Information-theoretic analysis of the directional influence between cellular processes

TL;DR

This paper introduces a theoretical framework using transfer entropy to determine the directional influence between cellular processes, addressing noise and feedback complexities, and re-analyzes experimental data to validate the approach.

Contribution

It presents a novel theoretical method for computing transfer entropy in noisy biological systems, improving inference of cellular process interactions.

Findings

Confirmed directional influence between growth and gene expression in E. coli

Identified noise structure constraints for accurate transfer entropy estimation

Provided practical guidelines for time series data requirements

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.