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Predicting species emergence in simulated complex pre-biotic networks

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 ini...

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Detalles Bibliográficos
Autores principales: Markovitch, Omer, Krasnogor, Natalio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5813963/
https://www.ncbi.nlm.nih.gov/pubmed/29447212
http://dx.doi.org/10.1371/journal.pone.0192871
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author Markovitch, Omer
Krasnogor, Natalio
author_facet Markovitch, Omer
Krasnogor, Natalio
author_sort Markovitch, Omer
collection PubMed
description 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.
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spelling pubmed-58139632018-03-02 Predicting species emergence in simulated complex pre-biotic networks Markovitch, Omer Krasnogor, Natalio PLoS One Research Article 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. Public Library of Science 2018-02-15 /pmc/articles/PMC5813963/ /pubmed/29447212 http://dx.doi.org/10.1371/journal.pone.0192871 Text en © 2018 Markovitch, Krasnogor http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Markovitch, Omer
Krasnogor, Natalio
Predicting species emergence in simulated complex pre-biotic networks
title Predicting species emergence in simulated complex pre-biotic networks
title_full Predicting species emergence in simulated complex pre-biotic networks
title_fullStr Predicting species emergence in simulated complex pre-biotic networks
title_full_unstemmed Predicting species emergence in simulated complex pre-biotic networks
title_short Predicting species emergence in simulated complex pre-biotic networks
title_sort predicting species emergence in simulated complex pre-biotic networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5813963/
https://www.ncbi.nlm.nih.gov/pubmed/29447212
http://dx.doi.org/10.1371/journal.pone.0192871
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