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An open source computational workflow for the discovery of autocatalytic networks in abiotic reactions

A central question in origins of life research is how non-entailed chemical processes, which simply dissipate chemical energy because they can do so due to immediate reaction kinetics and thermodynamics, enabled the origin of highly-entailed ones, in which concatenated kinetically and thermodynamica...

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Autores principales: Arya, Aayush, Ray, Jessica, Sharma, Siddhant, Cruz Simbron, Romulo, Lozano, Alejandro, Smith, Harrison B., Andersen, Jakob Lykke, Chen, Huan, Meringer, Markus, Cleaves, Henderson James
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9067619/
https://www.ncbi.nlm.nih.gov/pubmed/35655880
http://dx.doi.org/10.1039/d2sc00256f
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author Arya, Aayush
Ray, Jessica
Sharma, Siddhant
Cruz Simbron, Romulo
Lozano, Alejandro
Smith, Harrison B.
Andersen, Jakob Lykke
Chen, Huan
Meringer, Markus
Cleaves, Henderson James
author_facet Arya, Aayush
Ray, Jessica
Sharma, Siddhant
Cruz Simbron, Romulo
Lozano, Alejandro
Smith, Harrison B.
Andersen, Jakob Lykke
Chen, Huan
Meringer, Markus
Cleaves, Henderson James
author_sort Arya, Aayush
collection PubMed
description A central question in origins of life research is how non-entailed chemical processes, which simply dissipate chemical energy because they can do so due to immediate reaction kinetics and thermodynamics, enabled the origin of highly-entailed ones, in which concatenated kinetically and thermodynamically favorable processes enhanced some processes over others. Some degree of molecular complexity likely had to be supplied by environmental processes to produce entailed self-replicating processes. The origin of entailment, therefore, must connect to fundamental chemistry that builds molecular complexity. We present here an open-source chemoinformatic workflow to model abiological chemistry to discover such entailment. This pipeline automates generation of chemical reaction networks and their analysis to discover novel compounds and autocatalytic processes. We demonstrate this pipeline's capabilities against a well-studied model system by vetting it against experimental data. This workflow can enable rapid identification of products of complex chemistries and their underlying synthetic relationships to help identify autocatalysis, and potentially self-organization, in such systems. The algorithms used in this study are open-source and reconfigurable by other user-developed workflows.
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spelling pubmed-90676192022-06-01 An open source computational workflow for the discovery of autocatalytic networks in abiotic reactions Arya, Aayush Ray, Jessica Sharma, Siddhant Cruz Simbron, Romulo Lozano, Alejandro Smith, Harrison B. Andersen, Jakob Lykke Chen, Huan Meringer, Markus Cleaves, Henderson James Chem Sci Chemistry A central question in origins of life research is how non-entailed chemical processes, which simply dissipate chemical energy because they can do so due to immediate reaction kinetics and thermodynamics, enabled the origin of highly-entailed ones, in which concatenated kinetically and thermodynamically favorable processes enhanced some processes over others. Some degree of molecular complexity likely had to be supplied by environmental processes to produce entailed self-replicating processes. The origin of entailment, therefore, must connect to fundamental chemistry that builds molecular complexity. We present here an open-source chemoinformatic workflow to model abiological chemistry to discover such entailment. This pipeline automates generation of chemical reaction networks and their analysis to discover novel compounds and autocatalytic processes. We demonstrate this pipeline's capabilities against a well-studied model system by vetting it against experimental data. This workflow can enable rapid identification of products of complex chemistries and their underlying synthetic relationships to help identify autocatalysis, and potentially self-organization, in such systems. The algorithms used in this study are open-source and reconfigurable by other user-developed workflows. The Royal Society of Chemistry 2022-03-23 /pmc/articles/PMC9067619/ /pubmed/35655880 http://dx.doi.org/10.1039/d2sc00256f Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by-nc/3.0/
spellingShingle Chemistry
Arya, Aayush
Ray, Jessica
Sharma, Siddhant
Cruz Simbron, Romulo
Lozano, Alejandro
Smith, Harrison B.
Andersen, Jakob Lykke
Chen, Huan
Meringer, Markus
Cleaves, Henderson James
An open source computational workflow for the discovery of autocatalytic networks in abiotic reactions
title An open source computational workflow for the discovery of autocatalytic networks in abiotic reactions
title_full An open source computational workflow for the discovery of autocatalytic networks in abiotic reactions
title_fullStr An open source computational workflow for the discovery of autocatalytic networks in abiotic reactions
title_full_unstemmed An open source computational workflow for the discovery of autocatalytic networks in abiotic reactions
title_short An open source computational workflow for the discovery of autocatalytic networks in abiotic reactions
title_sort open source computational workflow for the discovery of autocatalytic networks in abiotic reactions
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9067619/
https://www.ncbi.nlm.nih.gov/pubmed/35655880
http://dx.doi.org/10.1039/d2sc00256f
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