A Comparison of Methods for Quantifying Prediction Uncertainty in Systems Biology

7 pages, 4 figures, 2 tablesThe 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 applicationThis research has received funding from the European Unions Horizon 2020 research and innovation program under grant agreement No 686282 (CanPathPro) and the German Ministry of Education and Research (BMBF) under the grant agreement No 01ZX1310B (SYS-Stomach) and No 01ZX1705A (INCOME)Peer reviewe