Cargando…
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....
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10154285/ https://www.ncbi.nlm.nih.gov/pubmed/37131095 http://dx.doi.org/10.1007/s00285-023-01924-6 |
_version_ | 1785036095560351744 |
---|---|
author | Wang, Yue He, Siqi |
author_facet | Wang, Yue He, Siqi |
author_sort | Wang, Yue |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10154285 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-101542852023-05-04 Inference on autoregulation in gene expression with variance-to-mean ratio Wang, Yue He, Siqi J Math Biol Article 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. Springer Berlin Heidelberg 2023-05-03 2023 /pmc/articles/PMC10154285/ /pubmed/37131095 http://dx.doi.org/10.1007/s00285-023-01924-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Wang, Yue He, Siqi Inference on autoregulation in gene expression with variance-to-mean ratio |
title | Inference on autoregulation in gene expression with variance-to-mean ratio |
title_full | Inference on autoregulation in gene expression with variance-to-mean ratio |
title_fullStr | Inference on autoregulation in gene expression with variance-to-mean ratio |
title_full_unstemmed | Inference on autoregulation in gene expression with variance-to-mean ratio |
title_short | Inference on autoregulation in gene expression with variance-to-mean ratio |
title_sort | inference on autoregulation in gene expression with variance-to-mean ratio |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10154285/ https://www.ncbi.nlm.nih.gov/pubmed/37131095 http://dx.doi.org/10.1007/s00285-023-01924-6 |
work_keys_str_mv | AT wangyue inferenceonautoregulationingeneexpressionwithvariancetomeanratio AT hesiqi inferenceonautoregulationingeneexpressionwithvariancetomeanratio |