Designing attractive models via automated identification of chaotic and oscillatory dynamical regimes

Daniel Silk, Paul D.W. Kirk, Chris P. Barnes, Tina Toni, Anna Rose, Simon Moon, Margaret J. Dallman and Michael P.H. Stumpf ()
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Daniel Silk: Centre for Bioinformatics, Imperial College London, London SW7 2AZ, UK.
Paul D.W. Kirk: Centre for Bioinformatics, Imperial College London, London SW7 2AZ, UK.
Chris P. Barnes: Centre for Bioinformatics, Imperial College London, London SW7 2AZ, UK.
Tina Toni: Centre for Bioinformatics, Imperial College London, London SW7 2AZ, UK.
Anna Rose: Centre for Integrative Systems Biology at Imperial College London, London SW7 2AZ, UK.
Simon Moon: Centre for Integrative Systems Biology at Imperial College London, London SW7 2AZ, UK.
Margaret J. Dallman: Centre for Integrative Systems Biology at Imperial College London, London SW7 2AZ, UK.
Michael P.H. Stumpf: Centre for Bioinformatics, Imperial College London, London SW7 2AZ, UK.

Nature Communications, 2011, vol. 2, issue 1, 1-6

Abstract: Abstract Chaos and oscillations continue to capture the interest of both the scientific and public domains. Yet despite the importance of these qualitative features, most attempts at constructing mathematical models of such phenomena have taken an indirect, quantitative approach, for example, by fitting models to a finite number of data points. Here we develop a qualitative inference framework that allows us to both reverse-engineer and design systems exhibiting these and other dynamical behaviours by directly specifying the desired characteristics of the underlying dynamical attractor. This change in perspective from quantitative to qualitative dynamics, provides fundamental and new insights into the properties of dynamical systems.

Date: 2011
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DOI: 10.1038/ncomms1496

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