ReviewQuantitative biology (Beijing, China)2023
Genome-scale metabolic models applied for human health and biopharmaceutical engineering.
Review in Quantitative biology (Beijing, China), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- A historical journey of metabolite-protein interaction discovery: from data harmonization to AI-driven prediction.Briefings in bioinformatics · 2026Review
- Advances in genome-scale metabolic modeling of Bacillus subtilis.Biotechnology letters · 2026Review
- Article
- Synthetic microbial communities: a novel emerging models for dissecting gut microbiota-host interactions in neurodegenerative diseases.Frontiers in immunology · 2026Review
- Integrative systems biology approaches for analyzing microbiome dysbiosis and species interactions.Briefings in bioinformatics · 2025Review
- Genome-scale metabolic models applied for human health and biopharmaceutical engineering.Quantitative biology (Beijing, China) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the last 15 years, genome-scale metabolic models (GEMs) have been reconstructed for human and model animals, such as mouse and rat, to systematically understand metabolism, simulate multicellular or multi-tissue interplay, understand human diseases, and guide cell factory design for biopharmaceutical protein production. Here, we describe how metabolic networks can be represented using stoichiometric matrices and well-defined constraints for flux simulation. Then, we review the history of GEM development for quantitative understanding of
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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.