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Evolving Always-Critical Networks

Living beings share several common features at the molecular level, but there are very few large-scale “operating principles” which hold for all (or almost all) organisms. However, biology is subject to a deluge of data, and as such, general concepts such as this would be extremely valuable. One int...

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Autores principales: Villani, Marco, Magrì, Salvatore, Roli, Andrea, Serra, Roberto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7151631/
https://www.ncbi.nlm.nih.gov/pubmed/32143532
http://dx.doi.org/10.3390/life10030022
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author Villani, Marco
Magrì, Salvatore
Roli, Andrea
Serra, Roberto
author_facet Villani, Marco
Magrì, Salvatore
Roli, Andrea
Serra, Roberto
author_sort Villani, Marco
collection PubMed
description Living beings share several common features at the molecular level, but there are very few large-scale “operating principles” which hold for all (or almost all) organisms. However, biology is subject to a deluge of data, and as such, general concepts such as this would be extremely valuable. One interesting candidate is the “criticality” principle, which claims that biological evolution favors those dynamical regimes that are intermediaries between ordered and disordered states (i.e., “at the edge of chaos”). The reasons why this should be the case and experimental evidence are briefly discussed, observing that gene regulatory networks are indeed often found on, or close to, the critical boundaries. Therefore, assuming that criticality provides an edge, it is important to ascertain whether systems that are critical can further evolve while remaining critical. In order to explore the possibility of achieving such “always-critical” evolution, we resort to simulated evolution, by suitably modifying a genetic algorithm in such a way that the newly-generated individuals are constrained to be critical. It is then shown that these modified genetic algorithms can actually develop critical gene regulatory networks with two interesting (and quite different) features of biological significance, involving, in one case, the average gene activation values and, in the other case, the response to perturbations. These two cases suggest that it is often possible to evolve networks with interesting properties without losing the advantages of criticality. The evolved networks also show some interesting features which are discussed.
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spelling pubmed-71516312020-04-20 Evolving Always-Critical Networks Villani, Marco Magrì, Salvatore Roli, Andrea Serra, Roberto Life (Basel) Article Living beings share several common features at the molecular level, but there are very few large-scale “operating principles” which hold for all (or almost all) organisms. However, biology is subject to a deluge of data, and as such, general concepts such as this would be extremely valuable. One interesting candidate is the “criticality” principle, which claims that biological evolution favors those dynamical regimes that are intermediaries between ordered and disordered states (i.e., “at the edge of chaos”). The reasons why this should be the case and experimental evidence are briefly discussed, observing that gene regulatory networks are indeed often found on, or close to, the critical boundaries. Therefore, assuming that criticality provides an edge, it is important to ascertain whether systems that are critical can further evolve while remaining critical. In order to explore the possibility of achieving such “always-critical” evolution, we resort to simulated evolution, by suitably modifying a genetic algorithm in such a way that the newly-generated individuals are constrained to be critical. It is then shown that these modified genetic algorithms can actually develop critical gene regulatory networks with two interesting (and quite different) features of biological significance, involving, in one case, the average gene activation values and, in the other case, the response to perturbations. These two cases suggest that it is often possible to evolve networks with interesting properties without losing the advantages of criticality. The evolved networks also show some interesting features which are discussed. MDPI 2020-03-04 /pmc/articles/PMC7151631/ /pubmed/32143532 http://dx.doi.org/10.3390/life10030022 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Villani, Marco
Magrì, Salvatore
Roli, Andrea
Serra, Roberto
Evolving Always-Critical Networks
title Evolving Always-Critical Networks
title_full Evolving Always-Critical Networks
title_fullStr Evolving Always-Critical Networks
title_full_unstemmed Evolving Always-Critical Networks
title_short Evolving Always-Critical Networks
title_sort evolving always-critical networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7151631/
https://www.ncbi.nlm.nih.gov/pubmed/32143532
http://dx.doi.org/10.3390/life10030022
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