Queuing Models of Gene Expression: Analytical Distributions and Beyond

Area of Science:

• Biophysics
• Molecular Biology
• Systems Biology

Background:

• Gene activation is a complex, multistep biochemical process.
• Classical models assume memoryless (Markovian) processes, which do not capture experimental observations of promoter inactivity.
• Recent data show gene promoters exhibit strong memory effects during inactive phases.

Purpose of the Study:

• To model the complex gene activation process using a non-exponential waiting-time distribution.
• To analyze a queuing model of stochastic transcription incorporating molecular memory.
• To provide insights into how molecular memory affects mRNA expression and noise.

Main Methods:

• Utilized a non-exponential waiting-time distribution to model gene promoter activation.
• Developed and analyzed a queuing model for stochastic transcription.
• Derived an analytical expression for the stationary mRNA distribution.

Main Results:

• The derived mRNA distribution offers insights into the impact of molecular memory on gene expression.
• Found that reducing waiting-time noise can paradoxically increase mRNA noise, challenging prior conclusions.
• Successfully developed a method to infer waiting-time distributions from mRNA distributions.

Conclusions:

• The study highlights the importance of incorporating molecular memory into gene expression models.
• The findings suggest complex activation dynamics can lead to non-intuitive noise characteristics.
• The developed inference method provides a valuable tool for analyzing gene expression data.