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*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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.

Original languageEnglish
JournalChemical Science
Issue number17
Pages (from-to)4838-4853
Publication statusPublished - 7. May 2022


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