Constraining the complexity of promoter dynamics using fluctuations in gene expression

Area of Science:

• Molecular Biology
• Biophysics
• Systems Biology

Background:

• Gene expression is stochastic, with mRNA production often occurring in bursts.
• Simple two-state promoter models are common, but experimental data suggest more complex dynamics.

Purpose of the Study:

• To determine the minimal complexity required to model gene promoter dynamics.
• To establish methods for determining this complexity using experimental data.

Main Methods:

• Utilized renewal and queueing theory to analyze promoter switching dynamics.
• Derived analytical expressions connecting mRNA Fano factor to promoter waiting-time distributions.
• Studied models with general waiting-time distributions for inactive-to-active state switching.

Main Results:

• Established fundamental bounds on promoter dynamics complexity using mRNA fluctuation measurements.
• Derived expressions linking Fano factor to waiting-time distributions for promoter state switching.
• Provided bounds on the minimal number of promoter states required for modeling.

Conclusions:

• mRNA fluctuation measurements can bound the complexity of gene promoter dynamics.
• The minimal complexity of promoter dynamics can be determined from single-cell mRNA level data.
• This work offers a framework for understanding complex gene expression regulation.