Extracting falsifiable predictions from sloppy models.
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.
Gutenkunst
Ryan N
RN
Laboratory of Atomic and Solid State Physics, Cornell University, Ithaca, NY 14853, USA. rng7@cornell.edu
Casey
Fergal P
FP
Waterfall
Joshua J
JJ
Myers
Christopher R
CR
Sethna
James P
JP
D000465:Algorithms; D016477:Artifacts; D001698:Biomedical Engineering / Q000379:methods; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005189:False Positive Reactions; D015870:Gene Expression / Q000502:physiology*; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008390:Markov Chains; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D015398:Signal Transduction / Q000502:physiology*
D020543:Proteome