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Information transmission in gene regulatory networks is enhanced by bimodal inputs and cooperative binding. Gene expression bursting dynamics also boost information capacity in these systems.

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Area of Science:

  • Systems Biology
  • Information Theory
  • Molecular Biology

Background:

  • Gene regulatory networks (GRNs) control cellular functions through complex interactions.
  • Understanding information flow in GRNs is crucial for deciphering cellular decision-making.
  • Cooperative binding in cis-regulatory systems significantly impacts gene expression dynamics.

Purpose of the Study:

  • To quantify information transmission through a simple gene cascade using information theory.
  • To investigate the role of regulator binding cooperativity in information processing.
  • To analyze how input signal characteristics affect information transfer efficiency.

Main Methods:

  • Utilized information theory, specifically mutual information, to measure information transmission.
  • Modeled a gene cascade with an unregulated upstream gene and a cooperative genetic switch downstream.
  • Calculated exact probability distributions for gene expression states.

Main Results:

  • Mutual information was higher for bimodal input signals compared to unimodal signals.
  • A peak in mutual information was observed at a specific cooperativity intensity.
  • The position of the mutual information maximum was dependent on promoter kinetic rates.
  • Input signal bursting dynamics were found to enhance information transmission capacity.

Conclusions:

  • Cooperative binding and bimodal inputs are key factors in optimizing information transmission in gene cascades.
  • Gene expression bursting can improve the information processing capabilities of GRNs.
  • This study provides insights into the biophysical mechanisms underlying information processing in biological systems.