IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1003625.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003625
    Download Restriction: no
    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003625&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pcbi.1003625?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Federico Sevlever & Juan Pablo Di Bella & Alejandra C Ventura, 2020. "Discriminating between negative cooperativity and ligand binding to independent sites using pre-equilibrium properties of binding curves," PLOS Computational Biology, Public Library of Science, vol. 16(6), pages 1-21, June.
    2. Wei, Bo & Liu, Jie & Wei, Daijun & Gao, Cai & Deng, Yong, 2015. "Weighted k-shell decomposition for complex networks based on potential edge weights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 277-283.
    3. Andreas Koulouris & Ioannis Katerelos & Theodore Tsekeris, 2013. "Multi-Equilibria Regulation Agent-Based Model of Opinion Dynamics in Social Networks," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 11(1), pages 51-70.
    4. Samuel Bandara & Johannes P Schlöder & Roland Eils & Hans Georg Bock & Tobias Meyer, 2009. "Optimal Experimental Design for Parameter Estimation of a Cell Signaling Model," PLOS Computational Biology, Public Library of Science, vol. 5(11), pages 1-12, November.
    5. He, He & Yang, Bo & Hu, Xiaoming, 2016. "Exploring community structure in networks by consensus dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 342-353.
    6. Ellinas, Christos & Allan, Neil & Johansson, Anders, 2016. "Project systemic risk: Application examples of a network model," International Journal of Production Economics, Elsevier, vol. 182(C), pages 50-62.
    7. Yang, Hyeonchae & Jung, Woo-Sung, 2016. "Structural efficiency to manipulate public research institution networks," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 21-32.
    8. Bo Zhang & Jianping Yuan & J. F. Pan & Xiaoyu Wu & Jianjun Luo & Li Qiu, 2017. "Global Feedback Control for Coordinated Linear Switched Reluctance Machines Network with Full-State Observation and Internal Model Compensation," Energies, MDPI, vol. 10(12), pages 1-19, December.
    9. Meng, Tao & Duan, Gaopeng & Li, Aming & Wang, Long, 2023. "Control energy scaling for target control of complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    10. Yan Zhang & Antonios Garas & Frank Schweitzer, 2019. "Control Contribution Identifies Top Driver Nodes In Complex Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(07n08), pages 1-15, December.
    11. Tao Jia & Robert F Spivey & Boleslaw Szymanski & Gyorgy Korniss, 2015. "An Analysis of the Matching Hypothesis in Networks," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-12, June.
    12. Jun Feng & Zhenting Li & Shizhen Zhang & Chun Bao & Jingxian Fang & Yun Yin & Bolei Chen & Lei Pan & Bing Wang & Yu Zheng, 2023. "A Microimage-Processing-Based Technique for Detecting Qualitative and Quantitative Characteristics of Plant Cells," Agriculture, MDPI, vol. 13(9), pages 1-16, September.
    13. Yang, Xu-Hua & Lou, Shun-Li & Chen, Guang & Chen, Sheng-Yong & Huang, Wei, 2013. "Scale-free networks via attaching to random neighbors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3531-3536.
    14. Zhang, Rui & Wang, Xiaomeng & Cheng, Ming & Jia, Tao, 2019. "The evolution of network controllability in growing networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 257-266.
    15. Wouter Vermeer & Otto Koppius & Peter Vervest, 2018. "The Radiation-Transmission-Reception (RTR) model of propagation: Implications for the effectiveness of network interventions," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-21, December.
    16. Neil Johnson & Guannan Zhao & Eric Hunsader & Jing Meng & Amith Ravindar & Spencer Carran & Brian Tivnan, 2012. "Financial black swans driven by ultrafast machine ecology," Papers 1202.1448, arXiv.org.
    17. Zhdanov, Vladimir P., 2012. "Periodic perturbation of genetic oscillations," Chaos, Solitons & Fractals, Elsevier, vol. 45(5), pages 577-587.
    18. Chen, Shi-Ming & Xu, Yun-Fei & Nie, Sen, 2017. "Robustness of network controllability in cascading failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 536-539.
    19. Xizhe Zhang & Huaizhen Wang & Tianyang Lv, 2017. "Efficient target control of complex networks based on preferential matching," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-10, April.
    20. Badhwar, Rahul & Bagler, Ganesh, 2017. "A distance constrained synaptic plasticity model of C. elegans neuronal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 313-322.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1003625. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.