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Optimality and sub-optimality in a bacterial growth law

Author

Listed:
  • Benjamin D. Towbin

    (Weizmann Institute of Science)

  • Yael Korem

    (Weizmann Institute of Science)

  • Anat Bren

    (Weizmann Institute of Science)

  • Shany Doron

    (Weizmann Institute of Science)

  • Rotem Sorek

    (Weizmann Institute of Science)

  • Uri Alon

    (Weizmann Institute of Science)

Abstract

Organisms adjust their gene expression to improve fitness in diverse environments. But finding the optimal expression in each environment presents a challenge. We ask how good cells are at finding such optima by studying the control of carbon catabolism genes in Escherichia coli. Bacteria show a growth law: growth rate on different carbon sources declines linearly with the steady-state expression of carbon catabolic genes. We experimentally modulate gene expression to ask if this growth law always maximizes growth rate, as has been suggested by theory. We find that the growth law is optimal in many conditions, including a range of perturbations to lactose uptake, but provides sub-optimal growth on several other carbon sources. Combining theory and experiment, we genetically re-engineer E. coli to make sub-optimal conditions into optimal ones and vice versa. We conclude that the carbon growth law is not always optimal, but represents a practical heuristic that often works but sometimes fails.

Suggested Citation

  • Benjamin D. Towbin & Yael Korem & Anat Bren & Shany Doron & Rotem Sorek & Uri Alon, 2017. "Optimality and sub-optimality in a bacterial growth law," Nature Communications, Nature, vol. 8(1), pages 1-8, April.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms14123
    DOI: 10.1038/ncomms14123
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    Cited by:

    1. Zihan Wang & Akshit Goyal & Veronika Dubinkina & Ashish B. George & Tong Wang & Yulia Fridman & Sergei Maslov, 2021. "Complementary resource preferences spontaneously emerge in diauxic microbial communities," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    2. 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.
    3. Robert Planqué & Josephus Hulshof & Bas Teusink & Johannes C Hendriks & Frank J Bruggeman, 2018. "Maintaining maximal metabolic flux by gene expression control," PLOS Computational Biology, Public Library of Science, vol. 14(9), pages 1-20, September.
    4. Klement Stojanovski & Helge Großhans & Benjamin D. Towbin, 2022. "Coupling of growth rate and developmental tempo reduces body size heterogeneity in C. elegans," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

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