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Related Experiment Video

Updated: Feb 19, 2026

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Information-theoretic analysis of the directional influence between cellular processes.

Sourabh Lahiri1, Philippe Nghe2, Sander J Tans3

  • 1Gulliver laboratory, PSL Research University, ESPCI, 10 rue de Vauquelin, 75231 Paris Cedex 05, France.

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Summary

This study introduces transfer entropy (TE) to determine the direction of cellular process interactions without needing prior models. The method confirms gene expression and growth rate links in E. coli, offering guidelines for accurate data analysis.

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Area of Science:

  • Systems Biology
  • Information Theory
  • Computational Biology

Background:

  • Inferring directional interactions between cellular processes is a key challenge in systems biology.
  • Existing methods like time-lagged correlations rely on assumed interaction models.
  • Transfer entropy (TE) offers a model-free approach to quantify directional influence between variables.

Purpose of the Study:

  • To present a theoretical framework for computing transfer entropy (TE) in biological systems.
  • To apply TE to analyze gene expression and growth rate dynamics in E. coli.
  • To establish practical guidelines for optimal TE inference from time-series data.

Main Methods:

  • Developed a theoretical approach to compute transfer entropy, accounting for extrinsic noise and feedback loops.
  • Re-analyzed experimental data on E. coli gene expression and growth rate fluctuations.
  • Applied TE to confirm previously identified modes of interaction under stricter noise conditions.

Main Results:

  • Confirmed previously detected modes between growth and gene expression in E. coli.
  • Demonstrated the utility of TE for model-free inference of interaction directionality.
  • Identified critical requirements for time-series length and sampling rate for accurate TE estimation.

Conclusions:

  • Transfer entropy is a powerful tool for elucidating directional causality in biological systems.
  • The study provides a robust method for analyzing complex cellular dynamics.
  • Practical recommendations are offered to improve the reliability of systems biology analyses using TE.