ReviewBiotechnology and bioengineering2010
Systematizing the generation of missing metabolic knowledge.
Review in Biotechnology and bioengineering, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
73 citing papers in PubMed.
- Dynamical Systems-Constrained Metabolic Modeling Enables Forecasting of Host-Microbiome Dynamics.bioRxiv : the preprint server for biology · 2026Article
- A multi-way SMILES-based hypergraph inference network for metabolic model reconstruction.Communications biology · 2026Article
- MACAW: a method for semi-automatic detection of errors in genome-scale metabolic models.Genome biology · 2025Article
- Teasing out missing reactions in genome-scale metabolic networks through hypergraph learning.Nature communications · 2023Article
- Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems.Computational and structural biotechnology journal · 2023Review
- Past, Present, and Future of Genome Modification inMicroorganisms · 2022Review
- Network Reconstruction and Modelling Made Reproducible with moped.Metabolites · 2022Article
- Article
- Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes.Annual review of biomedical engineering · 2021Review
- IDARE2-Simultaneous Visualisation of Multiomics Data in Cytoscape.Metabolites · 2021Article
- Genomics-guided identification of a conserved CptBA-like toxin-antitoxin system inJournal of advanced research · 2021Article
- Systematically gap-filling the genome-scale metabolic model of CHO cells.Biotechnology letters · 2021Article
- Reconstructing organisms in silico: genome-scale models and their emerging applications.Nature reviews. Microbiology · 2020Review
- Taxonomic weighting improves the accuracy of a gap-filling algorithm for metabolic models.Bioinformatics (Oxford, England) · 2020Article
- Computational Modeling of the Human Microbiome.Microorganisms · 2020Review
- Article
- A workflow for generating multi-strain genome-scale metabolic models of prokaryotes.Nature protocols · 2020Article
- AMON: annotation of metabolite origins via networks to integrate microbiome and metabolome data.BMC bioinformatics · 2019Article
- Article
- Enzyme annotation for orphan and novel reactions using knowledge of substrate reactive sites.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
13 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Genome-scale metabolic network reconstructions are built from all of the known metabolic reactions and genes in a target organism. However, since our knowledge of any organism is incomplete, these network reconstructions contain gaps. Reactions may be missing, resulting in dead-ends in pathways, while unknown gene products may catalyze known reactions. New computational methods that analyze data, such as growth phenotypes or gene essentiality, in the context of genome-scale metabolic networks, have been developed to predict these missing reactions or genes likely to fill these knowledge gaps. A growing number of experimental studies are appearing that address these computational predictions, leading to discovery of new metabolic capabilities in the target organism. Gap-filling methods can thus be used to improve metabolic network models while simultaneously leading to discovery of new metabolic gene functions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.