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Knowledge-based generalization of metabolic networks: a practical study

Anna Zhukova 1, 2, * David James Sherman 1, 2
* Corresponding author
1 MAGNOME - Models and Algorithms for the Genome
CNRS - Centre National de la Recherche Scientifique : UMR5800, UB - Université de Bordeaux, Inria Bordeaux - Sud-Ouest
Abstract : 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.
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Anna Zhukova, David James Sherman. Knowledge-based generalization of metabolic networks: a practical study. Journal of Bioinformatics and Computational Biology, World Scientific Publishing, 2014, 12(2) (1441001), ⟨10.1142/S0219720014410017⟩. ⟨hal-00906911⟩

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