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 extreme...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Life |
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| Online Access: | https://www.mdpi.com/2075-1729/10/3/22 |
| _version_ | 1828279488134250496 |
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| author | Marco Villani Salvatore Magrì Andrea Roli Roberto Serra |
| author_facet | Marco Villani Salvatore Magrì Andrea Roli Roberto Serra |
| author_sort | Marco Villani |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-13T07:44:14Z |
| format | Article |
| id | doaj.art-002cac3785884e27b98432dc254a59ba |
| institution | Directory Open Access Journal |
| issn | 2075-1729 |
| language | English |
| last_indexed | 2024-04-13T07:44:14Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj.art-002cac3785884e27b98432dc254a59ba2022-12-22T02:55:43ZengMDPI AGLife2075-17292020-03-011032210.3390/life10030022life10030022Evolving Always-Critical NetworksMarco Villani0Salvatore Magrì1Andrea Roli2Roberto Serra3Department of Physics, Informatics and Mathematics, University of Modena and Reggio Emilia, I-41125 Modena, ItalyDepartment of Physics, University of Bologna, 40126 Bologna, ItalyEuropean Centre for Living Technology, 30123 Venice, ItalyDepartment of Physics, Informatics and Mathematics, University of Modena and Reggio Emilia, I-41125 Modena, ItalyLiving 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.https://www.mdpi.com/2075-1729/10/3/22evolving systemscriticalityedge of chaosgene regulatory networksboolean modelsgenetic algorithmsrandom boolean networks |
| spellingShingle | Marco Villani Salvatore Magrì Andrea Roli Roberto Serra Evolving Always-Critical Networks Life evolving systems criticality edge of chaos gene regulatory networks boolean models genetic algorithms random boolean 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 | evolving systems criticality edge of chaos gene regulatory networks boolean models genetic algorithms random boolean networks |
| url | https://www.mdpi.com/2075-1729/10/3/22 |
| work_keys_str_mv | AT marcovillani evolvingalwayscriticalnetworks AT salvatoremagri evolvingalwayscriticalnetworks AT andrearoli evolvingalwayscriticalnetworks AT robertoserra evolvingalwayscriticalnetworks |