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Bioinformatics. 2018 Apr 15;34(8):1421-1423. doi: 10.1093/bioinformatics/btx735.

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

Author information

1
Faculty of Physics and Center for NanoScience (CeNS), Ludwig-Maximilians-Universität, 80539 München, Germany.
2
Institute of Computational Biology, Helmholtz Zentrum München, 85764 München, Germany.
3
Center of Mathematics, Technische Universität München, 85748 München, Germany.
4
Technological Institute for Industrial Mathematics, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain.
5
(Bio)Process Engineering Group, Spanish National Research Council, IIM-CSIC, 36208 Vigo, Spain.

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.

Availability and implementation:

GenSSI 2.0 is an open-source MATLAB toolbox and available at https://github.com/genssi-developer/GenSSI.

Contact:

thomas.ligon@physik.uni-muenchen.de or jan.hasenauer@helmholtz-muenchen.de.

Supplementary information:

Supplementary data are available at Bioinformatics online.

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