Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks
Journal article, 2017
Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system.
Author
Sylvain Prigent
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Clémence Frioux
Institut de Recherche en Informatique et Systemes Aleatoires
CNRS Centre National de la Recherche Scientifique
INRIA Institut National de Recherche en Informatique et en Automatique
Simon M. Dittami
Universite Pierre et Marie Curie
Sven Thiele
Max Planck Institute for Dynamics of Complex Technical Systems
INRIA Institut National de Recherche en Informatique et en Automatique
Abdelhalim Larhlimi
Laboratoire d'Informatique de Nantes-Atlantique
Guillaume Collet
Institut de Recherche en Informatique et Systemes Aleatoires
INRIA Institut National de Recherche en Informatique et en Automatique
CNRS Centre National de la Recherche Scientifique
Fabien Gutknecht
Universite de Strasbourg
Jeanne Got
CNRS Centre National de la Recherche Scientifique
Institut de Recherche en Informatique et Systemes Aleatoires
INRIA Institut National de Recherche en Informatique et en Automatique
Damien Eveillard
Laboratoire d'Informatique de Nantes-Atlantique
Jérémie Bourdon
Laboratoire d'Informatique de Nantes-Atlantique
Frédéric Plewniak
Universite de Strasbourg
CNRS Centre National de la Recherche Scientifique
Thierry Tonon
Universite Pierre et Marie Curie
University of York
Anne Siegel
Institut de Recherche en Informatique et Systemes Aleatoires
CNRS Centre National de la Recherche Scientifique
INRIA Institut National de Recherche en Informatique et en Automatique
PLoS Computational Biology
1553-734X (ISSN) 1553-7358 (eISSN)
Vol. 13 1 Artno:e1005276- e1005276Subject Categories (SSIF 2011)
Bioinformatics and Systems Biology
DOI
10.1371/journal.pcbi.1005276