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Predicting species emergence in simulated complex pre-biotic networks

Lookup NU author(s): Omer Markovitch, Professor Natalio KrasnogorORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2018 Markovitch, Krasnogor. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. An intriguing question in evolution is what would happen if one could “replay” life’s tape. Here, we explore the following hypothesis: when replaying the tape, the details (“decorations”) of the outcomes would vary but certain “invariants” might emerge across different life-tapes sharing similar initial conditions. We use large-scale simulations of an in silico model of pre-biotic evolution called GARD (Graded Autocatalysis Replication Domain) to test this hypothesis. GARD models the temporal evolution of molecular assemblies, governed by a rates matrix (i.e. network) that biases different molecules’ likelihood of joining or leaving a dynamically growing and splitting assembly. Previous studies have shown the emergence of so called compotypes, i.e., species capable of replication and selection response. Here, we apply networks’ science to ascertain the degree to which invariants emerge across different life-tapes under GARD dynamics and whether one can predict these invariant from the chemistry specification alone (i.e. GARD’s rates network representing initial conditions). We analysed the (complex) rates’ network communities and asked whether communities are related (and how) to the emerging species under GARD’s dynamic, and found that the communities correspond to the species emerging from the simulations. Importantly, we show how to use the set of communities detected to predict species emergence without performing any simulations. The analysis developed here may impact complex systems simulations in general.

Publication metadata

Author(s): Markovitch O, Krasnogor N

Publication type: Article

Publication status: Published

Journal: PLoS ONE

Year: 2018

Volume: 13

Issue: 2

Online publication date: 15/02/2018

Acceptance date: 31/01/2018

Date deposited: 06/03/2018

ISSN (electronic): 1932-6203

Publisher: Public Library of Science


DOI: 10.1371/journal.pone.0192871


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