Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood
University of Freiburg · German Cancer Research Center · +2 more institutions
Abstract
MOTIVATION: Mathematical description of biological reaction networks by differential equations leads to large models whose parameters are calibrated in order to optimally explain experimental data. Often only parts of the model can be observed directly. Given a model that sufficiently describes the measured data, it is important to infer how well model parameters are determined by the amount and quality of experimental data. This knowledge is essential for further investigation of model predictions. For this reason a major topic in modeling is identifiability analysis. RESULTS: We suggest an approach that exploits the profile likelihood. It enables to detect structural non-identifiabilities, which manifest in…
Citation impact
- FWCI
- 11.09
- Percentile
- 100%
- References
- 29
Authors
7- ARAndreas RaueCorresponding
University of Freiburg, German Cancer Research Center, Heidelberg University, DKFZ-ZMBH Alliance
- CKClemens Kreutz
University of Freiburg, German Cancer Research Center, Heidelberg University, DKFZ-ZMBH Alliance
- TMTim Maiwald
University of Freiburg, German Cancer Research Center, Heidelberg University, DKFZ-ZMBH Alliance
- JBJasmin Bachmann
University of Freiburg, German Cancer Research Center, Heidelberg University, DKFZ-ZMBH Alliance
- MSMarcel Schilling
University of Freiburg, German Cancer Research Center, Heidelberg University, DKFZ-ZMBH Alliance
Topics & keywords
- Identifiability
- Computer science
- Toolbox
- MATLAB
- Data mining
- Software
- Experimental data
- Exploit