title: Efficient inference of parsimonious phenomenological models of cellular dynamics using S-systems and alternating regression.
abstract: The nonlinearity of dynamics in systems biology makes it hard to infer them from experimental data. Simple linear models are computationally efficient, but cannot incorporate these important nonlinearities. An adaptive method based on the S-system formalism, which is a sensible representation of nonlinear mass-action kinetics typically found in cellular dynamics, maintains the efficiency of linear regression. We combine this approach with adaptive model selection to obtain efficient and parsimonious representations of cellular dynamics. The approach is tested by inferring the dynamics of yeast glycolysis from simulated data. With little computing time, it produces dynamical models with high predictive power and with structural complexity adapted to the difficulty of the inference problem.
authors: Center for Complexity and Collective Computation, Wisconsin Institute for Discovery, University of Wisconsin, Madison, WI 53715, USA.
authors: Bryan C
authors: BC
authors: Daniels
authors: Departments of Physics and Biology, Emory University, Atlanta, GA 30322, USA.
authors: Ilya
authors: I
authors: Nemenman
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D053263:Gene Regulatory Networks; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D049490:Systems Biology; D015003:Yeasts