we reveal the relationship between stochastic fluctuations and feedback topology at the single-molecule level, which provides novel insights into how and to what extent a feedback loop can enhance or suppress molecular fluctuations. Based on such relationship, we also develop an effective method to extract the topological information of a gene regulatory network from single-cell gene expression data. The theory is demonstrated by numerical simulations and, more importantly, validated quantitatively by single-cell data analysis of a synthetic gene circuit integrated in human kidney cells.