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Independent control of mean and noise by convolution of gene expression distributions

Author

Listed:
  • Karl P. Gerhardt

    (Rice University)

  • Satyajit D. Rao

    (Rice University)

  • Evan J. Olson

    (Rice University)

  • Oleg A. Igoshin

    (Rice University
    Rice University
    Rice University
    Rice University)

  • Jeffrey J. Tabor

    (Rice University
    Rice University)

Abstract

Gene expression noise can reduce cellular fitness or facilitate processes such as alternative metabolism, antibiotic resistance, and differentiation. Unfortunately, efforts to study the impacts of noise have been hampered by a scaling relationship between noise and expression level from individual promoters. Here, we use theory to demonstrate that mean and noise can be controlled independently by expressing two copies of a gene from separate inducible promoters in the same cell. We engineer low and high noise inducible promoters to validate this result in Escherichia coli, and develop a model that predicts the experimental distributions. Finally, we use our method to reveal that the response of a promoter to a repressor is less sensitive with higher repressor noise and explain this result using a law from probability theory. Our approach can be applied to investigate the effects of noise on diverse biological pathways or program cellular heterogeneity for synthetic biology applications.

Suggested Citation

  • Karl P. Gerhardt & Satyajit D. Rao & Evan J. Olson & Oleg A. Igoshin & Jeffrey J. Tabor, 2021. "Independent control of mean and noise by convolution of gene expression distributions," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-27070-5
    DOI: 10.1038/s41467-021-27070-5
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    References listed on IDEAS

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    1. William J. Blake & Mads KÆrn & Charles R. Cantor & J. J. Collins, 2003. "Noise in eukaryotic gene expression," Nature, Nature, vol. 422(6932), pages 633-637, April.
    2. Dirk Benzinger & Mustafa Khammash, 2018. "Pulsatile inputs achieve tunable attenuation of gene expression variability and graded multi-gene regulation," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    3. Michelle Hung & Emily Chang & Razika Hussein & Katya Frazier & Jung-Eun Shin & Shiori Sagawa & Han N. Lim, 2014. "Modulating the frequency and bias of stochastic switching to control phenotypic variation," Nature Communications, Nature, vol. 5(1), pages 1-11, December.
    4. Attila Becskei & Luis Serrano, 2000. "Engineering stability in gene networks by autoregulation," Nature, Nature, vol. 405(6786), pages 590-593, June.
    5. Long Cai & Nir Friedman & X. Sunney Xie, 2006. "Stochastic protein expression in individual cells at the single molecule level," Nature, Nature, vol. 440(7082), pages 358-362, March.
    6. Martin Ackermann & Bärbel Stecher & Nikki E. Freed & Pascal Songhet & Wolf-Dietrich Hardt & Michael Doebeli, 2008. "Self-destructive cooperation mediated by phenotypic noise," Nature, Nature, vol. 454(7207), pages 987-990, August.
    7. Alain R. Bonny & João Pedro Fonseca & Jesslyn E. Park & Hana El-Samad, 2021. "Orthogonal control of mean and variability of endogenous genes in a human cell line," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
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