A Comparison of Methods for Quantifying Prediction Uncertainty in Systems Biology. Issue 26 (2019)

• Record Type: Journal Article
• Authors: Villaverde, Alejandro F.; Raimúndez, Elba; Hasenauer, Jan; Banga, Julio R.
• Is Part Of: IFAC-PapersOnLine. Volume 52:Issue 26(2019)
• Page Start: 45
• Page End: 51
• Publication Date: 2019
• Subjects: Computational methods -- Dynamic models -- Nonlinear systems -- Observability -- Prediction error methods -- State estimation -- Uncertainty; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2019.12.234
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: The parameters of dynamical models of biological processes always possess some degree of uncertainty. This parameter uncertainty translates into an uncertainty of model predictions. The trajectories of unmeasured state variables are examples of such predictions. Quantifying the uncertainty associated with a given prediction is an important problem for model developers and users. However, the nonlinearity and complexity of most dynamical models renders it nontrivial. Here, we evaluate three state-of-the-art approaches for prediction uncertainty quantification using two models of different sizes and computational complexities. We discuss the trade-offs between applicability and statistical interpretability of the different methods, and provide guidelines for their application.