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Ultrasensitive Negative Feedback Control: A Natural Approach for the Design of Synthetic Controllers

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  • Francesco Montefusco
  • Ozgur E Akman
  • Orkun S Soyer
  • Declan G Bates

Abstract

Many of the most important potential applications of Synthetic Biology will require the ability to design and implement high performance feedback control systems that can accurately regulate the dynamics of multiple molecular species within the cell. Here, we argue that the use of design strategies based on combining ultrasensitive response dynamics with negative feedback represents a natural approach to this problem that fully exploits the strongly nonlinear nature of cellular information processing. We propose that such feedback mechanisms can explain the adaptive responses observed in one of the most widely studied biomolecular feedback systems—the yeast osmoregulatory response network. Based on our analysis of such system, we identify strong links with a well-known branch of mathematical systems theory from the field of Control Engineering, known as Sliding Mode Control. These insights allow us to develop design guidelines that can inform the construction of feedback controllers for synthetic biological systems.

Suggested Citation

  • Francesco Montefusco & Ozgur E Akman & Orkun S Soyer & Declan G Bates, 2016. "Ultrasensitive Negative Feedback Control: A Natural Approach for the Design of Synthetic Controllers," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-22, August.
    • Handle: RePEc:plo:pone00:0161605
    DOI: 10.1371/journal.pone.0161605
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    1. Yong-Jun Shin & Leonidas Bleris, 2010. "Linear Control Theory for Gene Network Modeling," PLOS ONE, Public Library of Science, vol. 5(9), pages 1-16, September.
    2. Zhike Zi & Wolfram Liebermeister & Edda Klipp, 2010. "A Quantitative Study of the Hog1 MAPK Response to Fluctuating Osmotic Stress in Saccharomyces cerevisiae," PLOS ONE, Public Library of Science, vol. 5(3), pages 1-13, March.
    3. Carlos Gomez-Uribe & George C Verghese & Leonid A Mirny, 2007. "Operating Regimes of Signaling Cycles: Statics, Dynamics, and Noise Filtering," PLOS Computational Biology, Public Library of Science, vol. 3(12), pages 1-11, December.
    4. John R. S. Newman & Sina Ghaemmaghami & Jan Ihmels & David K. Breslow & Matthew Noble & Joseph L. DeRisi & Jonathan S. Weissman, 2006. "Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise," Nature, Nature, vol. 441(7095), pages 840-846, June.
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