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Cell-free prediction of protein expression costs for growing cells

View ORCID ProfileOlivier Borkowski, View ORCID ProfileCarlos Bricio, Michaela Murgiano, View ORCID ProfileGuy-Bart Stan, View ORCID ProfileTom Ellis
doi: https://doi.org/10.1101/172627
Olivier Borkowski
1Centre for Synthetic Biology and Innovation, Imperial College London, London SW7 2AZ, UK.
2Department of Bioengineering, Imperial College London, London SW7 2AZ, UK.
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Carlos Bricio
1Centre for Synthetic Biology and Innovation, Imperial College London, London SW7 2AZ, UK.
2Department of Bioengineering, Imperial College London, London SW7 2AZ, UK.
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Michaela Murgiano
1Centre for Synthetic Biology and Innovation, Imperial College London, London SW7 2AZ, UK.
2Department of Bioengineering, Imperial College London, London SW7 2AZ, UK.
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Guy-Bart Stan
1Centre for Synthetic Biology and Innovation, Imperial College London, London SW7 2AZ, UK.
2Department of Bioengineering, Imperial College London, London SW7 2AZ, UK.
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Tom Ellis
1Centre for Synthetic Biology and Innovation, Imperial College London, London SW7 2AZ, UK.
2Department of Bioengineering, Imperial College London, London SW7 2AZ, UK.
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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 gene is a major challenge, as multiple processes and factors determine translation efficiency. Here, to enable prediction of the cost of gene expression in bacteria, we describe a standard cell-free lysate assay that determines the relationship between in vivo and cell-free measurements and γ, a relative measure of the resource consumption when a given protein is expressed. When combined with a computational model of translation, this enables prediction of 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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted August 04, 2017.
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Cell-free prediction of protein expression costs for growing cells
Olivier Borkowski, Carlos Bricio, Michaela Murgiano, Guy-Bart Stan, Tom Ellis
bioRxiv 172627; doi: https://doi.org/10.1101/172627
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Cell-free prediction of protein expression costs for growing cells
Olivier Borkowski, Carlos Bricio, Michaela Murgiano, Guy-Bart Stan, Tom Ellis
bioRxiv 172627; doi: https://doi.org/10.1101/172627

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