Evolving Critical Boolean Networks

Magrì, Salvatore; Villani, Marco; Roli, Andrea; Serra, Roberto

doi:10.1007/978-3-030-21733-4_2

Cited by 4 publications

(3 citation statements)

References 39 publications

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“…We recognize that none of the aforementioned works explicitly constrains the dynamical regime of the networks along the evolutionary process. To the best of our knowledge, the first works applying this constraint are [52,53], where genetic algorithm variants with genetic operators that do not change the proportion of 0 s and 1 s in the Boolean functions are presented. Some of the results of these papers will be summarized below, as they are the starting point of this contribution.…”

Section: Evolving Boolean Networkmentioning

confidence: 99%

“…The GA may be run without constraints (and could therefore change the BNs' bias by influencing the dynamic regime of the system, i.e., "free GA", or simply "GA"), or it may be constrained to maintain the bias of RBNs present in the first generation. Starting from a generation composed of individuals having similar biases, there are various ways to maintain this situation [52,53]: the GA variant used in this work (called "balanced GA") randomly chooses the crossover cutoff point among those that lead to the smallest changes of the overall bias of the resulting individuals. Then, if the resulting bias is still different form the desired one, it makes point mutations in order to correct the residual deviations.…”

Section: The Genetic Algorithmsmentioning

confidence: 99%

“…The general scheme of the used GAs (#G denotes the number of elements in set G). Steps 6 and 7 highlight the differences between free and balanced GA [52].…”

Section: The Genetic Algorithmsmentioning

confidence: 99%

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Evolving Always-Critical Networks

Villani

Magrì

Roli

et al. 2020

Life

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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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Section: Evolving Boolean Networkmentioning

confidence: 99%

Section: The Genetic Algorithmsmentioning

confidence: 99%