IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0187431.html
   My bibliography  Save this article

Information-theoretic analysis of the directional influence between cellular processes

Author

Listed:
  • Sourabh Lahiri
  • Philippe Nghe
  • Sander J Tans
  • Martin Luc Rosinberg
  • David Lacoste

Abstract

Inferring the directionality of interactions between cellular processes is a major challenge in systems biology. Time-lagged correlations allow to discriminate between alternative models, but they still rely on assumed underlying interactions. Here, we use the transfer entropy (TE), an information-theoretic quantity that quantifies the directional influence between fluctuating variables in a model-free way. We present a theoretical approach to compute the transfer entropy, even when the noise has an extrinsic component or in the presence of feedback. We re-analyze the experimental data from Kiviet et al. (2014) where fluctuations in gene expression of metabolic enzymes and growth rate have been measured in single cells of E. coli. We confirm the formerly detected modes between growth and gene expression, while prescribing more stringent conditions on the structure of noise sources. We furthermore point out practical requirements in terms of length of time series and sampling time which must be satisfied in order to infer optimally transfer entropy from times series of fluctuations.

Suggested Citation

  • Sourabh Lahiri & Philippe Nghe & Sander J Tans & Martin Luc Rosinberg & David Lacoste, 2017. "Information-theoretic analysis of the directional influence between cellular processes," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-26, November.
    • Handle: RePEc:plo:pone00:0187431
    DOI: 10.1371/journal.pone.0187431
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0187431
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0187431&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0187431?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. Daniel J. Kiviet & Philippe Nghe & Noreen Walker & Sarah Boulineau & Vanda Sunderlikova & Sander J. Tans, 2014. "Stochasticity of metabolism and growth at the single-cell level," Nature, Nature, vol. 514(7522), pages 376-379, October.
    2. Robert J Prill & Robert Vogel & Guillermo A Cecchi & Grégoire Altan-Bonnet & Gustavo Stolovitzky, 2015. "Noise-Driven Causal Inference in Biomolecular Networks," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-16, June.
    3. Michael Wibral & Nicolae Pampu & Viola Priesemann & Felix Siebenhühner & Hannes Seiwert & Michael Lindner & Joseph T Lizier & Raul Vicente, 2013. "Measuring Information-Transfer Delays," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-19, February.
    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. Tam, H.C. & Ching, Emily S.C. & Lai, Pik-Yin, 2018. "Reconstructing networks from dynamics with correlated noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 106-122.
    2. David L Gibbs & Ilya Shmulevich, 2017. "Solving the influence maximization problem reveals regulatory organization of the yeast cell cycle," PLOS Computational Biology, Public Library of Science, vol. 13(6), pages 1-19, June.
    3. Coulibaly, Saliya & Bessin, Florent & Clerc, Marcel G. & Mussot, Arnaud, 2022. "Precursors-driven machine learning prediction of chaotic extreme pulses in Kerr resonators," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    4. Vera Bettenworth & Simon Vliet & Bartosz Turkowyd & Annika Bamberger & Heiko Wendt & Matthew McIntosh & Wieland Steinchen & Ulrike Endesfelder & Anke Becker, 2022. "Frequency modulation of a bacterial quorum sensing response," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    5. Jessica A Lee & Siavash Riazi & Shahla Nemati & Jannell V Bazurto & Andreas E Vasdekis & Benjamin J Ridenhour & Christopher H Remien & Christopher J Marx, 2019. "Microbial phenotypic heterogeneity in response to a metabolic toxin: Continuous, dynamically shifting distribution of formaldehyde tolerance in Methylobacterium extorquens populations," PLOS Genetics, Public Library of Science, vol. 15(11), pages 1-38, November.
    6. Libor Pekař & Radek Matušů & Roman Prokop, 2017. "Gridding discretization-based multiple stability switching delay search algorithm: The movement of a human being on a controlled swaying bow," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-23, June.
    7. Xiaogeng Wan & Lanxi Xu, 2018. "A study for multiscale information transfer measures based on conditional mutual information," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-30, December.
    8. Shinya Ito & Fang-Chin Yeh & Emma Hiolski & Przemyslaw Rydygier & Deborah E Gunning & Pawel Hottowy & Nicholas Timme & Alan M Litke & John M Beggs, 2014. "Large-Scale, High-Resolution Multielectrode-Array Recording Depicts Functional Network Differences of Cortical and Hippocampal Cultures," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-16, August.
    9. Nicholas Timme & Shinya Ito & Maxym Myroshnychenko & Fang-Chin Yeh & Emma Hiolski & Pawel Hottowy & John M Beggs, 2014. "Multiplex Networks of Cortical and Hippocampal Neurons Revealed at Different Timescales," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-43, December.
    10. Leonidas Sandoval Junior & Asher Mullokandov & Dror Y. Kenett, 2015. "Dependency Relations among International Stock Market Indices," JRFM, MDPI, vol. 8(2), pages 1-39, May.
    11. Daniel Toker & Friedrich T Sommer, 2019. "Information integration in large brain networks," PLOS Computational Biology, Public Library of Science, vol. 15(2), pages 1-26, February.
    12. Shuangyu Bi & Manika Kargeti & Remy Colin & Niklas Farke & Hannes Link & Victor Sourjik, 2023. "Dynamic fluctuations in a bacterial metabolic network," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

    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:pone00:0187431. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.