Next Article in Journal
The Effects of Salts and Osmoprotectants on Enzyme Activities of Fructose-1,6-biphosphate Aldolases in a Halotolerant Cyanobacterium, Halothece sp. PCC 7418
Previous Article in Journal
Evolution of Life on Earth: tRNA, Aminoacyl-tRNA Synthetases and the Genetic Code
Open AccessArticle

Evolving Always-Critical Networks

1
Department of Physics, Informatics and Mathematics, University of Modena and Reggio Emilia, I-41125 Modena, Italy
2
European Centre for Living Technology, 30123 Venice, Italy
3
Department of Physics, University of Bologna, 40126 Bologna, Italy
4
Department of Computer Science and Engineering, University of Bologna, 40126 Bologna, Italy
5
Institute for Advanced Study, University of Amsterdam, 1012 WX Amsterdam, The Netherlands
*
Author to whom correspondence should be addressed.
Life 2020, 10(3), 22; https://doi.org/10.3390/life10030022
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
Show Figures

Figure 1

MDPI and ACS Style

Villani, M.; Magrì, S.; Roli, A.; Serra, R. Evolving Always-Critical Networks. Life 2020, 10, 22.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop