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Evolution of Life on Earth: tRNA, Aminoacyl-tRNA Synthetases and the Genetic Code
Open AccessArticle

Evolving Always-Critical Networks

Department of Physics, Informatics and Mathematics, University of Modena and Reggio Emilia, I-41125 Modena, Italy
European Centre for Living Technology, 30123 Venice, Italy
Department of Physics, University of Bologna, 40126 Bologna, Italy
Department of Computer Science and Engineering, University of Bologna, 40126 Bologna, Italy
Institute for Advanced Study, University of Amsterdam, 1012 WX Amsterdam, The Netherlands
Author to whom correspondence should be addressed.
Life 2020, 10(3), 22;
Received: 31 January 2020 / Revised: 29 February 2020 / Accepted: 1 March 2020 / Published: 4 March 2020
(This article belongs to the Section Synthetic Biology and Systems Biology)
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. View Full-Text
Keywords: evolving systems; criticality; edge of chaos; gene regulatory networks; Boolean models; genetic algorithms; random Boolean networks evolving systems; criticality; edge of chaos; gene regulatory networks; Boolean models; genetic algorithms; random Boolean networks
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Villani, M.; Magrì, S.; Roli, A.; Serra, R. Evolving Always-Critical Networks. Life 2020, 10, 22.

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