Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autor principal: Baker, Josh E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cornell University 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10246067/
https://www.ncbi.nlm.nih.gov/pubmed/37292461
_version_ 1785054971432009728
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