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In-Vivo Real-Time Control of Protein Expression from Endogenous and Synthetic Gene Networks

Author

Listed:
  • Filippo Menolascina
  • Gianfranco Fiore
  • Emanuele Orabona
  • Luca De Stefano
  • Mike Ferry
  • Jeff Hasty
  • Mario di Bernardo
  • Diego di Bernardo

Abstract

We describe an innovative experimental and computational approach to control the expression of a protein in a population of yeast cells. We designed a simple control algorithm to automatically regulate the administration of inducer molecules to the cells by comparing the actual protein expression level in the cell population with the desired expression level. We then built an automated platform based on a microfluidic device, a time-lapse microscopy apparatus, and a set of motorized syringes, all controlled by a computer. We tested the platform to force yeast cells to express a desired fixed, or time-varying, amount of a reporter protein over thousands of minutes. The computer automatically switched the type of sugar administered to the cells, its concentration and its duration, according to the control algorithm. Our approach can be used to control expression of any protein, fused to a fluorescent reporter, provided that an external molecule known to (indirectly) affect its promoter activity is available.Author Summary: A crucial feature of biological systems is their ability to maintain homeostasis in spite of ever-changing conditions. In engineering, this ability can be embedded in devices ranging from the thermostat to the autopilot of a modern plane using control systems which operate via a negative feedback mechanism: the quantity to be controlled is measured then subtracted from the desired reference value, and the resulting error is used to compute the control action to be implemented on the physical system (e.g. switching on or off the heating, changing the position of the rudder). Here, we developed and applied a method to regulate the expression level of a protein, in a growing population of cells over several generations, in a completely automatic fashion. We designed and implemented an integrated platform comprising a microfluidic device, a time-lapse microscopy apparatus, and a set of motorized syringes, all controlled by a computer. We tested the platform to force yeast cells to express a desired time-varying amount of a gene in yeast. Our method can be applied to control a protein of interest in vivo allowing to probe the function of biological systems in unprecedented ways.

Suggested Citation

  • Filippo Menolascina & Gianfranco Fiore & Emanuele Orabona & Luca De Stefano & Mike Ferry & Jeff Hasty & Mario di Bernardo & Diego di Bernardo, 2014. "In-Vivo Real-Time Control of Protein Expression from Endogenous and Synthetic Gene Networks," PLOS Computational Biology, Public Library of Science, vol. 10(5), pages 1-14, May.
    • Handle: RePEc:plo:pcbi00:1003625
    DOI: 10.1371/journal.pcbi.1003625
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    References listed on IDEAS

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    1. Joshua F Apgar & Jared E Toettcher & Drew Endy & Forest M White & Bruce Tidor, 2008. "Stimulus Design for Model Selection and Validation in Cell Signaling," PLOS Computational Biology, Public Library of Science, vol. 4(2), pages 1-10, February.
    2. Yang-Yu Liu & Jean-Jacques Slotine & Albert-László Barabási, 2011. "Controllability of complex networks," Nature, Nature, vol. 473(7346), pages 167-173, May.
    3. Matthew R. Bennett & Wyming Lee Pang & Natalie A. Ostroff & Bridget L. Baumgartner & Sujata Nayak & Lev S. Tsimring & Jeff Hasty, 2008. "Metabolic gene regulation in a dynamically changing environment," Nature, Nature, vol. 454(7208), pages 1119-1122, August.
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    1. Koichi Kobayashi, 2019. "Design of Fixed Points in Boolean Networks Using Feedback Vertex Sets and Model Reduction," Complexity, Hindawi, vol. 2019, pages 1-9, March.
    2. Artémis Llamosi & Andres M Gonzalez-Vargas & Cristian Versari & Eugenio Cinquemani & Giancarlo Ferrari-Trecate & Pascal Hersen & Gregory Batt, 2016. "What Population Reveals about Individual Cell Identity: Single-Cell Parameter Estimation of Models of Gene Expression in Yeast," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-18, February.

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