Evidence map›Paper›PMID 35773252›Full record

ArticleNature communications2022

Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0.

Iván Domenzain, Benjamín Sánchez, Mihail Anton, Eduard J Kerkhoven, Aarón Millán-Oropeza, Céline Henry, Verena Siewers, John P Morrissey, Nikolaus Sonnenschein, Jens Nielsen

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers.

0numbers the graph read from it
0cells of the map it votes in
71citing papers in PubMed
10.7field-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

71 citing papers in PubMed, 136 citations in OpenAlex.

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  12. Bioinformatics Approaches in Noncoding RNAs Research.Experientia supplementum (2012) · 2026
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  15. Unravelling the Complexity of Sarcopenia Through a Systems Biology Approach.International journal of molecular sciences · 2025
    Review
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11 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

10 authors at 6 institutions in 4 countries.

Iván DomenzainDepartment of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden.ORCID http://orcid.org/0000-0002-5322-2040
Benjamín Sánchez *Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800, Kongens Lyngby, Denmark.ORCID http://orcid.org/0000-0001-6093-4110
Mihail Anton *Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden.ORCID http://orcid.org/0000-0002-7753-9042
Eduard J KerkhovenDepartment of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden.ORCID http://orcid.org/0000-0002-3593-5792
Aarón Millán-OropezaPlateforme d'analyse protéomique Paris Sud-Ouest (PAPPSO), INRAE, MICALIS Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France.
Céline HenryPlateforme d'analyse protéomique Paris Sud-Ouest (PAPPSO), INRAE, MICALIS Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France.ORCID http://orcid.org/0000-0002-2355-1791
Verena SiewersDepartment of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden.ORCID http://orcid.org/0000-0002-9502-9804
John P MorrisseySchool of Microbiology, Environmental Research Institute and APC Microbiome Ireland, University College Cork, T12 K8AF, Cork, Ireland.ORCID http://orcid.org/0000-0001-7960-2001
Nikolaus SonnenscheinDepartment of Biotechnology and Biomedicine, Technical University of Denmark, 2800, Kongens Lyngby, Denmark.
Jens NielsenDepartment of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden. nielsenj@chalmers.se.ORCID http://orcid.org/0000-0002-9955-6003
Chalmers University of Technology · SEUniversité Paris-Saclay · FRBioInnovation Institute · DKNovo Nordisk Foundation · DKTechnical University of Denmark · DKUniversity College Cork · IE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into such models was first enabled by the GECKO toolbox, allowing the study of phenotypes constrained by protein limitations. Here, we upgrade the toolbox in order to enhance models with enzyme and proteomics constraints for any organism with a compatible GEM reconstruction. With this, enzyme-constrained models for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus are generated to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions reveal that upregulation and high saturation of enzymes in amino acid metabolism are common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO is expanded with an automated framework for continuous and version-controlled update of enzyme-constrained GEMs, also producing such models for Escherichia coli and Homo sapiens. In this work, we facilitate the utilization of enzyme-constrained GEMs in basic science, metabolic engineering and synthetic biology purposes.

Indexed as

Metabolic EngineeringModels, BiologicalEscherichia coliGenotypeHumansKluyveromycesPhenotypeSaccharomyces cerevisiaeSynthetic BiologyYarrowia

Identifiers

PMID35773252
PMCPMC9246944
OpenAlexW4283733937

What OpenQuestion holds

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LicenceCC BY
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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.