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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 unpack the basic...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Cornell University
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10246067/ https://www.ncbi.nlm.nih.gov/pubmed/37292461 |
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author | Baker, Josh E. |
author_facet | Baker, Josh E. |
author_sort | Baker, Josh E. |
collection | PubMed |
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 unpack 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 his paradox. |
format | Online Article Text |
id | pubmed-10246067 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cornell University |
record_format | MEDLINE/PubMed |
spelling | pubmed-102460672023-06-08 Cells Solved the Gibbs Paradox by Learning to Contain Entropic Forces Baker, Josh E. ArXiv 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 unpack 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 his paradox. Cornell University 2023-05-17 /pmc/articles/PMC10246067/ /pubmed/37292461 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
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/PMC10246067/ https://www.ncbi.nlm.nih.gov/pubmed/37292461 |
work_keys_str_mv | AT bakerjoshe cellssolvedthegibbsparadoxbylearningtocontainentropicforces |