Information theoretic analysis of the directional influence between cellular processes

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 show that an information-theoretic quantity, the transfer entropy (TE), 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) [1], where fluctuations in gene expression of metabolic enzymes and growth rate have been measured in single cells of Escherichia coli. We confirm the formerly detected modes between growth and gene expression, while prescribing more stringent conditions on the structure of noise sources [2].

[1] Stochasticity of metabolism and growth at the single-cell level, D. J. Kiviet et al., Nature, 514, 376 (2014).
[2] Information theoretic analysis of the directional influence between cellular processes, S. Lahiri et al., https://arxiv.org/abs/1709.01746