IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1008076.html
   My bibliography  Save this article

Multiplexing information flow through dynamic signalling systems

Author

Listed:
  • Giorgos Minas
  • Dan J Woodcock
  • Louise Ashall
  • Claire V Harper
  • Michael R H White
  • David A Rand

Abstract

We consider how a signalling system can act as an information hub by multiplexing information arising from multiple signals. We formally define multiplexing, mathematically characterise which systems can multiplex and how well they can do it. While the results of this paper are theoretical, to motivate the idea of multiplexing, we provide experimental evidence that tentatively suggests that the NF-κB transcription factor can multiplex information about changes in multiple signals. We believe that our theoretical results may resolve the apparent paradox of how a system like NF-κB that regulates cell fate and inflammatory signalling in response to diverse stimuli can appear to have the low information carrying capacity suggested by recent studies on scalar signals. In carrying out our study, we introduce new methods for the analysis of large, nonlinear stochastic dynamic models, and develop computational algorithms that facilitate the calculation of fundamental constructs of information theory such as Kullback–Leibler divergences and sensitivity matrices, and link these methods to a new theory about multiplexing information. We show that many current models such as those of the NF-κB system cannot multiplex effectively and provide models that overcome this limitation using post-transcriptional modifications.Author summary: Cells use signalling systems to pass on information arising from their ever-changing environment to their processing units. These biochemical networks regulate the transmission of multiple signals within the noisy and complex cellular environment, controlling whether to turn on or off processes of cell defence, death, division, and others. The question of how they actually achieve that becomes particularly critical given that many diseases occur when signalling systems malfunction. In this paper, we develop methodology and computational tools for simulating, measuring and analysing the ability of signalling systems to transmit multi-dimensional signals. We specifically focus on the capacity of signalling systems to simultaneously transmit multiple signals, such as temperature changes, presence and concentration of cytokines, viral and bacterial pathogens or drugs, through a single noisy, dynamic signalling system. We argue that a signalling system can act as an information hub, sending information in a multiplexed fashion rather similar to the way in which telecommunications networks send multiple signals over a shared medium by combining them into one.

Suggested Citation

  • Giorgos Minas & Dan J Woodcock & Louise Ashall & Claire V Harper & Michael R H White & David A Rand, 2020. "Multiplexing information flow through dynamic signalling systems," PLOS Computational Biology, Public Library of Science, vol. 16(8), pages 1-18, August.
  • Handle: RePEc:plo:pcbi00:1008076
    DOI: 10.1371/journal.pcbi.1008076
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008076
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008076&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1008076?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. Ryan N Gutenkunst & Joshua J Waterfall & Fergal P Casey & Kevin S Brown & Christopher R Myers & James P Sethna, 2007. "Universally Sloppy Parameter Sensitivities in Systems Biology Models," PLOS Computational Biology, Public Library of Science, vol. 3(10), pages 1-8, October.
    2. Long Cai & Chiraj K. Dalal & Michael B. Elowitz, 2008. "Frequency-modulated nuclear localization bursts coordinate gene regulation," Nature, Nature, vol. 455(7212), pages 485-490, September.
    3. Clive G Bowsher & Margaritis Voliotis & Peter S Swain, 2013. "The Fidelity of Dynamic Signaling by Noisy Biomolecular Networks," PLOS Computational Biology, Public Library of Science, vol. 9(3), pages 1-9, March.
    4. Giorgos Minas & David A Rand, 2017. "Long-time analytic approximation of large stochastic oscillators: Simulation, analysis and inference," PLOS Computational Biology, Public Library of Science, vol. 13(7), pages 1-23, July.
    5. Savaş Tay & Jacob J. Hughey & Timothy K. Lee & Tomasz Lipniacki & Stephen R. Quake & Markus W. Covert, 2010. "Single-cell NF-κB dynamics reveal digital activation and analogue information processing," Nature, Nature, vol. 466(7303), pages 267-271, July.
    Full references (including those not matched with items on IDEAS)

    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. Gabriele Micali & Gerardo Aquino & David M Richards & Robert G Endres, 2015. "Accurate Encoding and Decoding by Single Cells: Amplitude Versus Frequency Modulation," PLOS Computational Biology, Public Library of Science, vol. 11(6), pages 1-21, June.
    2. 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.
    3. Adel Dayarian & Madalena Chaves & Eduardo D Sontag & Anirvan M Sengupta, 2009. "Shape, Size, and Robustness: Feasible Regions in the Parameter Space of Biochemical Networks," PLOS Computational Biology, Public Library of Science, vol. 5(1), pages 1-12, January.
    4. Amrita X Sarkar & Eric A Sobie, 2010. "Regression Analysis for Constraining Free Parameters in Electrophysiological Models of Cardiac Cells," PLOS Computational Biology, Public Library of Science, vol. 6(9), pages 1-11, September.
    5. Hongwei Shao & Tao Peng & Zhiwei Ji & Jing Su & Xiaobo Zhou, 2013. "Systematically Studying Kinase Inhibitor Induced Signaling Network Signatures by Integrating Both Therapeutic and Side Effects," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-16, December.
    6. Alireza Yazdani & Lu Lu & Maziar Raissi & George Em Karniadakis, 2020. "Systems biology informed deep learning for inferring parameters and hidden dynamics," PLOS Computational Biology, Public Library of Science, vol. 16(11), pages 1-19, November.
    7. Fridtjof Brauns & Leila Iñigo de la Cruz & Werner K.-G. Daalman & Ilse Bruin & Jacob Halatek & Liedewij Laan & Erwin Frey, 2023. "Redundancy and the role of protein copy numbers in the cell polarization machinery of budding yeast," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    8. Eberhard O Voit & Harald A Martens & Stig W Omholt, 2015. "150 Years of the Mass Action Law," PLOS Computational Biology, Public Library of Science, vol. 11(1), pages 1-7, January.
    9. Céline Christiansen-Jucht & Kamil Erguler & Chee Yan Shek & María-Gloria Basáñez & Paul E. Parham, 2015. "Modelling Anopheles gambiae s.s. Population Dynamics with Temperature- and Age-Dependent Survival," IJERPH, MDPI, vol. 12(6), pages 1-31, May.
    10. Gabriele Lillacci & Mustafa Khammash, 2010. "Parameter Estimation and Model Selection in Computational Biology," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-17, March.
    11. Andrew White & Malachi Tolman & Howard D Thames & Hubert Rodney Withers & Kathy A Mason & Mark K Transtrum, 2016. "The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-26, December.
    12. Diego Fernández Slezak & Cecilia Suárez & Guillermo A Cecchi & Guillermo Marshall & Gustavo Stolovitzky, 2010. "When the Optimal Is Not the Best: Parameter Estimation in Complex Biological Models," PLOS ONE, Public Library of Science, vol. 5(10), pages 1-10, October.
    13. Elba Raimúndez & Simone Keller & Gwen Zwingenberger & Karolin Ebert & Sabine Hug & Fabian J Theis & Dieter Maier & Birgit Luber & Jan Hasenauer, 2020. "Model-based analysis of response and resistance factors of cetuximab treatment in gastric cancer cell lines," PLOS Computational Biology, Public Library of Science, vol. 16(3), pages 1-21, March.
    14. Joseph D Taylor & Samuel Winnall & Alain Nogaret, 2020. "Estimation of neuron parameters from imperfect observations," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-22, July.
    15. Xinxian Shao & Andrew Mugler & Justin Kim & Ha Jun Jeong & Bruce R Levin & Ilya Nemenman, 2017. "Growth of bacteria in 3-d colonies," PLOS Computational Biology, Public Library of Science, vol. 13(7), pages 1-19, July.
    16. Andreas Doncic & Umut Eser & Oguzhan Atay & Jan M Skotheim, 2013. "An Algorithm to Automate Yeast Segmentation and Tracking," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-11, March.
    17. Maksat Ashyraliyev & Ken Siggens & Hilde Janssens & Joke Blom & Michael Akam & Johannes Jaeger, 2009. "Gene Circuit Analysis of the Terminal Gap Gene huckebein," PLOS Computational Biology, Public Library of Science, vol. 5(10), pages 1-16, October.
    18. Martiny, Emil S. & Jensen, Mogens H. & Heltberg, Mathias S., 2022. "Detecting limit cycles in stochastic time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    19. Joachim Almquist & Loubna Bendrioua & Caroline Beck Adiels & Mattias Goksör & Stefan Hohmann & Mats Jirstrand, 2015. "A Nonlinear Mixed Effects Approach for Modeling the Cell-To-Cell Variability of Mig1 Dynamics in Yeast," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-32, April.
    20. Agus Hartoyo & Peter J Cadusch & David T J Liley & Damien G Hicks, 2019. "Parameter estimation and identifiability in a neural population model for electro-cortical activity," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-27, May.

    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:pcbi00:1008076. 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.