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Ann. N.Y. Acad. Sci., Annals PrePrint, published online ahead of print October 9, 2007 doi: 10.1196/annals.1407.003 Copyright © 2007 by the New York Academy of Sciences description
1 Laboratory of Atomic and Solid State Physics, Cornell University, Clark Hall, Ithaca, New York, 14850, United States 2 UCD Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland 3 Molecular Biology and Genetics, Cornell University, Ithaca, New York, United States 4 Cornell Theory Center, Cornell University, Ithaca, New York, United States 5 Laboratory of Atomic and Solid State Physics, Cornell University, Ithaca, New York, United States
* To whom correspondence should be addressed. E-mail: rng7{at}cornell.edu. PrePrint 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. Key Words:
systems biology, sloppy models, prediction uncertainties, Monte-Carlo, covariance analysis
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