|Title||Efficient Reconstruction of Predictive Consensus Metabolic Network Models|
|Author(s)||Heck, Ruben G.A. van; Ganter, Mathias; Martins dos Santos, Vitor A.P.; Stelling, Joerg|
|Source||PLoS Computational Biology 12 (2016)8. - ISSN 1553-734X|
Systems and Synthetic Biology
|Publication type||Refereed Article in a scientific journal|
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.