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A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism

Author

Listed:
  • Hongzhong Lu

    (Chalmers University of Technology)

  • Feiran Li

    (Chalmers University of Technology)

  • Benjamín J. Sánchez

    (Chalmers University of Technology)

  • Zhengming Zhu

    (Chalmers University of Technology
    Jiangnan University)

  • Gang Li

    (Chalmers University of Technology)

  • Iván Domenzain

    (Chalmers University of Technology)

  • Simonas Marcišauskas

    (Chalmers University of Technology)

  • Petre Mihail Anton

    (Chalmers University of Technology)

  • Dimitra Lappa

    (Chalmers University of Technology)

  • Christian Lieven

    (Technical University of Denmark)

  • Moritz Emanuel Beber

    (Technical University of Denmark)

  • Nikolaus Sonnenschein

    (Technical University of Denmark)

  • Eduard J. Kerkhoven

    (Chalmers University of Technology)

  • Jens Nielsen

    (Chalmers University of Technology
    Technical University of Denmark
    BioInnovation Institute)

Abstract

Genome-scale metabolic models (GEMs) represent extensive knowledgebases that provide a platform for model simulations and integrative analysis of omics data. This study introduces Yeast8 and an associated ecosystem of models that represent a comprehensive computational resource for performing simulations of the metabolism of Saccharomyces cerevisiae––an important model organism and widely used cell-factory. Yeast8 tracks community development with version control, setting a standard for how GEMs can be continuously updated in a simple and reproducible way. We use Yeast8 to develop the derived models panYeast8 and coreYeast8, which in turn enable the reconstruction of GEMs for 1,011 different yeast strains. Through integration with enzyme constraints (ecYeast8) and protein 3D structures (proYeast8DB), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits.

Suggested Citation

  • Hongzhong Lu & Feiran Li & Benjamín J. Sánchez & Zhengming Zhu & Gang Li & Iván Domenzain & Simonas Marcišauskas & Petre Mihail Anton & Dimitra Lappa & Christian Lieven & Moritz Emanuel Beber & Nikola, 2019. "A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11581-3
    DOI: 10.1038/s41467-019-11581-3
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    Citations

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    Cited by:

    1. Mohammad H. Mirhakkak & Xiuqiang Chen & Yueqiong Ni & Thorsten Heinekamp & Tongta Sae-Ong & Lin-Lin Xu & Oliver Kurzai & Amelia E. Barber & Axel A. Brakhage & Sebastien Boutin & Sascha Schäuble & Gian, 2023. "Genome-scale metabolic modeling of Aspergillus fumigatus strains reveals growth dependencies on the lung microbiome," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    2. Ning Qin & Lingyun Li & Xiaozhen Wan & Xu Ji & Yu Chen & Chaokun Li & Ping Liu & Yijie Zhang & Weijie Yang & Junfeng Jiang & Jianye Xia & Shuobo Shi & Tianwei Tan & Jens Nielsen & Yun Chen & Zihe Liu, 2024. "Increased CO2 fixation enables high carbon-yield production of 3-hydroxypropionic acid in yeast," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    3. Iván Domenzain & Benjamín Sánchez & Mihail Anton & Eduard J. Kerkhoven & Aarón Millán-Oropeza & Céline Henry & Verena Siewers & John P. Morrissey & Nikolaus Sonnenschein & Jens Nielsen, 2022. "Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    4. Marius Arend & David Zimmer & Rudan Xu & Frederik Sommer & Timo Mühlhaus & Zoran Nikoloski, 2023. "Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    5. Philipp Wendering & Marius Arend & Zahra Razaghi-Moghadam & Zoran Nikoloski, 2023. "Data integration across conditions improves turnover number estimates and metabolic predictions," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    6. Feiran Li & Yu Chen & Qi Qi & Yanyan Wang & Le Yuan & Mingtao Huang & Ibrahim E. Elsemman & Amir Feizi & Eduard J. Kerkhoven & Jens Nielsen, 2022. "Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    7. Yameng Xu & Xinglong Wang & Chenyang Zhang & Xuan Zhou & Xianhao Xu & Luyao Han & Xueqin Lv & Yanfeng Liu & Song Liu & Jianghua Li & Guocheng Du & Jian Chen & Rodrigo Ledesma-Amaro & Long Liu, 2022. "De novo biosynthesis of rubusoside and rebaudiosides in engineered yeasts," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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