ArticleBMC systems biology2018
How accurate is automated gap filling of metabolic models?
Article in BMC systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Evaluating plant growth-defense trade-offs by modeling the interaction between primary and secondary metabolism.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Seed2LP: seed inference in metabolic networks for reverse ecology applications.Bioinformatics (Oxford, England) · 2025Article
- MACAW: a method for semi-automatic detection of errors in genome-scale metabolic models.Genome biology · 2025Article
- Mathematical models of the colonic microbiota: an evaluation of accuracy usingFrontiers in nutrition · 2025Article
- Charting the state of GEMs in microalgae: progress, challenges, and innovations.Frontiers in plant science · 2025Review
- Interactive tools for functional annotation of bacterial genomes.Database : the journal of biological databases and curation · 2024Article
- Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing.Microbial biotechnology · 2024Review
- An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification.Journal of cheminformatics · 2023Article
- Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations.Briefings in bioinformatics · 2023Review
- Genome-scale metabolic modeling of the human gut bacterium Bacteroides fragilis strain 638R.PLoS computational biology · 2023Article
- Identifying the essential nutritional requirements of the probiotic bacteria Bifidobacterium animalis and Bifidobacterium longum through genome-scale modeling.NPJ systems biology and applications · 2021Article
- MiMiC: a bioinformatic approach for generation of synthetic communities from metagenomes.Microbial biotechnology · 2021Article
- Addressing uncertainty in genome-scale metabolic model reconstruction and analysis.Genome biology · 2021Review
- Modelling microbial communities: Harnessing consortia for biotechnological applications.Computational and structural biotechnology journal · 2021Review
- MetGEMs Toolbox: Metagenome-scale models as integrative toolbox for uncovering metabolic functions and routes of human gut microbiome.PLoS computational biology · 2021Article
- Biochemical Characteristics and a Genome-Scale Metabolic Model of an Indian Euryhaline Cyanobacterium with High Polyglucan Content.Metabolites · 2020Article
- The engineering of spatially linked microbial consortia - potential and perspectives.Current opinion in biotechnology · 2020Review
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3 authors.
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Abstract
backgroundReaction gap filling is a computational technique for proposing the addition of reactions to genome-scale metabolic models to permit those models to run correctly. Gap filling completes what are otherwise incomplete models that lack fully connected metabolic networks. The models are incomplete because they are derived from annotated genomes in which not all enzymes have been identified. Here we compare the results of applying an automated likelihood-based gap filler within the Pathway Tools software with the results of manually gap filling the same metabolic model. Both gap-filling exercises were applied to the same genome-derived qualitative metabolic reconstruction for Bifidobacterium longum subsp. longum JCM 1217, and to the same modeling conditions - anaerobic growth under four nutrients producing 53 biomass metabolites.
resultsThe solution computed by the gap-filling program GenDev contained 12 reactions, but closer examination showed that solution was not minimal; two of the twelve reactions can be removed to yield a set of ten reactions that enable model growth. The manually curated solution contained 13 reactions, eight of which were shared with the 12-reaction computed solution. Thus, GenDev achieved recall of 61.5% and precision of 66.6%. These results suggest that although computational gap fillers are populating metabolic models with significant numbers of correct reactions, automatically gap-filled metabolic models also contain significant numbers of incorrect reactions.
conclusionsOur conclusion is that manual curation of gap-filler results is needed to obtain high-accuracy models. Many of the differences between the manual and automatic solutions resulted from using expert biological knowledge to direct the choice of reactions within the curated solution, such as reactions specific to the anaerobic lifestyle of B. longum.
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