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Optimality and evolutionary tuning of the expression level of a protein

Author

Listed:
  • Erez Dekel

    (The Weizmann Institute of Science)

  • Uri Alon

    (The Weizmann Institute of Science)

Abstract

Different proteins have different expression levels. It is unclear to what extent these expression levels are optimized to their environment. Evolutionary theories suggest that protein expression levels maximize fitness1,2,3,4,5,6,7,8,9,10,11, but the fitness as a function of protein level has seldom been directly measured. To address this, we studied the lac system of Escherichia coli, which allows the cell to use the sugar lactose for growth12. We experimentally measured the growth burden13,14 due to production and maintenance of the Lac proteins (cost), as well as the growth advantage (benefit) conferred by the Lac proteins when lactose is present. The fitness function, given by the difference between the benefit and the cost, predicts that for each lactose environment there exists an optimal Lac expression level that maximizes growth rate. We then performed serial dilution evolution experiments at different lactose concentrations. In a few hundred generations, cells evolved to reach the predicted optimal expression levels. Thus, protein expression from the lac operon seems to be a solution of a cost–benefit optimization problem, and can be rapidly tuned by evolution to function optimally in new environments.

Suggested Citation

  • Erez Dekel & Uri Alon, 2005. "Optimality and evolutionary tuning of the expression level of a protein," Nature, Nature, vol. 436(7050), pages 588-592, July.
    • Handle: RePEc:nat:nature:v:436:y:2005:i:7050:d:10.1038_nature03842
    DOI: 10.1038/nature03842
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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. de Oliveira, Viviane M. & Amado, André & Campos, Paulo R.A., 2018. "The interplay of tradeoffs within the framework of a resource-based modelling," Ecological Modelling, Elsevier, vol. 384(C), pages 249-260.
    3. Avraham E Mayo & Yaakov Setty & Seagull Shavit & Alon Zaslaver & Uri Alon, 2006. "Plasticity of the cis-Regulatory Input Function of a Gene," PLOS Biology, Public Library of Science, vol. 4(4), pages 1-1, March.
    4. David A Sivak & Matt Thomson, 2014. "Environmental Statistics and Optimal Regulation," PLOS Computational Biology, Public Library of Science, vol. 10(9), pages 1-12, September.
    5. 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.
    6. Ruoyu Luo & Lin Ye & Chenyang Tao & Kankan Wang, 2013. "Simulation of E. coli Gene Regulation including Overlapping Cell Cycles, Growth, Division, Time Delays and Noise," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-10, April.
    7. Rok Grah & Tamar Friedlander, 2020. "The relation between crosstalk and gene regulation form revisited," PLOS Computational Biology, Public Library of Science, vol. 16(2), pages 1-24, February.
    8. William R Harcombe & Nigel F Delaney & Nicholas Leiby & Niels Klitgord & Christopher J Marx, 2013. "The Ability of Flux Balance Analysis to Predict Evolution of Central Metabolism Scales with the Initial Distance to the Optimum," PLOS Computational Biology, Public Library of Science, vol. 9(6), pages 1-11, June.

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