PyNetMet : python tools for efficient work with networks and metabolic models

• Autor(a) principal: Gamermann, Daniel
• Data de Publicação: 2014
• Outros Autores: Montagud Aquino, Arnau, Infante, Ramon Jaime, Triana, Julian, Urchueguía Schölzel, Javier Fermín, Fernández de Córdoba Castellá, Pedro José
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFRGS
• Texto Completo: http://hdl.handle.net/10183/103727
• topic: Biologia computacional

The 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/.