Extracting falsifiable predictions from sloppy models

Details Extracting falsifiable predictions from sloppy models Extracting falsifiable predictions from sloppy models

2007-04-23

arxiv.org/abs/0704.3049v1

Annals of the New York Academy of Sciences 1115:203-211 (2007)

Biology

Quantitative Biology

Quantitative Methods

4 pages, 2 figures. Submitted to the Annals of the New York Academy of Sciences for publication in "Reverse Engineering Biolog

Scientific paper

10.1196/annals.1407.003

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.

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