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Journal Articles eLife Year : 2023

Resource allocation accounts for the large variability of rate-yield phenotypes across bacterial strains

Abstract

Different strains of a microorganism growing in the same environment display a wide variety of growth rates and growth yields. We developed a coarse-grained model to test the hypothesis that different resource allocation strategies, corresponding to different compositions of the proteome, can account for the observed rate-yield variability. The model predictions were verified by means of a database of hundreds of published rate-yield and uptake-secretion phenotypes of Escherichia coli strains grown in standard laboratory conditions. We found a very good quantitative agreement between the range of predicted and observed growth rates, growth yields, and glucose uptake and acetate secretion rates. These results support the hypothesis that resource allocation is a major explanatory factor of the observed variability of growth rates and growth yields across different bacterial strains. An interesting prediction of our model, supported by the experimental data, is that high growth rates are not necessarily accompanied by low growth yields. The resource allocation strategies enabling high-rate, high-yield growth of E. coli lead to a higher saturation of enzymes and ribosomes, and thus to a more efficient utilization of proteomic resources. Our model thus contributes to a fundamental understanding of the quantitative relationship between rate and yield in E. coli and other microorganisms. It may also be useful for the rapid screening of strains in metabolic engineering and synthetic biology.
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hal-04145943 , version 1 (02-06-2022)
hal-04145943 , version 2 (20-12-2023)

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Valentina Baldazzi, Delphine Ropers, Jean-Luc Gouzé, Tomas Gedeon, Hidde de Jong. Resource allocation accounts for the large variability of rate-yield phenotypes across bacterial strains. eLife, 2023, 12, pp.1-29. ⟨10.7554/eLife.79815⟩. ⟨hal-04145943v2⟩
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