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Pluralistic and stochastic gene regulation: examples, models and consistent theory.

Elisa N Salas1, Jiang Shu2, Matyas F Cserhati2

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Gene regulation is complex, involving many factors acting together stochastically. This study reveals widespread pluralistic regulation, where multiple regulators influence gene pairs, challenging the idea of single master regulators.

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Area of Science:

  • Molecular Biology
  • Systems Biology
  • Genomics

Background:

  • Gene regulation is often modeled simplistically, not accounting for observed biological variability.
  • Evidence suggests transcription factor (TF) levels, binding, and activity fluctuate, and gene expression occurs in bursts.
  • Evolutionary analysis shows frequent loss/gain of regulators and rewiring of regulatory sites, indicating dynamic processes.

Purpose of the Study:

  • To develop a theory of pluralistic and stochastic gene regulation.
  • To integrate empirical data with mathematical modeling for a comprehensive understanding.
  • To investigate the regulatory mechanisms of co-expressed gene pairs, including ribosomal proteins.

Main Methods:

  • Meta-analysis of ENCODE ChIP-Seq experiments to study TF binding.
  • Analysis of approximately 800,000 co-expressed human gene pairs.
  • Machine learning models to predict transcript levels based on TF binding sites.

Main Results:

  • Widespread pluralistic regulation observed in ≈800,000 co-expressed gene pairs.
  • Approximately 50% of observed regulators bind reproducibly to both genes in a pair, twice as often as in independently expressed pairs.
  • No single master regulator identified; machine learning accurately predicted transcript levels despite TF binding fluctuations.

Conclusions:

  • Gene regulation is inherently pluralistic and stochastic, characterized by partially random binding, redundancy, and burst-like expression.
  • The theory explains degeneracy of binding motifs and massive regulatory rewiring during evolution.
  • Findings challenge traditional views and provide a more realistic framework for gene regulation research.