Model discrimination using data collaboration.

Ryan Feeley; Michael Frenklach; Matt Onsum; Trent Russi; Adam Arkin; Andrew Packard

doi:10.1021/jp056309s

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Model discrimination using data collaboration.

Ryan Feeley·Michael Frenklach·Matt Onsum·Trent Russi·Adam Arkin·Andrew Packard

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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.

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Algorithms Computational Biology Computer Simulation Data Interpretation, Statistical Incineration Kinetics Methane Models, Biological Models, Statistical Uncertainty Journal Article Research Support, U.S. Gov't, Non-P.H.S.

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Ryan Feeley, Michael Frenklach, Matt Onsum, Trent Russi, Adam Arkin, Andrew Packard. 2006-06-01. Model discrimination using data collaboration.. https://doi.org/10.1021/jp056309s

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