Evidence map›Paper›PMID 31196170›Full record

ReviewGenome biology2019

Current status and applications of genome-scale metabolic models.

Changdai Gu, Gi Bae Kim, Won Jun Kim, Hyun Uk Kim, Sang Yup Lee

Open access · goldAbstract readReview
In one paragraph

Review in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 388 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
388citing papers in PubMed, 2 pooled it
32.7field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

388 citing papers in PubMed, 2 syntheses or guidelines pooled it, 808 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
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  10. Review
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  12. Metabolic Engineering for Gibberellic Acid Production inMolecules (Basel, Switzerland) · 2026
    Review
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  20. Multi-strain analysis ofmSystems · 2026
    Article

328 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Changdai GuDepartment of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
Gi Bae KimDepartment of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
Won Jun KimDepartment of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
Hyun Uk KimDepartment of Chemical and Biomolecular Engineering (BK21 Plus Program), Systems Biology and Medicine Laboratory, KAIST, Daejeon, 34141, Republic of Korea. ehukim@kaist.ac.kr.
Sang Yup LeeDepartment of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea. leesy@kaist.ac.kr.ORCID 0000-0003-0599-3091
Korea Advanced Institute of Science and Technology · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.

Indexed as

Models, BiologicalAnimalsGenomicsHumansMetabolic Networks and Pathways

Identifiers

PMID31196170
PMCPMC6567666
OpenAlexW2952015518

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.