Cause and Cure of Sloppiness in Ordinary Differential Equation Models

Abstract

Data-based mathematical modeling of biochemical reaction networks, e.g. by nonlinear ordinary differential equation (ODE) models, has been successfully applied. In this context, parameter estimation and uncertainty analysis is a major task in order to assess the quality of the description of the system by the model. Recently, a broadened eigenvalue spectrum of the Hessian matrix of the objective function covering orders of magnitudes was observed and has been termed as sloppiness. In this work, we investigate the origin of sloppiness from structures in the sensitivity matrix arising from the properties of the model topology and the experimental design. Furthermore, we present strategies using optimal experimental design methods in order to circumvent the sloppiness issue and present non-sloppy designs for a benchmark model.

17 pages, 15 figures, submitted to Phys. Rev. E on 12 April 2014

References in corpus (1)

• The sloppy model universality class and the Vandermonde matrix

Cited by in corpus (4)

• Hierarchical Network Structure Promotes Dynamical Robustness
• Potential unsatisfiability of cyclic constraints on stochastic biological networks biases selection toward hierarchical architectures
• Observability and Structural Identifiability of Nonlinear Biological Systems
• Analysis of sloppiness in model simulations: unveiling parameter uncertainty when mathematical models are fitted to data