Evolution of discrete gene regulatory models

Esmaeili, Afshin; Jacob, Christian

doi:10.1145/1389095.1389150

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…More recently, works addressing the evolvability of robustness in BNs have been presented [1,5,27]. In the same direction is a recent paper, in which the global fitness function is defined as the sum of single functions, each related to a network parameter linked to network robustness [8]. Finally, Roli et al present a study on the impact of the characteristics of the BNs composing the initial population over the performance of a genetic algorithm whose goal is to design networks with a given attractor length [21].…”

Section: Design Methodologymentioning

confidence: 99%

On the Design of Boolean Network Robots

Roli

Manfroni

Pinciroli

et al. 2011

Lecture Notes in Computer Science

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Abstract. Dynamical systems theory and complexity science provide powerful tools for analysing artificial agents and robots. Furthermore, they have been recently proposed also as a source of design principles and guidelines. Boolean networks are a prominent example of complex dynamical systems and they have been shown to effectively capture important phenomena in gene regulation. From an engineering perspective, these models are very compelling, because they can exhibit rich and complex behaviours, in spite of the compactness of their description. In this paper, we propose the use of Boolean networks for controlling robots' behaviour. The network is designed by means of an automatic procedure based on stochastic local search techniques. We show that this approach makes it possible to design a network which enables the robot to accomplish a task that requires the capability of navigating the space using a light stimulus, as well as the formation and use of an internal memory.

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Section: Design Methodologymentioning

confidence: 99%

On the Design of Boolean Network Robots

Roli

Manfroni

Pinciroli

et al. 2011

Lecture Notes in Computer Science

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“…More recently, works addressing the evolvability of robustness in BNs have been presented [1,23,3]. In the same direction is a recent paper, [6], in which the global fitness function is defined as the sum of single functions, each related to a network parameter somehow linked to network robustness (e.g., number and length of attractors).…”

Section: Introductionmentioning

confidence: 99%

Boolean Networks Design by Genetic Algorithms

Roli¹,

Arcaroli²,

Lazzarini³

et al. 2011

Preprint

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We present and discuss the results of an experimental analysis in the design of Boolean networks by means of genetic algorithms. A population of networks is evolved with the aim of finding a network such that the attractor it reaches is of required length l. In general, any target can be defined, provided that it is possible to model the task as an optimisation problem over the space of networks. We experiment with different initial conditions for the networks, namely in ordered, chaotic and critical regions, and also with different target length values. Results show that all kinds of initial networks can attain the desired goal, but with different success ratios: initial populations composed of critical or chaotic networks are more likely to reach the target. Moreover, the evolution starting from critical networks achieves the best overall performance. This study is the first step toward the use of search algorithms as tools for automatically design Boolean networks with required properties.

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

Benedettini

Roli

Serra

et al. 2011

Applications of Evolutionary Computation

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In this work we address the issue of designing a Boolean network such that its attractors are maximally distant. The design objective is converted into an optimisation problem, that is solved via an iterated local search algorithm. This technique proves to be effective and enables us to design networks with size up to 200 nodes. We also show that the networks obtained through the optimisation technique exhibit a mixture of characteristics typical of networks in the critical and chaotic dynamical regim

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Evolution of discrete gene regulatory models

Cited by 6 publications

References 22 publications

On the Design of Boolean Network Robots

On the Design of Boolean Network Robots

Boolean Networks Design by Genetic Algorithms

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

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