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Rapid prototyping and design of cybergenetic single-cell controllers

Author

Listed:
  • Sant Kumar

    (ETH Zürich)

  • Marc Rullan

    (ETH Zürich)

  • Mustafa Khammash

    (ETH Zürich)

Abstract

The design and implementation of synthetic circuits that operate robustly in the cellular context is fundamental for the advancement of synthetic biology. However, their practical implementation presents challenges due to low predictability of synthetic circuit design and time-intensive troubleshooting. Here, we present the Cyberloop, a testing framework to accelerate the design process and implementation of biomolecular controllers. Cellular fluorescence measurements are sent in real-time to a computer simulating candidate stochastic controllers, which in turn compute the control inputs and feed them back to the controlled cells via light stimulation. Applying this framework to yeast cells engineered with optogenetic tools, we examine and characterize different biomolecular controllers, test the impact of non-ideal circuit behaviors such as dilution on their operation, and qualitatively demonstrate improvements in controller function with certain network modifications. From this analysis, we derive conditions for desirable biomolecular controller performance, thereby avoiding pitfalls during its biological implementation.

Suggested Citation

  • Sant Kumar & Marc Rullan & Mustafa Khammash, 2021. "Rapid prototyping and design of cybergenetic single-cell controllers," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25754-6
    DOI: 10.1038/s41467-021-25754-6
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    Cited by:

    1. 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.
    2. Maurice Filo & Sant Kumar & Mustafa Khammash, 2022. "A hierarchy of biomolecular proportional-integral-derivative feedback controllers for robust perfect adaptation and dynamic performance," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    3. Jean-Baptiste Lugagne & Caroline M. Blassick & Mary J. Dunlop, 2024. "Deep model predictive control of gene expression in thousands of single cells," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    4. Ankit Gupta & Mustafa Khammash, 2022. "Frequency spectra and the color of cellular noise," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    5. Zhou Fang & Ankit Gupta & Sant Kumar & Mustafa Khammash, 2024. "Advanced methods for gene network identification and noise decomposition from single-cell data," Nature Communications, Nature, vol. 15(1), pages 1-19, December.

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