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

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...

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Autores principales: Ligon, Thomas S, Fröhlich, Fabian, Chiş, Oana T, Banga, Julio R, Balsa-Canto, Eva, Hasenauer, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5905618/
https://www.ncbi.nlm.nih.gov/pubmed/29206901
http://dx.doi.org/10.1093/bioinformatics/btx735
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author Ligon, Thomas S
Fröhlich, Fabian
Chiş, Oana T
Banga, Julio R
Balsa-Canto, Eva
Hasenauer, Jan
author_facet Ligon, Thomas S
Fröhlich, Fabian
Chiş, Oana T
Banga, Julio R
Balsa-Canto, Eva
Hasenauer, Jan
author_sort Ligon, Thomas S
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-59056182018-04-23 GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models Ligon, Thomas S Fröhlich, Fabian Chiş, Oana T Banga, Julio R Balsa-Canto, Eva Hasenauer, Jan Bioinformatics Applications Notes 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2018-04-15 2017-11-30 /pmc/articles/PMC5905618/ /pubmed/29206901 http://dx.doi.org/10.1093/bioinformatics/btx735 Text en © The Author 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Ligon, Thomas S
Fröhlich, Fabian
Chiş, Oana T
Banga, Julio R
Balsa-Canto, Eva
Hasenauer, Jan
GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models
title GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models
title_full GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models
title_fullStr GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models
title_full_unstemmed GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models
title_short GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models
title_sort genssi 2.0: multi-experiment structural identifiability analysis of sbml models
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5905618/
https://www.ncbi.nlm.nih.gov/pubmed/29206901
http://dx.doi.org/10.1093/bioinformatics/btx735
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