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

Thomas Ligon | Zendy; Fabian Fröhlich | Zendy; O. Chis | Zendy; Julio R. Banga | Zendy; Eva BalsaCanto | Zendy; Jan Hasenauer | Zendy

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

Author(s) -

Thomas Ligon,

Fabian Fröhlich,

O. Chis,

Julio R. Banga,

Eva BalsaCanto,

Jan Hasenauer

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx735

Subject(s) - sbml , identifiability , computer science , programming language , theoretical computer science , algorithm , markup language , machine learning , world wide web , xml

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.

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