Nascent RNA kinetics with complex promoter architecture: Analytic results and parameter inference

Area of Science:

• * Molecular biology
• * Systems biology
• * Biophysics

Background:

• * Transcription is a complex stochastic process, challenging to model using mature RNA levels.
• * Single-cell technologies offer precise measurements of nascent RNA, reflecting real-time transcription kinetics.
• * Promoter architecture significantly influences the stochastic nature of gene expression.

Purpose of the Study:

• * To develop a general stochastic model for nascent RNA kinetics with complex promoter architectures.
• * To provide insights into the role of molecular memory in transcription dynamics.
• * To create a statistical method for inferring promoter memory from nascent RNA distributions.

Main Methods:

• * Application of queuing theory techniques to derive exact distributions and moments of nascent RNA.
• * Development of a statistical inference method based on analytical results.
• * Analysis of synthetic data and the HIV-1 gene for method validation.

Main Results:

• * The model accurately mimics nascent RNA kinetics, considering complex promoter structures.
• * Queuing theory provides insights into how molecular memory affects stochastic RNA production.
• * The developed statistical method successfully infers promoter memory from RNA distributions.

Conclusions:

• * The study presents a robust model for understanding nascent RNA dynamics.
• * Molecular memory plays a crucial role in the stochastic kinetics of transcription.
• * The inference method offers a valuable tool for analyzing gene regulation and promoter function.