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A quasi-integral controller for adaptation of genetic modules to variable ribosome demand

Author

Listed:
  • Hsin-Ho Huang

    (Department of Mechanical Engineering)

  • Yili Qian

    (Department of Mechanical Engineering)

  • Domitilla Del Vecchio

    (Department of Mechanical Engineering)

Abstract

The behavior of genetic circuits is often poorly predictable. A gene’s expression level is not only determined by the intended regulators, but also affected by changes in ribosome availability imparted by expression of other genes. Here we design a quasi-integral biomolecular feedback controller that enables the expression level of any gene of interest (GOI) to adapt to changes in available ribosomes. The feedback is implemented through a synthetic small RNA (sRNA) that silences the GOI’s mRNA, and uses orthogonal extracytoplasmic function (ECF) sigma factor to sense the GOI’s translation and to actuate sRNA transcription. Without the controller, the expression level of the GOI is reduced by 50% when a resource competitor is activated. With the controller, by contrast, gene expression level is practically unaffected by the competitor. This feedback controller allows adaptation of genetic modules to variable ribosome demand and thus aids modular construction of complicated circuits.

Suggested Citation

  • Hsin-Ho Huang & Yili Qian & Domitilla Del Vecchio, 2018. "A quasi-integral controller for adaptation of genetic modules to variable ribosome demand," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-07899-z
    DOI: 10.1038/s41467-018-07899-z
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    Citations

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    Cited by:

    1. 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.
    2. Ankit Gupta & Mustafa Khammash, 2022. "Frequency spectra and the color of cellular noise," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    3. Ross D. Jones & Yili Qian & Katherine Ilia & Benjamin Wang & Michael T. Laub & Domitilla Del Vecchio & Ron Weiss, 2022. "Robust and tunable signal processing in mammalian cells via engineered covalent modification cycles," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    4. Baiyang Liu & Christian Cuba Samaniego & Matthew R. Bennett & Elisa Franco & James Chappell, 2023. "A portable regulatory RNA array design enables tunable and complex regulation across diverse bacteria," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    5. Stanislav Anastassov & Maurice Filo & Ching-Hsiang Chang & Mustafa Khammash, 2023. "A cybergenetic framework for engineering intein-mediated integral feedback control systems," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    6. Roberto Di Blasi & Mara Pisani & Fabiana Tedeschi & Masue M. Marbiah & Karen Polizzi & Simone Furini & Velia Siciliano & Francesca Ceroni, 2023. "Resource-aware construct design in mammalian cells," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    7. Carlos Barajas & Hsin-Ho Huang & Jesse Gibson & Luis Sandoval & Domitilla Vecchio, 2022. "Feedforward growth rate control mitigates gene activation burden," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    8. Yuanli Gao & Lei Wang & Baojun Wang, 2023. "Customizing cellular signal processing by synthetic multi-level regulatory circuits," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

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