Computational models of signalling networks for non-linear control

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@Article{fuente2013computational,

• author = "Luis A. Fuente and Michael A. Lones and Alexander P. Turner and Susan Stepney and Leo S. Caves and Andy M. Tyrrell",
• title = "Computational models of signalling networks for non-linear control",
• journal = "Biosystems",
• year = "2013",
• volume = "112",
• number = "2",
• pages = "122--130",
• note = "Selected papers from the 9th International Conference on Information Processing in Cells and Tissues",
• keywords = "genetic algorithms, genetic programming, Cellular signalling, Biochemical networks, Crosstalk, Evolutionary algorithms, Chaos control",
• publisher = "Elsevier",
• ISSN = "0303-2647",
• URL = "http://www.sciencedirect.com/science/article/pii/S0303264713000506",
• DOI = "doi:10.1016/j.biosystems.2013.03.006",
• abstract = "Artificial signalling networks (ASNs) are a computational approach inspired by the signalling processes inside cells that decode outside environmental information. Using evolutionary algorithms to induce complex behaviours, we show how chaotic dynamics in a conservative dynamical system can be controlled. Such dynamics are of particular interest as they mimic the inherent complexity of non-linear physical systems in the real world. Considering the main biological interpretations of cellular signalling, in which complex behaviours and robust cellular responses emerge from the interaction of multiple pathways, we introduce two ASN representations: a stand-alone ASN and a coupled ASN. In particular we note how sophisticated cellular communication mechanisms can lead to effective controllers, where complicated problems can be divided into smaller and independent tasks.",

}

Genetic Programming entries for Luis A Fuente Michael A Lones Alexander P Turner Susan Stepney Leo Caves Andrew M Tyrrell

Citations