Model discrimination using data collaboration.
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.
Feeley
Ryan
R
Department of Mechanical Engineering, University of California, Berkeley, California 94720-1740, USA. myf@me.berkeley.edu
Frenklach
Michael
M
Onsum
Matt
M
Russi
Trent
T
Arkin
Adam
A
Packard
Andrew
A
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
D008697:Methane