ArticleJournal of mathematical biology2023
Inference on autoregulation in gene expression with variance-to-mean ratio.
Article in Journal of mathematical biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Bayesian inference of gene regulatory networks at stochastic steady state.Journal of the Royal Society, Interface · 2026Article
- Bayesian Inference of Gene Regulatory Networks at Stochastic Steady State.bioRxiv : the preprint server for biology · 2026Article
- The LFR-SWI/SNF complex: A chromatin wrench safeguarding cellular quiescence in theProceedings of the National Academy of Sciences of the United States of America · 2025Article
- TRENDY: gene regulatory network inference enhanced by transformer.Bioinformatics (Oxford, England) · 2025Article
- Order-of-Mutation Effects on Cancer Progression: Models for Myeloproliferative Neoplasm.Bulletin of mathematical biology · 2024Article
- spliceJAC: transition genes and state-specific gene regulation from single-cell transcriptome data.Molecular systems biology · 2022Article
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Authors and funding
2 authors.
Funding
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.
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