This paper introduces a practical data-driven method to discriminate among large-scale kinetic reaction models.The approach centers around a computable measure of model/data mismatch. We introduce two provablyconvergent algorithms that were developed to accommodate large ranges of uncertainty in the model parameters.The algorithms are demonstrated on a simple toy example and a methane combustion model with more than100 uncertain parameters. They are subsequently used to discriminate between two models for a contemporarilystudied biological signaling network.
