title: Model discrimination using data collaboration.
abstract: 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 provably convergent 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 than 100 uncertain parameters. They are subsequently used to discriminate between two models for a contemporarily studied biological signaling network.
authors.affiliation: Department of Mechanical Engineering, University of California, Berkeley, California 94720-1740, USA. myf@me.berkeley.edu
authors.forename: Ryan
authors.initials: R
authors.lastname: Feeley
authors.forename: Michael
authors.initials: M
authors.lastname: Frenklach
authors.forename: Matt
authors.initials: M
authors.lastname: Onsum
authors.forename: Trent
authors.initials: T
authors.lastname: Russi
authors.forename: Adam
authors.initials: A
authors.lastname: Arkin
authors.forename: Andrew
authors.initials: A
authors.lastname: Packard
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D017745:Incineration; D007700:Kinetics; D008697:Methane / Q000737:chemistry*; D008954:Models, Biological; D015233:Models, Statistical; D035501:Uncertainty
chemical_list: D008697:Methane