GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models

Ligon, Thomas S.; Frohlich, Fabian; Chis, Oana T.; Banga, Julio R.; Balsa-Canto, Eva und Hasenauer, Jan (2018): GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models. In: Bioinformatics, Bd. 34, Nr. 8: S. 1421-1423

Abstract

Motivation: Mathematical modeling using ordinary differential equations is used in systems biology to improve the understanding of dynamic biological processes. The parameters of ordinary differential equation models are usually estimated from experimental data. To analyze a priori the uniqueness of the solution of the estimation problem, structural identifiability analysis methods have been developed. Results: We introduce GenSSI 2.0, an advancement of the software toolbox GenSSI (Generating Series for testing Structural Identifiability). GenSSI 2.0 is the first toolbox for structural identifiability analysis to implement Systems Biology Markup Language import, state/parameter transformations and multi-experiment structural identifiability analysis. In addition, GenSSI 2.0 supports a range of MATLAB versions and is computationally more efficient than its previous version, enabling the analysis of more complex models.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Physik
• Fakultätsübergreifende Einrichtungen: Center for NanoScience (CENS)
• Themengebiete: 500 Naturwissenschaften und Mathematik > 500 Naturwissenschaften ; 500 Naturwissenschaften und Mathematik > 530 Physik
• ISSN: 1367-4803
• Sprache: Englisch