Extracting falsifiable predictions from sloppy models

Abstract

Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated.

4 pages, 2 figures. Submitted to the Annals of the New York Academy of Sciences for publication in "Reverse Engineering Biological Networks: Opportunities and Challenges in Computational Methods for Pathway Inference"

References in corpus (3)

• Universally Sloppy Parameter Sensitivities in Systems Biology
• The statistical mechanics of complex signaling networks : nerve growth factor signaling
• The sloppy model universality class and the Vandermonde matrix

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• Structural Susceptibility and Separation of Time Scales in the van der Pol Oscillator
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• Sloppy models can be identifiable