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Dynamic cybergenetic control of bacterial co-culture composition via optogenetic feedback

Author

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  • Joaquín Gutiérrez Mena

    (Department of Biosystems Science and Engineering (D-BSSE), ETH Zürich)

  • Sant Kumar

    (Department of Biosystems Science and Engineering (D-BSSE), ETH Zürich)

  • Mustafa Khammash

    (Department of Biosystems Science and Engineering (D-BSSE), ETH Zürich)

Abstract

Communities of microbes play important roles in natural environments and hold great potential for deploying division-of-labor strategies in synthetic biology and bioproduction. However, the difficulty of controlling the composition of microbial consortia over time hinders their optimal use in many applications. Here, we present a fully automated, high-throughput platform that combines real-time measurements and computer-controlled optogenetic modulation of bacterial growth to implement precise and robust compositional control of a two-strain E. coli community. In addition, we develop a general framework for dynamic modeling of synthetic genetic circuits in the physiological context of E. coli and use a host-aware model to determine the optimal control parameters of our closed-loop compositional control system. Our platform succeeds in stabilizing the strain ratio of multiple parallel co-cultures at arbitrary levels and in changing these targets over time, opening the door for the implementation of dynamic compositional programs in synthetic bacterial communities.

Suggested Citation

  • Joaquín Gutiérrez Mena & Sant Kumar & Mustafa Khammash, 2022. "Dynamic cybergenetic control of bacterial co-culture composition via optogenetic feedback," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32392-z
    DOI: 10.1038/s41467-022-32392-z
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    References listed on IDEAS

    as
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    Cited by:

    1. Kirill Sechkar & Harrison Steel & Giansimone Perrino & Guy-Bart Stan, 2024. "A coarse-grained bacterial cell model for resource-aware analysis and design of synthetic gene circuits," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    2. Michael B. Sheets & Nathan Tague & Mary J. Dunlop, 2023. "An optogenetic toolkit for light-inducible antibiotic resistance," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

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