title: Designing attractive models via automated identification of chaotic and oscillatory dynamical regimes.
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.
authors.affiliation: Centre for Bioinformatics, Imperial College London, London SW7 2AZ, UK.
authors.forename: Daniel
authors.initials: D
authors.lastname: Silk
authors.forename: Paul D W
authors.initials: PD
authors.lastname: Kirk
authors.forename: Chris P
authors.initials: CP
authors.lastname: Barnes
authors.forename: Tina
authors.initials: T
authors.lastname: Toni
authors.forename: Anna
authors.initials: A
authors.lastname: Rose
authors.forename: Simon
authors.initials: S
authors.lastname: Moon
authors.forename: Margaret J
authors.initials: MJ
authors.lastname: Dallman
authors.forename: Michael P H
authors.initials: MP
authors.lastname: Stumpf
mesh_terms: D001331:Automation; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D015398:Signal Transduction