Stochastic Local Search to Automatically Design Boolean Networks with Maximally Distant Attractors

Benedettini, Stefano; Roli, Andrea; Serra, Roberto; Villani, Marco

doi:10.1007/978-3-642-20525-5_3

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…The automatic design of BNs matching some target properties of biological cells, such as the distance between attractors or the capability of producing attractor landscapes with specific characteristics resembling differentiation trees, is the subject of more recent works [44][45][46]. Finally, it is worth mentioning that evolutionary algorithms and stochastic local search techniques in general have been also applied to design BNs which are capable of controlling robots [47,48], and the structural and dynamical properties of these BNs have been studied as well [49][50][51].…”

Section: Evolving Boolean Networkmentioning

confidence: 99%

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%

Evolving Always-Critical Networks

Villani

Magrì

Roli

et al. 2020

Life

Self Cite

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“…The perturbation used inside ILS is performed as a random flip for every transition function. The same algorithm as been used in a paper by Benedettini et al [10], in which the authors design an ensemble of BNs with maximally distant attractors. The interested reader is referred to that paper for further details on the algorithm.…”

Section: Methodsmentioning

confidence: 99%

“…This goal can be achieved by applying a recently proposed method, which consists in converting the BN design problem into an optimisation one and solve it through stochastic local search [8]. This automatic design method has been proven to successfully solve BN design problems [9,10,11] and it will be detailed, for the case at hand, in the following section.…”

Section: Improved Modelmentioning

confidence: 99%

Automatic Design of Boolean Networks for Modelling Cell Differentiation

Benedettini

Roli

Serra

et al. 2014

Evolution, Complexity and Artificial Life

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A mathematical model based on Random Boolean Networks (RBNs) has been recently proposed to describe the main features of cell differentiation. The model captures in a unique framework all the main phenomena involved in cell differentiation and can be subject to experimental testing. A prominent role in the model is played by cellular noise, which somehow controls the cell ontogenetic process from the stem, totipotent state to the mature, completely differentiated one. Noise is high in stem cells and decreases while the cell undergoes the differentiation process. A limitation of the current mathematical model is that RBNs, as an ensemble, are not endowed with the property of showing a smooth relation between noise level and the differentiation stages of cells. In this work, we show that it is possible to generate an ensemble of Boolean networks (BNs) that can satisfy such a requirement, while keeping the other main relevant statistical features of classical RBNs. This ensemble is designed by means of an optimisation process, in which a stochastic local search (SLS) optimises an objective function which accounts for the requirements the network ensemble has to fulfil

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An Efficient Simulator for Boolean Network Models

Benedettini

Roli

2013

Proceedings of the European Conference on Complex Systems 2012

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Stochastic Local Search to Automatically Design Boolean Networks with Maximally Distant Attractors

Cited by 4 publications

References 18 publications

Evolving Always-Critical Networks

Evolving Always-Critical Networks

Automatic Design of Boolean Networks for Modelling Cell Differentiation

An Efficient Simulator for Boolean Network Models

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