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SBML Level 3 Package: Flux Balance Constraints version 2

Constraint-based modeling is a well established modeling methodology used to analyze and study biological networks on both a medium and genome scale. Due to their large size and complexity such steady-state flux models are, typically, analyzed using constraint-based optimization techniques, for exam...

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Detalles Bibliográficos
Autores principales: Olivier, Brett G., Bergmann, Frank T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: De Gruyter 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167036/
https://www.ncbi.nlm.nih.gov/pubmed/29522419
http://dx.doi.org/10.1515/jib-2017-0082
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author Olivier, Brett G.
Bergmann, Frank T.
author_facet Olivier, Brett G.
Bergmann, Frank T.
author_sort Olivier, Brett G.
collection PubMed
description Constraint-based modeling is a well established modeling methodology used to analyze and study biological networks on both a medium and genome scale. Due to their large size and complexity such steady-state flux models are, typically, analyzed using constraint-based optimization techniques, for example, flux balance analysis (FBA). The Flux balance constraints (FBC) Package extends SBML Level 3 and provides a standardized format for the encoding, exchange and annotation of constraint-based models. It includes support for modeling concepts such as objective functions, flux bounds and model component annotation that facilitates reaction balancing. Version two expands on the original release by adding official support for encoding gene-protein associations and their associated elements. In addition to providing the elements necessary to unambiguously encode existing constraint-based models, the FBC Package provides an open platform facilitating the continued, cross-community development of an interoperable, constraint-based model encoding format.
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spelling pubmed-61670362019-01-28 SBML Level 3 Package: Flux Balance Constraints version 2 Olivier, Brett G. Bergmann, Frank T. J Integr Bioinform Article Constraint-based modeling is a well established modeling methodology used to analyze and study biological networks on both a medium and genome scale. Due to their large size and complexity such steady-state flux models are, typically, analyzed using constraint-based optimization techniques, for example, flux balance analysis (FBA). The Flux balance constraints (FBC) Package extends SBML Level 3 and provides a standardized format for the encoding, exchange and annotation of constraint-based models. It includes support for modeling concepts such as objective functions, flux bounds and model component annotation that facilitates reaction balancing. Version two expands on the original release by adding official support for encoding gene-protein associations and their associated elements. In addition to providing the elements necessary to unambiguously encode existing constraint-based models, the FBC Package provides an open platform facilitating the continued, cross-community development of an interoperable, constraint-based model encoding format. De Gruyter 2018-03-09 /pmc/articles/PMC6167036/ /pubmed/29522419 http://dx.doi.org/10.1515/jib-2017-0082 Text en ©2018 Brett G. Olivier et al., published by De Gruyter http://creativecommons.org/licenses/by-nc-nd/3.0 This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
spellingShingle Article
Olivier, Brett G.
Bergmann, Frank T.
SBML Level 3 Package: Flux Balance Constraints version 2
title SBML Level 3 Package: Flux Balance Constraints version 2
title_full SBML Level 3 Package: Flux Balance Constraints version 2
title_fullStr SBML Level 3 Package: Flux Balance Constraints version 2
title_full_unstemmed SBML Level 3 Package: Flux Balance Constraints version 2
title_short SBML Level 3 Package: Flux Balance Constraints version 2
title_sort sbml level 3 package: flux balance constraints version 2
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167036/
https://www.ncbi.nlm.nih.gov/pubmed/29522419
http://dx.doi.org/10.1515/jib-2017-0082
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