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PSAMM: A Portable System for the Analysis of Metabolic Models
The genome-scale models of metabolic networks have been broadly applied in phenotype prediction, evolutionary reconstruction, community functional analysis, and metabolic engineering. Despite the development of tools that support individual steps along the modeling procedure, it is still difficult t...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4734835/ https://www.ncbi.nlm.nih.gov/pubmed/26828591 http://dx.doi.org/10.1371/journal.pcbi.1004732 |
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author | Steffensen, Jon Lund Dufault-Thompson, Keith Zhang, Ying |
author_facet | Steffensen, Jon Lund Dufault-Thompson, Keith Zhang, Ying |
author_sort | Steffensen, Jon Lund |
collection | PubMed |
description | The genome-scale models of metabolic networks have been broadly applied in phenotype prediction, evolutionary reconstruction, community functional analysis, and metabolic engineering. Despite the development of tools that support individual steps along the modeling procedure, it is still difficult to associate mathematical simulation results with the annotation and biological interpretation of metabolic models. In order to solve this problem, here we developed a Portable System for the Analysis of Metabolic Models (PSAMM), a new open-source software package that supports the integration of heterogeneous metadata in model annotations and provides a user-friendly interface for the analysis of metabolic models. PSAMM is independent of paid software environments like MATLAB, and all its dependencies are freely available for academic users. Compared to existing tools, PSAMM significantly reduced the running time of constraint-based analysis and enabled flexible settings of simulation parameters using simple one-line commands. The integration of heterogeneous, model-specific annotation information in PSAMM is achieved with a novel format of YAML-based model representation, which has several advantages, such as providing a modular organization of model components and simulation settings, enabling model version tracking, and permitting the integration of multiple simulation problems. PSAMM also includes a number of quality checking procedures to examine stoichiometric balance and to identify blocked reactions. Applying PSAMM to 57 models collected from current literature, we demonstrated how the software can be used for managing and simulating metabolic models. We identified a number of common inconsistencies in existing models and constructed an updated model repository to document the resolution of these inconsistencies. |
format | Online Article Text |
id | pubmed-4734835 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-47348352016-02-04 PSAMM: A Portable System for the Analysis of Metabolic Models Steffensen, Jon Lund Dufault-Thompson, Keith Zhang, Ying PLoS Comput Biol Research Article The genome-scale models of metabolic networks have been broadly applied in phenotype prediction, evolutionary reconstruction, community functional analysis, and metabolic engineering. Despite the development of tools that support individual steps along the modeling procedure, it is still difficult to associate mathematical simulation results with the annotation and biological interpretation of metabolic models. In order to solve this problem, here we developed a Portable System for the Analysis of Metabolic Models (PSAMM), a new open-source software package that supports the integration of heterogeneous metadata in model annotations and provides a user-friendly interface for the analysis of metabolic models. PSAMM is independent of paid software environments like MATLAB, and all its dependencies are freely available for academic users. Compared to existing tools, PSAMM significantly reduced the running time of constraint-based analysis and enabled flexible settings of simulation parameters using simple one-line commands. The integration of heterogeneous, model-specific annotation information in PSAMM is achieved with a novel format of YAML-based model representation, which has several advantages, such as providing a modular organization of model components and simulation settings, enabling model version tracking, and permitting the integration of multiple simulation problems. PSAMM also includes a number of quality checking procedures to examine stoichiometric balance and to identify blocked reactions. Applying PSAMM to 57 models collected from current literature, we demonstrated how the software can be used for managing and simulating metabolic models. We identified a number of common inconsistencies in existing models and constructed an updated model repository to document the resolution of these inconsistencies. Public Library of Science 2016-02-01 /pmc/articles/PMC4734835/ /pubmed/26828591 http://dx.doi.org/10.1371/journal.pcbi.1004732 Text en © 2016 Steffensen et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Steffensen, Jon Lund Dufault-Thompson, Keith Zhang, Ying PSAMM: A Portable System for the Analysis of Metabolic Models |
title | PSAMM: A Portable System for the Analysis of Metabolic Models |
title_full | PSAMM: A Portable System for the Analysis of Metabolic Models |
title_fullStr | PSAMM: A Portable System for the Analysis of Metabolic Models |
title_full_unstemmed | PSAMM: A Portable System for the Analysis of Metabolic Models |
title_short | PSAMM: A Portable System for the Analysis of Metabolic Models |
title_sort | psamm: a portable system for the analysis of metabolic models |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4734835/ https://www.ncbi.nlm.nih.gov/pubmed/26828591 http://dx.doi.org/10.1371/journal.pcbi.1004732 |
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