Extracting falsifiable predictions from sloppy models

Ryan N. Gutenkunst, Fergal P. Casey, Joshua J. Waterfall, Christopher R. Myers, James P. Sethna

Research output: Chapter in Book/Report/Conference proceedingConference contribution

42 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationReverse Engineering Biological Networks
Subtitle of host publicationOpportunities and Challenges in Computational Methods for Pathway Inference
PublisherBlackwell Publishing Inc.
Number of pages9
ISBN (Print)9781573316897
StatePublished - Dec 2007

Publication series

NameAnnals of the New York Academy of Sciences
ISSN (Print)0077-8923
ISSN (Electronic)1749-6632


  • Covariance analysis
  • Monte-Carlo
  • Prediction uncertainties
  • Sloppy models
  • Systems biology

ASJC Scopus subject areas

  • Neuroscience(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • History and Philosophy of Science


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