Dynamical Criticality in Gene Regulatory Networks

Villani, Marco; Rocca, Luca La; Kauffman, Stuart; Serra, Roberto

doi:10.1155/2018/5980636

Cited by 30 publications

(31 citation statements)

References 62 publications

(102 reference statements)

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“…In the case of several models of type (a), which describe specific genetic circuits, a wide analysis by [15] has shown that the (static) sensitivity is close to 1. Similar results were obtained using models of type (b), i.e., RBNs, which were applied to describe the effects of gene perturbations induced by permanently inhibiting the expression of a single gene (knock-out) and by observing the changes of the expression levels of the other genes [12,14,16,30].…”

Section: Boolean Models Of Gene Regulatory Networksupporting

confidence: 58%

“…In this way, it is possible to use a static measure (the size of avalanches) to draw inferences about the dynamical regime of the network. This has been discussed in a series of papers, and it has been shown that it works in the well-studied case of the yeast S. Cerevisiae [12,14,16]. The best estimate of the Derrida parameter places it in the ordered region, quite close to the critical boundary [16].…”

Section: Boolean Models Of Gene Regulatory Networkmentioning

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: Boolean Models Of Gene Regulatory Networksupporting

confidence: 58%

Section: Boolean Models Of Gene Regulatory Networkmentioning

confidence: 99%

Evolving Always-Critical Networks

Villani

Magrì

Roli

et al. 2020

Life

Self Cite

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“…This can be understood because immune cells probably not only have a variable environment, but actually have evolved to thrive on it. The finding on the ordered dynamics of the CD4+ Tcell network is consistent with many research findings exhibiting that gene regulatory networks of biological systems have ordered or critical dynamics [49][50][51][52].…”

Section: Comparison Of Antifragility Between Multilayer and Single-lasupporting

confidence: 90%

A Multilayer Structure Facilitates the Production of Antifragile Systems in Boolean Network Models

2019

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Antifragility is a property from which systems are able to resist stress and furthermore benefit from it. Even though antifragile dynamics is found in various real-world complex systems where multiple subsystems interact with each other, the attribute has not been quantitatively explored yet in those complex systems which can be regarded as multilayer networks. Here we study how the multilayer structure affects the antifragility of the whole system. By comparing single-layer and multilayer Boolean networks based on our recently proposed antifragility measure, we found that the multilayer structure facilitated the production of antifragile systems. Our measure and findings will be useful for various applications such as exploring properties of biological systems with multilayer structures and creating more antifragile engineered systems.

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“…We found that A. thaliana cell-cycle repeatedly produces antifragile networks at regular intervals depending on the values of . Based on many studies demonstrating living organisms are ordered or critical [47][48][49][50], we can infer that A. thaliana might have been evolved in environments where particular dimensions of perturbations are added more frequently than other biological systems. We also found that CD4+ T cell differentiation and plasticity is the most antifragile of the ones studied, probably because it has the most variable environment.…”

Section: Antifragility In Biological Bnsmentioning

confidence: 99%

A Novel Antifragility Measure Based on Satisfaction and Its Application to Random and Biological Boolean Networks

2019

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Antifragility is a property that enhances the capability of a system in response to external perturbations. Although the concept has been applied in many areas, a practical measure of antifragility has not been developed yet. Here we propose a simply calculable measure of antifragility, based on the change of "satisfaction" before and after adding perturbations, and apply it to random Boolean networks (RBNs). Using the measure, we found that ordered RBNs are the most antifragile. Also, we demonstrated that seven biological systems are antifragile. Our measure and results can be used in various applications of Boolean networks (BNs) including creating antifragile engineering systems, identifying the genetic mechanism of antifragile biological systems, and developing new treatment strategies for various diseases.

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Dynamical Criticality in Gene Regulatory Networks

Cited by 30 publications

References 62 publications

Evolving Always-Critical Networks

Evolving Always-Critical Networks

A Multilayer Structure Facilitates the Production of Antifragile Systems in Boolean Network Models

A Novel Antifragility Measure Based on Satisfaction and Its Application to Random and Biological Boolean Networks

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