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A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics

Author

Listed:
  • Omid Oftadeh

    (École Polytechnique Fédérale de Lausanne (EPFL))

  • Pierre Salvy

    (École Polytechnique Fédérale de Lausanne (EPFL)
    Cambrium GmbH)

  • Maria Masid

    (École Polytechnique Fédérale de Lausanne (EPFL))

  • Maxime Curvat

    (École Polytechnique Fédérale de Lausanne (EPFL)
    Quotient Suisse SA)

  • Ljubisa Miskovic

    (École Polytechnique Fédérale de Lausanne (EPFL))

  • Vassily Hatzimanikatis

    (École Polytechnique Fédérale de Lausanne (EPFL))

Abstract

Eukaryotic organisms play an important role in industrial biotechnology, from the production of fuels and commodity chemicals to therapeutic proteins. To optimize these industrial systems, a mathematical approach can be used to integrate the description of multiple biological networks into a single model for cell analysis and engineering. One of the most accurate models of biological systems include Expression and Thermodynamics FLux (ETFL), which efficiently integrates RNA and protein synthesis with traditional genome-scale metabolic models. However, ETFL is so far only applicable for E. coli. To adapt this model for Saccharomyces cerevisiae, we developed yETFL, in which we augmented the original formulation with additional considerations for biomass composition, the compartmentalized cellular expression system, and the energetic costs of biological processes. We demonstrated the ability of yETFL to predict maximum growth rate, essential genes, and the phenotype of overflow metabolism. We envision that the presented formulation can be extended to a wide range of eukaryotic organisms to the benefit of academic and industrial research.

Suggested Citation

  • Omid Oftadeh & Pierre Salvy & Maria Masid & Maxime Curvat & Ljubisa Miskovic & Vassily Hatzimanikatis, 2021. "A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25158-6
    DOI: 10.1038/s41467-021-25158-6
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    Cited by:

    1. 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.

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