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.Comment: 14 pages, 2 figure