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Single-cell stochastic gene expression kinetics with coupled positive-plus-negative feedback.

Chen Jia1,2, Le Yi Wang3, George G Yin2

  • 1Division of Applied and Computational Mathematics, Beijing Computational Science Research Center, Beijing 100193, China.

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Summary
This summary is machine-generated.

This study reveals how combined positive and negative feedback in gene circuits amplifies gene expression while reducing noise. This synergistic interaction stabilizes expression levels, offering insights into gene regulatory network dynamics.

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Area of Science:

  • Systems Biology
  • Molecular Biology
  • Biophysics

Background:

  • Gene expression is inherently noisy due to stochastic processes.
  • Feedback loops are crucial for regulating gene expression dynamics.
  • Understanding coupled feedback mechanisms is key to deciphering cellular behavior.

Purpose of the Study:

  • Investigate single-cell gene expression kinetics in a minimal coupled gene circuit with positive-plus-negative feedback.
  • Analyze the synergistic interactions between positive and negative feedback loops.
  • Derive and generalize analytic distributions for protein abundance and noise decomposition.

Main Methods:

  • Stochastic bifurcation analysis of a minimal coupled gene circuit.
  • Derivation of analytic steady-state and time-dependent protein distributions.
  • Analysis of macroscopic limits (Kurtz and Lévy) of the chemical master equation.
  • Decomposition of gene expression noise into biophysical origins.

Main Results:

  • Observed a triphasic stochastic bifurcation with increasing feedback ratio, indicating strong synergy.
  • Coupled feedback amplifies expression mean and reduces noise, stabilizing expression at high levels.
  • Derived generalized analytic distributions for discrete models and macroscopic limits.
  • Provided a complete decomposition of gene expression noise.

Conclusions:

  • Positive-plus-negative feedback offers a robust mechanism for stabilizing gene expression.
  • The study provides a comprehensive analytical framework for understanding gene expression noise.
  • Findings advance the understanding of regulatory network design principles in biological systems.