Fast integration-based prediction bands for ordinary differential equation models
17.12.2015 Hass H, Kreutz C, Timmer J, Kaschek D Bioinformatics. 2015 Dec 17. [Epub ahead of print] To gain a deeper understanding of biological processes and their relevance in disease, mathematical models are built upon experimental data. Uncertainty in the data leads to uncertainties of the model's parameters and in turn to uncertainties of predictions. Mechanistic dynamic models of biochemical networks are frequently based on nonlinear differential equation systems and feature a large number of parameters, sparse observations of the model components and lack of information in the available data. Due to the curse of dimensionality, classical and sampling approaches propagating parameter uncertainties to predictions are hardly feasible and insufficient. However, for experimental design and to discriminate between competing models, prediction and confidence bands are essential. To circumvent the hurdles of the former methods, an approach to calculate a profile likelihood on arbitrary observations for a specific time point has been introduced, which provides accurate confidence and prediction intervals for nonlinear models and is computationally feasible for high-dimensional models (Kreutz et al., 2012).In this paper, reliable and smooth point-wise prediction and confidence bands to assess the model's uncertainty on the whole time-course are achieved via explicit integration with elaborate correction mechanisms. The corresponding system of ordinary differential equations (ODEs) is derived and tested on three established models for cellular signalling. An efficiency analysis is performed to illustrate the computational benefit compared to repeated profile likelihood calculations at multiple time points. 