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

Aayush Arya; Jessica Ray; Siddhant Sharma; Romulo Cruz Simbron; Alejandro Lozano; Harrison B. Smith; Jakob Lykke Andersen; Huan Chen; Markus Meringer; Henderson James Cleaves

doi:10.1039/d2sc00256f

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

Aayush Arya, Jessica Ray, Siddhant Sharma, Romulo Cruz Simbron, Alejandro Lozano, Harrison B. Smith, Jakob Lykke Andersen, Huan Chen, Markus Meringer, Henderson James Cleaves^*

^*Kontaktforfatter

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

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Abstrakt

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.

Originalsprog	Engelsk
Tidsskrift	Chemical Science
Vol/bind	13
Udgave nummer	17
Sider (fra-til)	4838-4853
ISSN	2041-6520
DOI	https://doi.org/10.1039/d2sc00256f
Status	Udgivet - 7. maj 2022

Bibliografisk note

Funding Information:
AA, JR, SS, RC, EALG, and HJC would like to thank the Blue Marble Space Institute of Science (BMSIS) for organizational support. JA and HJC would like to thank the Earth-Life Science Institute (ELSI) and the ELSI Origins Network (EON) for financial support during the early development of this work. EON was supported by a grant from the John Templeton Foundation. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the John Templeton Foundation. SS would like to acknowledge the SETI Forward Award from the SETI Institute. RC wishes to acknowledge FONDECYT (Convenio 208-2015-FONDECYT) for his Master scholarship. He would also like to thank Miguel Miní for his suggestion and support in the imperative search code for autocatalytic cycles. JA is also supported by the Novo Nordisk Foundation grant NNF19OC0057834 and by the Independent Research Fund Denmark, Natural Sciences, grant DFF-0135-00420B. A portion of this work was funded by the National Science Foundation Division of Chemistry and Division of Materials Research through NSF DMR-1644779, and the state of Florida. We would like to thank the anonymous referees for their constructive feedback that led to improvements in the manuscript. We would also like to express our sincere regards to Rana Dogan for her assistance in the early phases of this work.

Publisher Copyright:
© 2022 The Royal Society of Chemistry

Dokumenter og links

10.1039/d2sc00256fLicens: CC BY-NC

Open access versionForlagets udgivne version, 2,13 MBLicens: CC BY-NC

Citationsformater

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abstract = "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.",

author = "Aayush Arya and Jessica Ray and Siddhant Sharma and {Cruz Simbron}, Romulo and Alejandro Lozano and Smith, {Harrison B.} and Andersen, {Jakob Lykke} and Huan Chen and Markus Meringer and Cleaves, {Henderson James}",

note = "Funding Information: AA, JR, SS, RC, EALG, and HJC would like to thank the Blue Marble Space Institute of Science (BMSIS) for organizational support. JA and HJC would like to thank the Earth-Life Science Institute (ELSI) and the ELSI Origins Network (EON) for financial support during the early development of this work. EON was supported by a grant from the John Templeton Foundation. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the John Templeton Foundation. SS would like to acknowledge the SETI Forward Award from the SETI Institute. RC wishes to acknowledge FONDECYT (Convenio 208-2015-FONDECYT) for his Master scholarship. He would also like to thank Miguel Min{\'i} for his suggestion and support in the imperative search code for autocatalytic cycles. JA is also supported by the Novo Nordisk Foundation grant NNF19OC0057834 and by the Independent Research Fund Denmark, Natural Sciences, grant DFF-0135-00420B. A portion of this work was funded by the National Science Foundation Division of Chemistry and Division of Materials Research through NSF DMR-1644779, and the state of Florida. We would like to thank the anonymous referees for their constructive feedback that led to improvements in the manuscript. We would also like to express our sincere regards to Rana Dogan for her assistance in the early phases of this work. Publisher Copyright: {\textcopyright} 2022 The Royal Society of Chemistry",

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AU - Arya, Aayush

AU - Ray, Jessica

AU - Sharma, Siddhant

AU - Cruz Simbron, Romulo

AU - Lozano, Alejandro

AU - Smith, Harrison B.

AU - Andersen, Jakob Lykke

AU - Chen, Huan

AU - Meringer, Markus

AU - Cleaves, Henderson James

N1 - Funding Information: AA, JR, SS, RC, EALG, and HJC would like to thank the Blue Marble Space Institute of Science (BMSIS) for organizational support. JA and HJC would like to thank the Earth-Life Science Institute (ELSI) and the ELSI Origins Network (EON) for financial support during the early development of this work. EON was supported by a grant from the John Templeton Foundation. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the John Templeton Foundation. SS would like to acknowledge the SETI Forward Award from the SETI Institute. RC wishes to acknowledge FONDECYT (Convenio 208-2015-FONDECYT) for his Master scholarship. He would also like to thank Miguel Miní for his suggestion and support in the imperative search code for autocatalytic cycles. JA is also supported by the Novo Nordisk Foundation grant NNF19OC0057834 and by the Independent Research Fund Denmark, Natural Sciences, grant DFF-0135-00420B. A portion of this work was funded by the National Science Foundation Division of Chemistry and Division of Materials Research through NSF DMR-1644779, and the state of Florida. We would like to thank the anonymous referees for their constructive feedback that led to improvements in the manuscript. We would also like to express our sincere regards to Rana Dogan for her assistance in the early phases of this work. Publisher Copyright: © 2022 The Royal Society of Chemistry

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N2 - 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.

AB - 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.

U2 - 10.1039/d2sc00256f

DO - 10.1039/d2sc00256f

M3 - Journal article

C2 - 35655880

AN - SCOPUS:85129170758

SN - 2041-6520

VL - 13

SP - 4838

EP - 4853

JO - Chemical Science

JF - Chemical Science

IS - 17

ER -

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

Abstrakt

Bibliografisk note

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