Regime shifts driven by dynamic correlations in gene expression noise.

Area of Science:

• Systems Biology
• Molecular Biology
• Biophysics

Background:

• Gene expression exhibits inherent noise, leading to phenotypic variability through regime shifts between cellular states.
• While white noise effects are understood, the impact of colored noise (with correlation time) on gene expression dynamics remains largely unexplored.

Purpose of the Study:

• To investigate the influence of colored noise correlation time on regime shifts in a genetic autoactivation circuit.
• To elucidate how noise characteristics affect phenotypic states and bistability in gene expression.

Main Methods:

• Theoretical analysis using stochastic potential, stationary probability density function, and first-passage time based on the Fokker-Planck equation.
• Modeling colored noise using the Ornstein-Uhlenbeck process.
• Validation through direct numerical simulations of stochastic differential equations.

Main Results:

• Increased noise correlation time in degradation rates can shift gene expression to higher protein concentrations and enhance bistability.
• Increased noise correlation time in basal rates maintains a bimodal distribution of protein concentrations.
• Cross-correlated colored noises in basal and degradation rates can induce low-to-high protein concentration shifts but decrease bistability.

Conclusions:

• Noise correlation time is a critical factor influencing regime shifts and phenotypic variability in gene expression.
• Understanding these colored noise effects provides insights into the origins of harmful phenotypes and potential therapeutic interventions in diseases.