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The expression of one gene might be regulated by its corresponding protein,which is called autoregulation. Although gene regulation is a central topic inbiology, autoregulation is much less studied. In general, it is extremelydifficult to determine the existence of autoregulation with direct biochemicalapproaches. Nevertheless, some papers have observed that certain types ofautoregulations are linked to noise levels in gene expression. We generalizethese results by two propositions on discrete-state continuous-time Markovchains. These two propositions form a simple but robust method to infer theexistence of autoregulation in certain scenarios from gene expression data.This method only depends on the Fano factor, namely the ratio of variance andmean of the gene expression level. Compared to other methods for inferringautoregulation, our method only requires non-interventional one-time data, anddoes not need to estimate parameters. Besides, our method has few restrictionson the model. We apply this method to four groups of experimental data and findsome genes that might have autoregulation. Some inferred autoregulations havebeen verified by experiments or other theoretical works.

Using Fano factors to determine certain types of gene autoregulation