Knowledge-based generalization of metabolic networks: a practical study
ZHUKOVA, Anna
Models and Algorithms for the Genome [ MAGNOME]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Models and Algorithms for the Genome [ MAGNOME]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
SHERMAN, David James
Models and Algorithms for the Genome [ MAGNOME]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Models and Algorithms for the Genome [ MAGNOME]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
ZHUKOVA, Anna
Models and Algorithms for the Genome [ MAGNOME]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Models and Algorithms for the Genome [ MAGNOME]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
SHERMAN, David James
Models and Algorithms for the Genome [ MAGNOME]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
< Réduire
Models and Algorithms for the Genome [ MAGNOME]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Langue
en
Article de revue
Ce document a été publié dans
Journal of Bioinformatics and Computational Biology. 2014, vol. 12(2), n° 1441001
World Scientific Publishing
Résumé en anglais
The complex process of genome-scale metabolic network reconstruction involves semi- automatic reaction inference, analysis, and refinement through curation by human experts. Unfortunately, decisions by experts are hampered ...Lire la suite >
The complex process of genome-scale metabolic network reconstruction involves semi- automatic reaction inference, analysis, and refinement through curation by human experts. Unfortunately, decisions by experts are hampered by the complexity of the network, which can mask errors in the inferred network. In order to aid an expert in making sense out of the thousands of reactions in the organism's metabolism, we developed a method for knowledge-based generalization that provides a higher-level view of the network, highlighting the particularities and essential structure, while hiding the details. In this study, we show the application of this generalization method to 1286 metabolic networks of organisms in Path2Models that describe fatty acid metabolism. We compare the generalized networks and show that we successfully highlight the aspects that are important for their curation and comparison.< Réduire
Mots clés en anglais
Metabolic modeling
generalization
genome-scale reconstruction
fatty acid metabolism
Origine
Importé de halUnités de recherche