Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks
Artikel i vetenskaplig tidskrift, 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.

Författare

Sylvain Prigent

Chalmers, Biologi och bioteknik, Systembiologi

Clémence Frioux

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

Simon M. Dittami

Universite Pierre et Marie Curie (UPMC)

Sven Thiele

Max Planck-institutet

INRIA Institut National de Recherche en Informatique et en Automatique

Abdelhalim Larhlimi

Laboratoire d'Informatique de Nantes-Atlantique

Guillaume Collet

INRIA Institut National de Recherche en Informatique et en Automatique

Institut de Recherche en Informatique et Systemes Aleatoires

CNRS Centre National de la Recherche Scientifique

Fabien Gutknecht

Universite de Strasbourg

Jeanne Got

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

Damien Eveillard

Laboratoire d'Informatique de Nantes-Atlantique

Jérémie Bourdon

Laboratoire d'Informatique de Nantes-Atlantique

Frédéric Plewniak

CNRS Centre National de la Recherche Scientifique

Universite de Strasbourg

Thierry Tonon

University of York

Universite Pierre et Marie Curie (UPMC)

Anne Siegel

INRIA Institut National de Recherche en Informatique et en Automatique

CNRS Centre National de la Recherche Scientifique

Institut de Recherche en Informatique et Systemes Aleatoires

PLoS Computational Biology

1553-734X (ISSN) 1553-7358 (eISSN)

Vol. 13 Artno:e1005276- e1005276

Ämneskategorier

Bioinformatik och systembiologi

DOI

10.1371/journal.pcbi.1005276