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a Laboratory of Atomic and Solid State Physics, Cornell University, Ithaca, New York, USA b UCD Conway Institute of Biomolecular and Biomedical Research, University College, Dublin, Dublin, Ireland c Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA d Cornell Theory Center, Cornell University, Ithaca, New York, USA
Key Words: systems biology • sloppy models • prediction uncertainties • Monte-Carlo • covariance analysis
Address for correspondence: Ryan Gutenkunst, Laboratory of Atomic and Solid State Physics, Cornell University, Ithaca, NY 14853. Voice: 607-227-7914; fax: 607-255-6428. rng7{at}cornell.edu
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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