Correlation between external regulators governs the mean-noise relationship in stochastic gene expression

Area of Science:

• Molecular Biology
• Systems Biology
• Genetics

Background:

• Gene transcription in single cells is a probabilistic process, leading to inherent noise.
• The relationship between expression variance and mean is crucial for understanding gene expression noise.
• This relationship is often modeled using a power law, with a key parameter β.

Purpose of the Study:

• To investigate the factors controlling the mean-noise relationship in gene expression.
• To determine whether promoter structure or external regulation primarily governs this relationship.
• To elucidate the mechanistic basis of gene expression noise.

Main Methods:

• Mathematical modeling of gene transcription.
• Analysis of the relationship between variance (σ²) and mean expression (μ).
• Formulation and analysis of the equation log(σ²/μ²) = β log μ + log α.

Main Results:

• The sign of the parameter β is solely determined by external regulatory factors.
• External regulators being independent leads to a negative β, while correlated regulators result in a positive β.
• Promoter structure does not influence the sign of β.

Conclusions:

• External regulation, not promoter sequence, governs the mean-noise relationship in gene expression.
• The correlation of stochastic variables in external regulation is the key determinant of noise dependence on mean expression.
• This finding provides a unified mechanism to interpret diverse experimental observations of gene expression noise.