Evidence map›Paper›PMID 39134886›Full record

ArticleMolecular systems biology2024

Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community.

Chengyu Zhang, Benjamín J Sánchez, Feiran Li, Cheng Wei Quan Eiden, William T Scott, Ulf W Liebal, Lars M Blank, Hendrik G Mengers, Mihail Anton, Albert Tafur Rangel and 5 more

Abstract read
In one paragraph

Article in Molecular systems biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
–field-weighted citation impact
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

33 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Review
  9. Reconstruction of human metabolic models with large language models.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  10. Article
  11. Review
  12. Review
  13. Systems biology of yeast metabolism.FEMS yeast research · 2026
    Review
  14. Article
  15. Computational and structural biotechnology journal · 2026
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. The Power of Yeast.Yeast (Chichester, England) · 2025
    Review
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

15 authors.

Chengyu ZhangState Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 200240, Shanghai, China.ORCID http://orcid.org/0009-0004-3006-9073
Benjamín J SánchezThe Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark.ORCID http://orcid.org/0000-0001-6093-4110
Feiran LiInstitute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China.ORCID http://orcid.org/0000-0001-9155-5260
Cheng Wei Quan EidenSchool of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, Singapore, 637459, Singapore.
William T ScottUNLOCK, Wageningen University & Research, Wageningen, The Netherlands.ORCID http://orcid.org/0000-0002-4029-2998
Ulf W LiebalInstitute of Applied Microbiology - iAMB, Aachen Biology and Biotechnology - ABBt, RWTH Aachen University, 52074, Aachen, Germany.
Lars M BlankInstitute of Applied Microbiology - iAMB, Aachen Biology and Biotechnology - ABBt, RWTH Aachen University, 52074, Aachen, Germany.ORCID http://orcid.org/0000-0003-0961-4976
Hendrik G MengersInstitute of Applied Microbiology - iAMB, Aachen Biology and Biotechnology - ABBt, RWTH Aachen University, 52074, Aachen, Germany.
Mihail AntonDepartment of Life Sciences, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Gothenburg, SE412 58, Sweden.ORCID http://orcid.org/0000-0002-7753-9042
Albert Tafur RangelThe Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark.ORCID http://orcid.org/0000-0002-9428-183X
Sebastián N MendozaCenter for Mathematical Modeling, University of Chile, Santiago, Chile.ORCID http://orcid.org/0000-0002-2192-5569
Lixin ZhangState Key Laboratory of Bioreactor Engineering, and School of Biotechnology, East China University of Science and Technology (ECUST), 200237, Shanghai, China.
Jens NielsenDepartment of Life Sciences, Chalmers University of Technology, Gothenburg, SE412 96, Sweden.
Hongzhong LuState Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 200240, Shanghai, China. hongzhonglu@sjtu.edu.cn.ORCID http://orcid.org/0009-0005-8555-1841
Eduard J KerkhovenThe Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark. eduardk@chalmers.se.ORCID http://orcid.org/0000-0002-3593-5792

Funding

111 Plan | Overseas Expertise Introduction Project for Discipline Innovation B18022Agencia Nacional de Investigación y Desarrollo (ANID) 13220002Consejo Nacional de Innovación, Ciencia y Tecnología (CONICYT) 72180373Deutsche Forschungsgemeinschaft (DFG) 2186EC | Horizon 2020 Framework Programme (H2020) 686070EC | Horizon 2020 Framework Programme (H2020) 720824MOST | National Key Research and Development Program of China (NKPs) 2020YFA0907800MOST | National Key Research and Development Program of China (NKPs) 2022YFA0913000MOST | National Natural Science Foundation of China (NSFC) 22208211 and 22378263Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) 184.035.007Novo Nordisk Fonden (NNF) NNF20CC0035580UC | FCFM | Centro de Modelamiento Matemático, Facultad de Ciencias Físicas y Matemáticas (CMM) ACE210010 and FB210005
6 · The paper itself

Abstract

Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This has increased the quality and scope of the model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or reference conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference for nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains' growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism.

Indexed as

Genome, FungalModels, BiologicalSaccharomyces cerevisiaeCarbonGene Expression ProfilingMetabolic Networks and PathwaysNitrogenOsmotic PressureProteomicsSaccharomyces cerevisiae ProteinsSystems BiologyTranscriptomeCarbonNitrogenSaccharomyces cerevisiae ProteinsGenome-scale Metabolic ModelsMachine LearningMulti-omics IntegrationSaccharomyces cerevisiae

Identifiers

PMID39134886
PMCPMC11450192

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.