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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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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. |
format | Online Article Text |
id | pubmed-5905618 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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