Inference on autoregulation in gene expression with variance-to-mean ratio

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....

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Bibliographic Details
Published in:Journal of mathematical biology 2023-05, Vol.86 (5), p.87-87, Article 87
Main Authors: Wang, Yue, He, Siqi
Format: Article
Language:English
Subjects:
Applications of Mathematics
Gene Expression
Gene Expression Regulation
Gene regulation
Genes
Homeostasis - genetics
Markov Chains
Mathematical and Computational Biology
Mathematics
Mathematics and Statistics
Noise levels
Parameter estimation
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Summary: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.
ISSN:0303-6812
1432-1416
DOI:10.1007/s00285-023-01924-6