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dc.contributor.authorGamermann, Danielpt_BR
dc.contributor.authorMontagud Aquino, Arnaupt_BR
dc.contributor.authorInfante, Ramon Jaimept_BR
dc.contributor.authorTriana, Julianpt_BR
dc.contributor.authorUrchueguía Schölzel, Javier Fermínpt_BR
dc.contributor.authorFernández de Córdoba Castellá, Pedro Josépt_BR
dc.date.accessioned2014-09-24T02:12:55Zpt_BR
dc.date.issued2014pt_BR
dc.identifier.issn2219-1402pt_BR
dc.identifier.urihttp://hdl.handle.net/10183/103727pt_BR
dc.description.abstractThe complexity of genome-scale metabolic models and networks associated to biological systems makes the use of computational tools an essential element in the field of systems biology. Here we present PyNetMet, a Python library of tools to work with networks and metabolic models. These are open-source free tools for use in a Python platform, which adds considerably versatility to them when compared to their desktop similar. On the other hand, these tools allow one to work with different standards of metabolic models (OptGene and SBML) and the fact that they are programmed in Python opens the possibility of efficient integration with any other existing Python package. In order to illustrate the most important features and some uses of our software, we show results obtained in the analysis of metabolic models taken from the literature. For this purpose, three different models (one in OptGene and two in SBML format) were downloaded and throughly analyzed with our software. Also, we performed a comparison of the underlying metabolic networks of these models with randomly generated networks, pointing out the main differences between them. The PyNetMet package is available from the python package index (https://pypi.python.org/pypi/PyNetMet) for different platforms and documentation and more extensive illustrative examples can be found in the webpage pythonhosted.org/PyNetMet/.en
dc.format.mimetypeapplication/pdfpt_BR
dc.language.isoengpt_BR
dc.relation.ispartofComputational and mathematical biology. Kowloon, Hong Kong. Vol. 3, no. 5 (2014), 11 p.pt_BR
dc.rightsOpen Accessen
dc.subjectBiologia computacionalpt_BR
dc.titlePyNetMet : python tools for efficient work with networks and metabolic modelspt_BR
dc.typeArtigo de periódicopt_BR
dc.identifier.nrb000922075pt_BR
dc.type.originEstrangeiropt_BR


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