Sloppiness and the Geometry of Parameter Space

Brian K. Mannakee, Aaron P. Ragsdale, Mark K. Transtrum, Ryan N. Gutenkunst

Research output: Chapter in Book/Report/Conference proceedingChapter

25 Scopus citations


When modeling complex biological systems, exploring parameter space is critical, because parameter values are typically poorly known a priori. This exploration can be challenging, because parameter space often has high dimension and complex structure. Recent work, however, has revealed universal structure in parameter space of models for nonlinear systems. In particular, models are often sloppy, with strong parameter correlations and an exponential range of parameter sensitivities. Here we review the evidence for universal sloppiness and its implications for parameter fitting, model prediction, and experimental design. In principle, one can transform parameters to alleviate sloppiness, but a parameterization-independent information geometry perspective reveals deeper universal structure. We thus also review the recent insights offered by information geometry, particularly in regard to sloppiness and numerical methods.

Original languageEnglish (US)
Title of host publicationStudies in Mechanobiology, Tissue Engineering and Biomaterials
Number of pages29
StatePublished - 2016

Publication series

NameStudies in Mechanobiology, Tissue Engineering and Biomaterials
ISSN (Print)1868-2006
ISSN (Electronic)1868-2014


  • Bayesian ensembles
  • Cost functions
  • Experimental design
  • Hessian
  • Information geometry
  • Sloppiness

ASJC Scopus subject areas

  • Biotechnology
  • Biophysics
  • Medicine (miscellaneous)
  • Biomaterials
  • Biomedical Engineering
  • Mechanics of Materials


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