title: Learning (from) the errors of a systems biology model.
abstract: Mathematical modelling is a labour intensive process involving several iterations of testing on real data and manual model modifications. In biology, the domain knowledge guiding model development is in many cases itself incomplete and uncertain. A major problem in this context is that biological systems are open. Missed or unknown external influences as well as erroneous interactions in the model could thus lead to severely misleading results. Here we introduce the dynamic elastic-net, a data driven mathematical method which automatically detects such model errors in ordinary differential equation (ODE) models. We demonstrate for real and simulated data, how the dynamic elastic-net approach can be used to automatically (i) reconstruct the error signal, (ii) identify the target variables of model error, and (iii) reconstruct the true system state even for incomplete or preliminary models. Our work provides a systematic computational method facilitating modelling of open biological systems under uncertain knowledge.
authors: Rheinische Friedrich-Wilhelms-Universität Bonn, Institute for Computer Science, Algorithmic Bioinformatics, c/o Bonn-Aachen International Center for IT, Dahlmannstr. 2, 53113, Bonn, Germany.
authors: Benjamin
authors: B
authors: Engelhardt
authors: Rheinische Friedrich-Wilhelms-Universität Bonn, Institute for Computer Science, Algorithmic Bioinformatics, c/o Bonn-Aachen International Center for IT, Dahlmannstr. 2, 53113, Bonn, Germany.
authors: Holger
authors: H
authors: Frőhlich
authors: University of Applied Sciences Koblenz, RheinAhrCampus, Department of Mathematics and Technology, Joseph-Rovan-Allee 2, 53424 Remagen, Germany.
authors: Maik
authors: M
authors: Kschischo
mesh_terms: D003198:Computer Simulation; D006801:Humans; D007858:Learning; D008954:Models, Biological; D049490:Systems Biology / Q000706:statistics & numerical data*; D035501:Uncertainty