Inference on autoregulation in gene expression

TL;DR

This paper introduces a simple, robust method to infer gene autoregulation from gene expression data by comparing mean and variance, applicable with minimal data and model restrictions.

Contribution

It proposes a novel inference method based on Markov chain propositions that requires only non-interventional data and no parameter estimation.

Findings

Identified potential autoregulated genes in experimental data

Method validated against existing experimental and theoretical results

Applicable to various gene regulation models

Abstract

Some genes can promote or repress their own expressions, which is called autoregulation. Although gene regulation is a central topic in biology, autoregulation is much less studied. In general, it is extremely difficult to determine the existence of autoregulation with direct biochemical approaches. Nevertheless, some papers have observed that certain types of autoregulations are linked to noise levels in gene expression. We generalize these results by two propositions on discrete-state continuous-time Markov chains. These two propositions form a simple but robust method to infer the existence of autoregulation from gene expression data. This method only needs to compare the mean and variance of the gene expression level. Compared to other methods for inferring autoregulation, our method only requires non-interventional one-time data, and does not need to estimate parameters. Besides, our method has few restrictions on the model. We apply this method to four groups of experimental data and find some genes that might have autoregulation. Some inferred autoregulations have been verified by experiments or other theoretical works.