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Multiplexing information flow through dynamic signalling systems

Author

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  • 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
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    References listed on IDEAS

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    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.
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