TY - GEN A1 - Dierkes, Thomas A1 - Röblitz, Susanna A1 - Wade, Moritz A1 - Deuflhard, Peter T1 - Parameter identification in large kinetic networks with BioPARKIN N2 - Motivation. Modelling, parameter identification, and simulation play an important role in systems biology. Usually, the goal is to determine parameter values that minimise the difference between experimental measurement values and model predictions in a least-squares sense. Large-scale biological networks, however, often suffer from missing data for parameter identification. Thus, the least-squares problems are rank-deficient and solutions are not unique. Many common optimisation methods ignore this detail because they do not take into account the structure of the underlying inverse problem. These algorithms simply return a “solution” without additional information on identifiability or uniqueness. This can yield misleading results, especially if parameters are co-regulated and data are noisy. Results. The Gauss-Newton method presented in this paper monitors the numerical rank of the Jacobian and converges locally, for the class of adequate problems, to a solution that is unique within the subspace of identifiable parameters. This method has been implemented in BioPARKIN, a software package that combines state-of-the-art numerical algorithms with compliance to system biology standards, most importantly SBML, and an accessible interface. Availability. The software package BioPARKIN is available for download at http://bioparkin.zib.de . KW - affine invariant Gauss-Newton algorithm KW - graphical user interface KW - numerical library KW - ordinary differential equations KW - parameter identification KW - sensitivity analysis KW - systems biology Y1 - 2013 UR - https://opus4.kobv.de/opus4-matheon/frontdoor/index/index/docId/1239 UR - https://nbn-resolving.org/urn:nbn:de:0296-matheon-12398 ER -