Effect of feedback regulation on stochastic gene expression

Area of Science:

• Systems Biology
• Molecular Biology
• Biophysics

Background:

• Gene expression involves molecular species in small copy numbers, leading to stochastic noise.
• Understanding this noise is crucial for comprehending cellular processes and gene regulation.

Purpose of the Study:

• To investigate stochastic noise in a single gene network using Omega-expansion techniques.
• To provide a statistical interpretation for measuring gene expression noise and analyze its components.

Main Methods:

• Application of Omega-expansion techniques to a single gene network model.
• Analysis of steady-state statistics, including normalized variances and covariances.
• Decomposition of total noise into contributions from molecule number and molecular interactions.

Main Results:

• Identified an invariant relationship between normalized variances and covariances under linear noise approximation.
• Demonstrated that negative feedback reduces protein noise in gene networks.
• Showed that protein intrinsic noise decreases with increasing burst size, while extrinsic noise remains independent.

Conclusions:

• The normalized variance is a key metric for quantifying gene expression noise.
• Negative feedback and burst size are critical factors influencing protein noise levels.
• The study provides insights into the fundamental relationships between stochasticity, system size, and feedback in gene expression.