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Modelling genetic stability in engineered cell populations

Predicting the evolution of engineered cell populations is a highly sought-after goal in biotechnology. While models of evolutionary dynamics are far from new, their application to synthetic systems is scarce where the vast combination of genetic parts and regulatory elements creates a unique challe...

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Autores principales: Ingram, Duncan, Stan, Guy-Bart
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10260955/
https://www.ncbi.nlm.nih.gov/pubmed/37308512
http://dx.doi.org/10.1038/s41467-023-38850-6
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author Ingram, Duncan
Stan, Guy-Bart
author_facet Ingram, Duncan
Stan, Guy-Bart
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description Predicting the evolution of engineered cell populations is a highly sought-after goal in biotechnology. While models of evolutionary dynamics are far from new, their application to synthetic systems is scarce where the vast combination of genetic parts and regulatory elements creates a unique challenge. To address this gap, we here-in present a framework that allows one to connect the DNA design of varied genetic devices with mutation spread in a growing cell population. Users can specify the functional parts of their system and the degree of mutation heterogeneity to explore, after which our model generates host-aware transition dynamics between different mutation phenotypes over time. We show how our framework can be used to generate insightful hypotheses across broad applications, from how a device’s components can be tweaked to optimise long-term protein yield and genetic shelf life, to generating new design paradigms for gene regulatory networks that improve their functionality.
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spelling pubmed-102609552023-06-15 Modelling genetic stability in engineered cell populations Ingram, Duncan Stan, Guy-Bart Nat Commun Article Predicting the evolution of engineered cell populations is a highly sought-after goal in biotechnology. While models of evolutionary dynamics are far from new, their application to synthetic systems is scarce where the vast combination of genetic parts and regulatory elements creates a unique challenge. To address this gap, we here-in present a framework that allows one to connect the DNA design of varied genetic devices with mutation spread in a growing cell population. Users can specify the functional parts of their system and the degree of mutation heterogeneity to explore, after which our model generates host-aware transition dynamics between different mutation phenotypes over time. We show how our framework can be used to generate insightful hypotheses across broad applications, from how a device’s components can be tweaked to optimise long-term protein yield and genetic shelf life, to generating new design paradigms for gene regulatory networks that improve their functionality. Nature Publishing Group UK 2023-06-12 /pmc/articles/PMC10260955/ /pubmed/37308512 http://dx.doi.org/10.1038/s41467-023-38850-6 Text en © The Author(s) 2023, corrected publication 2023 https://creativecommons.org/licenses/by/4.0/Open Access 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. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Ingram, Duncan
Stan, Guy-Bart
Modelling genetic stability in engineered cell populations
title Modelling genetic stability in engineered cell populations
title_full Modelling genetic stability in engineered cell populations
title_fullStr Modelling genetic stability in engineered cell populations
title_full_unstemmed Modelling genetic stability in engineered cell populations
title_short Modelling genetic stability in engineered cell populations
title_sort modelling genetic stability in engineered cell populations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10260955/
https://www.ncbi.nlm.nih.gov/pubmed/37308512
http://dx.doi.org/10.1038/s41467-023-38850-6
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