Efficient Reconstruction of Predictive Consensus Metabolic Network Models

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. COMMGE...

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Bibliographic Details
Published in:PLoS computational biology 2016-08, Vol.12 (8), p.e1005085-e1005085
Main Authors: van Heck, Ruben G A, Ganter, Mathias, Martins Dos Santos, Vitor A P, Stelling, Joerg
Format: Article
Language:English
Subjects:
Algorithms
Bacteria - metabolism
Biology and Life Sciences
Computational Biology - methods
Computer and Information Sciences
Databases, Genetic
Enzymes
Gene expression
Genetic aspects
Genome-wide association studies
Genomes
Knowledge
Linear programming
Medicine and Health Sciences
Metabolic Networks and Pathways - physiology
Metabolism
Metabolites
Models, Biological
Observations
Organisms
Pathogens
Physical Sciences
Software
Systeem en Synthetische Biologie
Systems and Synthetic Biology
VLAG
Yeasts - metabolism
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Summary: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.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1005085