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An analytical theory of balanced cellular growth

Author

Listed:
  • Hugo Dourado

    (Heinrich Heine University)

  • Martin J. Lercher

    (Heinrich Heine University)

Abstract

The biological fitness of microbes is largely determined by the rate with which they replicate their biomass composition. Mathematical models that maximize this balanced growth rate while accounting for mass conservation, reaction kinetics, and limits on dry mass per volume are inevitably non-linear. Here, we develop a general theory for such models, termed Growth Balance Analysis (GBA), which provides explicit expressions for protein concentrations, fluxes, and growth rates. These variables are functions of the concentrations of cellular components, for which we calculate marginal fitness costs and benefits that are related to metabolic control coefficients. At maximal growth rate, the net benefits of all concentrations are equal. Based solely on physicochemical constraints, GBA unveils fundamental quantitative principles of cellular resource allocation and growth; it accurately predicts the relationship between growth rates and ribosome concentrations in E. coli and yeast and between growth rate and dry mass density in E. coli.

Suggested Citation

  • Hugo Dourado & Martin J. Lercher, 2020. "An analytical theory of balanced cellular growth," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14751-w
    DOI: 10.1038/s41467-020-14751-w
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    Cited by:

    1. Matteo Mori & Chuankai Cheng & Brian R. Taylor & Hiroyuki Okano & Terence Hwa, 2023. "Functional decomposition of metabolism allows a system-level quantification of fluxes and protein allocation towards specific metabolic functions," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    2. Alexander Kroll & Yvan Rousset & Xiao-Pan Hu & Nina A. Liebrand & Martin J. Lercher, 2023. "Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    3. Gaoyang Li & Li Liu & Wei Du & Huansheng Cao, 2023. "Local flux coordination and global gene expression regulation in metabolic modeling," Nature Communications, Nature, vol. 14(1), pages 1-18, December.

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