title: Automated adaptive inference of phenomenological dynamical models.
abstract: Dynamics of complex systems is often driven by large and intricate networks of microscopic interactions, whose sheer size obfuscates understanding. With limited experimental data, many parameters of such dynamics are unknown, and thus detailed, mechanistic models risk overfitting and making faulty predictions. At the other extreme, simple ad hoc models often miss defining features of the underlying systems. Here we develop an approach that instead constructs phenomenological, coarse-grained models of network dynamics that automatically adapt their complexity to the available data. Such adaptive models produce accurate predictions even when microscopic details are unknown. The approach is computationally tractable, even for a relatively large number of dynamical variables. Using simulated data, it correctly infers the phase space structure for planetary motion, avoids overfitting in a biological signalling system and produces accurate predictions for yeast glycolysis with tens of data points and over half of the interacting species unobserved.
authors: Center for Complexity and Collective Computation, Wisconsin Institute for Discovery, University of Wisconsin, Madison, Wisconsin 53715, USA.
authors: Bryan C
authors: BC
authors: Daniels
authors: Department of Physics, Emory University, Atlanta, Georgia 30322, USA.|Department of Biology, Emory University, Atlanta, Georgia 30322, USA.
authors: Ilya
authors: I
authors: Nemenman
mesh_terms: D001499:Bayes Theorem; D002468:Cell Physiological Phenomena; D003198:Computer Simulation; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D008954:Models, Biological; D017711:Nonlinear Dynamics; D010766:Phosphorylation; D015003:Yeasts / Q000502:physiology