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Information-theoretic analysis of the directional influence between cellular processes

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  • 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
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    References listed on IDEAS

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    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.
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