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Cells solved the Gibbs paradox by learning to contain entropic forces

As Nature’s version of machine learning, evolution has solved many extraordinarily complex problems, none perhaps more remarkable than learning to harness an increase in chemical entropy (disorder) to generate directed chemical forces (order). Using muscle as a model system, here I describe the basi...

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Detalles Bibliográficos
Autor principal: Baker, Josh E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10547751/
https://www.ncbi.nlm.nih.gov/pubmed/37789054
http://dx.doi.org/10.1038/s41598-023-43532-w
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author Baker, Josh E.
author_facet Baker, Josh E.
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description As Nature’s version of machine learning, evolution has solved many extraordinarily complex problems, none perhaps more remarkable than learning to harness an increase in chemical entropy (disorder) to generate directed chemical forces (order). Using muscle as a model system, here I describe the basic mechanism by which life creates order from disorder. In short, evolution tuned the physical properties of certain proteins to contain changes in chemical entropy. As it happens these are the “sensible” properties Gibbs postulated were needed to solve a paradox that has intrigued and challenged scientists and philosophers for over 100 years.
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spelling pubmed-105477512023-10-05 Cells solved the Gibbs paradox by learning to contain entropic forces Baker, Josh E. Sci Rep Article As Nature’s version of machine learning, evolution has solved many extraordinarily complex problems, none perhaps more remarkable than learning to harness an increase in chemical entropy (disorder) to generate directed chemical forces (order). Using muscle as a model system, here I describe the basic mechanism by which life creates order from disorder. In short, evolution tuned the physical properties of certain proteins to contain changes in chemical entropy. As it happens these are the “sensible” properties Gibbs postulated were needed to solve a paradox that has intrigued and challenged scientists and philosophers for over 100 years. Nature Publishing Group UK 2023-10-03 /pmc/articles/PMC10547751/ /pubmed/37789054 http://dx.doi.org/10.1038/s41598-023-43532-w Text en © The Author(s) 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 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
Baker, Josh E.
Cells solved the Gibbs paradox by learning to contain entropic forces
title Cells solved the Gibbs paradox by learning to contain entropic forces
title_full Cells solved the Gibbs paradox by learning to contain entropic forces
title_fullStr Cells solved the Gibbs paradox by learning to contain entropic forces
title_full_unstemmed Cells solved the Gibbs paradox by learning to contain entropic forces
title_short Cells solved the Gibbs paradox by learning to contain entropic forces
title_sort cells solved the gibbs paradox by learning to contain entropic forces
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10547751/
https://www.ncbi.nlm.nih.gov/pubmed/37789054
http://dx.doi.org/10.1038/s41598-023-43532-w
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