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Cell-free prediction of protein expression costs for growing cells

Author

Listed:
  • Olivier Borkowski

    (Imperial College London
    Imperial College London)

  • Carlos Bricio

    (Imperial College London
    Imperial College London)

  • Michela Murgiano

    (Imperial College London
    Imperial College London)

  • Brooke Rothschild-Mancinelli

    (Imperial College London
    Imperial College London)

  • Guy-Bart Stan

    (Imperial College London
    Imperial College London)

  • Tom Ellis

    (Imperial College London
    Imperial College London)

Abstract

Translating heterologous proteins places significant burden on host cells, consuming expression resources leading to slower cell growth and productivity. Yet predicting the cost of protein production for any given gene is a major challenge, as multiple processes and factors combine to determine translation efficiency. To enable prediction of the cost of gene expression in bacteria, we describe here a standard cell-free lysate assay that provides a relative measure of resource consumption when a protein coding sequence is expressed. These lysate measurements can then be used with a computational model of translation to predict the in vivo burden placed on growing E. coli cells for a variety of proteins of different functions and lengths. Using this approach, we can predict the burden of expressing multigene operons of different designs and differentiate between the fraction of burden related to gene expression compared to action of a metabolic pathway.

Suggested Citation

  • Olivier Borkowski & Carlos Bricio & Michela Murgiano & Brooke Rothschild-Mancinelli & Guy-Bart Stan & Tom Ellis, 2018. "Cell-free prediction of protein expression costs for growing cells," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-03970-x
    DOI: 10.1038/s41467-018-03970-x
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    Cited by:

    1. Amir Pandi & Christoph Diehl & Ali Yazdizadeh Kharrazi & Scott A. Scholz & Elizaveta Bobkova & Léon Faure & Maren Nattermann & David Adam & Nils Chapin & Yeganeh Foroughijabbari & Charles Moritz & Nic, 2022. "A versatile active learning workflow for optimization of genetic and metabolic networks," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Bob Sluijs & Roel J. M. Maas & Ardjan J. Linden & Tom F. A. Greef & Wilhelm T. S. Huck, 2022. "A microfluidic optimal experimental design platform for forward design of cell-free genetic networks," Nature Communications, Nature, vol. 13(1), pages 1-11, December.

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