Computing with Artificial Gene Regulatory Networks

Lones, Michael A.

doi:10.1002/9781119079453.ch15

Cited by 6 publications

(2 citation statements)

References 63 publications

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“…The computability of gene regulatory networks has been been studied extensively over the past years. An full review of computability in GRNs can be found in [103]. Artificial GRNs have also been used to investigate a number of questions in the context of evolution.…”

Section: Internal Dynamics Of Grnsmentioning

confidence: 99%

Artificial Gene Regulatory Networks—A Review

2019

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In nature, gene regulatory networks are a key mediator between the information stored in the DNA of living organisms (their genotype) and the structural and behavioral expression this finds in their bodies, surviving in the world (their phenotype). They integrate environmental signals, steer development, buffer stochasticity, and allow evolution to proceed. In engineering, modeling and implementations of artificial gene regulatory networks have been an expanding field of research and development over the past few decades. This review discusses the concept of gene regulation, describes the current state of the art in gene regulatory networks, including modeling and simulation, and reviews their use in artificial evolutionary settings. We provide evidence for the benefits of this concept in natural and the engineering domains.

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Section: Internal Dynamics Of Grnsmentioning

confidence: 99%

Artificial Gene Regulatory Networks—A Review

2019

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“…Another, less well-known role, involves using these models to carry out computation, in a manner akin to the relationship between ANNs and biological neural networks. These artificial gene regulatory networks take on various forms (see [14] for a recent review). In some cases, representations are borrowed from the wider genetic network modeling community.…”

Section: A Artificial Gene Regulatory Networkmentioning

confidence: 99%

Artificial Epigenetic Networks: Automatic Decomposition of Dynamical Control Tasks Using Topological Self-Modification

Turner

Caves

Stepney

et al. 2017

IEEE Trans. Neural Netw. Learning Syst.

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This paper describes the artificial epigenetic network, a recurrent connectionist architecture that is able to dynamically modify its topology in order to automatically decompose and solve dynamical problems. The approach is motivated by the behavior of gene regulatory networks, particularly the epigenetic process of chromatin remodeling that leads to topological change and which underlies the differentiation of cells within complex biological organisms. We expected this approach to be useful in situations where there is a need to switch between different dynamical behaviors, and do so in a sensitive and robust manner in the absence of a priori information about problem structure. This hypothesis was tested using a series of dynamical control tasks, each requiring solutions that could express different dynamical behaviors at different stages within the task. In each case, the addition of topological self-modification was shown to improve the performance and robustness of controllers. We believe this is due to the ability of topological changes to stabilize attractors, promoting stability within a dynamical regime while allowing rapid switching between different regimes. Post hoc analysis of the controllers also demonstrated how the partitioning of the networks could provide new insights into problem structure.

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Towards in Vivo Genetic Programming: Evolving Boolean Networks to Determine Cell States

Taou¹,

Lones²

2018

Lecture Notes in Computer Science

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Computing with Artificial Gene Regulatory Networks

Cited by 6 publications

References 63 publications

Artificial Gene Regulatory Networks—A Review

Artificial Gene Regulatory Networks—A Review

Artificial Epigenetic Networks: Automatic Decomposition of Dynamical Control Tasks Using Topological Self-Modification

Towards in Vivo Genetic Programming: Evolving Boolean Networks to Determine Cell States

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