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Laboratory evolution reveals a two-dimensional rate-yield tradeoff in microbial metabolism

  • Chuankai Cheng
  • , Edward J. O'Brien
  • , Douglas McCloskey
  • , Jose Utrilla
  • , Connor Olson
  • , Ryan A. LaCroix
  • , Troy E. Sandberg
  • , Adam M. Feist
  • , Bernhard O. Palsson
  • , Zachary A. King
    • Technical University of Denmark
    • University of California at San Diego

    Research output: Contribution to journalJournal articleResearchpeer-review

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    Abstract

    Growth rate and yield are fundamental features of microbial growth. However, we lack a mechanistic and quantitative understanding of the rate-yield relationship. Studies pairing computational predictions with experiments have shown the importance of maintenance energy and proteome allocation in explaining rate-yield tradeoffs and overflow metabolism. Recently, adaptive evolution experiments of Escherichia coli reveal a phenotypic diversity beyond what has been explained using simple models of growth rate versus yield. Here, we identify a two-dimensional rate-yield tradeoff in adapted E. coli strains where the dimensions are (A) a tradeoff between growth rate and yield and (B) a tradeoff between substrate (glucose) uptake rate and growth yield. We employ a multi-scale modeling approach, combining a previously reported coarse-grained small-scale proteome allocation model with a fine-grained genome-scale model of metabolism and gene expression (ME-model), to develop a quantitative description of the full rate-yield relationship for E. coli K-12 MG1655. The multi-scale analysis resolves the complexity of ME-model which hindered its practical use in proteome complexity analysis, and provides a mechanistic explanation of the two-dimensional tradeoff. Further, the analysis identifies modifications to the P/O ratio and the flux allocation between glycolysis and pentose phosphate pathway (PPP) as potential mechanisms that enable the tradeoff between glucose uptake rate and growth yield. Thus, the rate-yield tradeoffs that govern microbial adaptation to new environments are more complex than previously reported, and they can be understood in mechanistic detail using a multi-scale modeling approach.

    Original languageEnglish
    Article numbere1007066
    JournalPLOS Computational Biology
    Volume15
    Issue number6
    ISSN1553-7358
    DOIs
    Publication statusPublished - 2019

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