Efficient Reconstruction of Predictive Consensus Metabolic Network Models

PLoS Comput Biol. 2016 Aug 26;12(8):e1005085. doi: 10.1371/journal.pcbi.1005085. eCollection 2016 Aug.


Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Bacteria / metabolism
  • Computational Biology / methods*
  • Databases, Genetic
  • Metabolic Networks and Pathways / physiology*
  • Models, Biological*
  • Software
  • Yeasts / metabolism

Grants and funding

We gratefully acknowledge financial support from the Swiss Initiative for Systems Biology (SystemsX.ch, project MetaNetX) reviewed by the Swiss National Science Foundation (SNF), the Wageningen university IPOP project, and the European projects INFECT (Project reference: 305340) and EmPowerPutida (Project reference: 635536). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.