Letter | Published:

A global resource allocation strategy governs growth transition kinetics of Escherichia coli

Nature volume 551, pages 119123 (02 November 2017) | Download Citation

Abstract

A grand challenge of systems biology is to predict the kinetic responses of living systems to perturbations starting from the underlying molecular interactions. Changes in the nutrient environment have long been used to study regulation and adaptation phenomena in microorganisms1,2,3 and they remain a topic of active investigation4,5,6,7,8,9,10,11. Although much is known about the molecular interactions that govern the regulation of key metabolic processes in response to applied perturbations12,13,14,15,16,17, they are insufficiently quantified for predictive bottom-up modelling. Here we develop a top-down approach, expanding the recently established coarse-grained proteome allocation models15,18,19,20 from steady-state growth into the kinetic regime. Using only qualitative knowledge of the underlying regulatory processes and imposing the condition of flux balance, we derive a quantitative model of bacterial growth transitions that is independent of inaccessible kinetic parameters. The resulting flux-controlled regulation model accurately predicts the time course of gene expression and biomass accumulation in response to carbon upshifts and downshifts (for example, diauxic shifts) without adjustable parameters. As predicted by the model and validated by quantitative proteomics, cells exhibit suboptimal recovery kinetics in response to nutrient shifts owing to a rigid strategy of protein synthesis allocation, which is not directed towards alleviating specific metabolic bottlenecks. Our approach does not rely on kinetic parameters, and therefore points to a theoretical framework for describing a broad range of such kinetic processes without detailed knowledge of the underlying biochemical reactions.

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Acknowledgements

We are grateful to E. Mateescu for initiating this work and to J. Hasty and R. Young for discussions. This work is supported by the National Institutes of Health (NIH; grant 1R01GM109069) and by the Simons Foundation (grant 330378) through T.H., by the NIH (grant 1R01GM118850) through J.R.W., and by the German Research Foundation via the Excellence Cluster ‘Nanosystems Initiative Munich’ and the priority program SPP1617 (grant GE1098/6-2) through U.G.

Author information

Author notes

    • David W. Erickson
    •  & Severin J. Schink

    These authors contributed equally to this work.

Affiliations

  1. Department of Physics, University of California San Diego, La Jolla, California 92093, USA

    • David W. Erickson
    • , Severin J. Schink
    •  & Terence Hwa
  2. Physics of Complex Biosystems, Physics Department, Technical University of Munich, 85748 Garching, Germany

    • Severin J. Schink
    •  & Ulrich Gerland
  3. Department of Integrative Structural and Computational Biology, Department of Chemistry, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, California 92037, USA

    • Vadim Patsalo
    •  & James R. Williamson

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Contributions

D.W.E., S.J.S., U.G. and T.H. designed this study. Growth shifts were performed by D.W.E. and S.J.S. and analysed by S.J.S., D.W.E. and T.H. Quantitative mass spectrometry analysis was performed by V.P. and analysed by S.J.S., V.P. and J.R.W. D.W.E., S.J.S., U.G. and T.H. developed the model and all authors participated in writing the paper and the Supplementary Information.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Ulrich Gerland or Terence Hwa.

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Extended data

Supplementary information

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    Supplementary Information

    This file contains full descriptions for Supplementary Tables 1-4, Supplementary Notes and Supplementary references – see contents page for details.

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    Life Sciences Reporting Summary

CSV files

  1. 1.

    Supplementary Table 1

    This table contains relative expression data, see Supplementary Information document for full description.

  2. 2.

    Supplementary Table 2

    This table contains absolute expression data, see Supplementary Information document for full description.

  3. 3.

    Supplementary Table 3

    This table shows proteome sectors, see Supplementary Information document for full description.

  4. 4.

    Supplementary Table 4

    This table shows protein groups, see Supplementary Information document for full description.

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DOI

https://doi.org/10.1038/nature24299

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