Issue 17, 2022

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

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.

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

Supplementary files

Article information

Article type
Edge Article
Submitted
14 jan 2022
Accepted
16 mrt 2022
First published
23 mrt 2022
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY-NC license

Chem. Sci., 2022,13, 4838-4853

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

A. Arya, J. Ray, S. Sharma, R. Cruz Simbron, A. Lozano, H. B. Smith, J. L. Andersen, H. Chen, M. Meringer and H. J. Cleaves, Chem. Sci., 2022, 13, 4838 DOI: 10.1039/D2SC00256F

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