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
Author Information
Aayush Arya: Department of Physics, Lovely Professional University Jalandhar Delhi-GT Road Phagwara Punjab 144411 India. ORCID
Jessica Ray: Blue Marble Space Institute of Science Seattle Washington 98104 USA.
Siddhant Sharma: Blue Marble Space Institute of Science Seattle Washington 98104 USA. ORCID
Romulo Cruz Simbron: Blue Marble Space Institute of Science Seattle Washington 98104 USA. ORCID
Alejandro Lozano: Blue Marble Space Institute of Science Seattle Washington 98104 USA.
Harrison B Smith: Earth-Life Science Institute, Tokyo Institute of Technology Tokyo Japan hcleaves@elsi.jp. ORCID
Jakob Lykke Andersen: Department of Mathematics and Computer Science, University of Southern Denmark Campusvej 55 5230 Odense M Denmark.
Huan Chen: National High Magnetic Field Laboratory Tallahassee Florida 32310 USA. ORCID
Markus Meringer: German Aerospace Center (DLR) 82234 Oberpfaffenhofen Wessling Germany. ORCID
Henderson James Cleaves: Blue Marble Space Institute of Science Seattle Washington 98104 USA. ORCID
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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