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Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives

Prebiotic chemistry often involves the study of complex systems of chemical reactions that form large networks with a large number of diverse species. Such complex systems may have given rise to emergent phenomena that ultimately led to the origin of life on Earth. The environmental conditions and p...

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Detalles Bibliográficos
Autores principales: Sharma, Siddhant, Arya, Aayush, Cruz, Romulo, Cleaves II, Henderson James
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8624352/
https://www.ncbi.nlm.nih.gov/pubmed/34833016
http://dx.doi.org/10.3390/life11111140
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author Sharma, Siddhant
Arya, Aayush
Cruz, Romulo
Cleaves II, Henderson James
author_facet Sharma, Siddhant
Arya, Aayush
Cruz, Romulo
Cleaves II, Henderson James
author_sort Sharma, Siddhant
collection PubMed
description Prebiotic chemistry often involves the study of complex systems of chemical reactions that form large networks with a large number of diverse species. Such complex systems may have given rise to emergent phenomena that ultimately led to the origin of life on Earth. The environmental conditions and processes involved in this emergence may not be fully recapitulable, making it difficult for experimentalists to study prebiotic systems in laboratory simulations. Computational chemistry offers efficient ways to study such chemical systems and identify the ones most likely to display complex properties associated with life. Here, we review tools and techniques for modelling prebiotic chemical reaction networks and outline possible ways to identify self-replicating features that are central to many origin-of-life models.
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spelling pubmed-86243522021-11-27 Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives Sharma, Siddhant Arya, Aayush Cruz, Romulo Cleaves II, Henderson James Life (Basel) Review Prebiotic chemistry often involves the study of complex systems of chemical reactions that form large networks with a large number of diverse species. Such complex systems may have given rise to emergent phenomena that ultimately led to the origin of life on Earth. The environmental conditions and processes involved in this emergence may not be fully recapitulable, making it difficult for experimentalists to study prebiotic systems in laboratory simulations. Computational chemistry offers efficient ways to study such chemical systems and identify the ones most likely to display complex properties associated with life. Here, we review tools and techniques for modelling prebiotic chemical reaction networks and outline possible ways to identify self-replicating features that are central to many origin-of-life models. MDPI 2021-10-26 /pmc/articles/PMC8624352/ /pubmed/34833016 http://dx.doi.org/10.3390/life11111140 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Sharma, Siddhant
Arya, Aayush
Cruz, Romulo
Cleaves II, Henderson James
Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives
title Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives
title_full Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives
title_fullStr Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives
title_full_unstemmed Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives
title_short Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives
title_sort automated exploration of prebiotic chemical reaction space: progress and perspectives
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8624352/
https://www.ncbi.nlm.nih.gov/pubmed/34833016
http://dx.doi.org/10.3390/life11111140
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