Independent control of mean and noise by convolution of gene expression distributions
| dc.citation.articleNumber | 6957 | en_US |
| dc.citation.journalTitle | Nature Communications | en_US |
| dc.citation.volumeNumber | 12 | en_US |
| dc.contributor.author | Gerhardt, Karl P. | en_US |
| dc.contributor.author | Rao, Satyajit D. | en_US |
| dc.contributor.author | Olson, Evan J. | en_US |
| dc.contributor.author | Igoshin, Oleg A. | en_US |
| dc.contributor.author | Tabor, Jeffrey J. | en_US |
| dc.contributor.org | Center for Theoretical Biophysics | en_US |
| dc.date.accessioned | 2021-12-15T22:10:21Z | en_US |
| dc.date.available | 2021-12-15T22:10:21Z | en_US |
| dc.date.issued | 2021 | en_US |
| dc.description.abstract | Gene expression noise can reduce cellular fitness or facilitate processes such as alternative metabolism, antibiotic resistance, and differentiation. Unfortunately, efforts to study the impacts of noise have been hampered by a scaling relationship between noise and expression level from individual promoters. Here, we use theory to demonstrate that mean and noise can be controlled independently by expressing two copies of a gene from separate inducible promoters in the same cell. We engineer low and high noise inducible promoters to validate this result in Escherichia coli, and develop a model that predicts the experimental distributions. Finally, we use our method to reveal that the response of a promoter to a repressor is less sensitive with higher repressor noise and explain this result using a law from probability theory. Our approach can be applied to investigate the effects of noise on diverse biological pathways or program cellular heterogeneity for synthetic biology applications. | en_US |
| dc.identifier.citation | Gerhardt, Karl P., Rao, Satyajit D., Olson, Evan J., et al.. "Independent control of mean and noise by convolution of gene expression distributions." <i>Nature Communications,</i> 12, (2021) Springer Nature: https://doi.org/10.1038/s41467-021-27070-5. | en_US |
| dc.identifier.digital | s41467-021-27070-5 | en_US |
| dc.identifier.doi | https://doi.org/10.1038/s41467-021-27070-5 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/111796 | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Springer Nature | en_US |
| dc.rights | This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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. | en_US |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
| dc.title | Independent control of mean and noise by convolution of gene expression distributions | en_US |
| dc.type | Journal article | en_US |
| dc.type.dcmi | Text | en_US |
| dc.type.publication | publisher version | en_US |
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