Stochastic simulation and statistical inference platform for visualization and estimation of transcriptional kinetics

Area of Science:

• Molecular Biology
• Biophysics
• Computational Biology

Background:

• Single-molecule fluorescent imaging allows quantitative transcription measurements at the single gene level.
• Accurate understanding of transcriptional kinetics is hindered by complex biophysical models.
• Analytical solutions for detailed prokaryotic transcriptional kinetics are often unavailable.

Purpose of the Study:

• To develop a stochastic simulation and statistical inference platform for detailed transcriptional kinetics in prokaryotic systems.
• To model gene activation, mRNA synthesis, elongation, cytoplasmic release, and co-transcriptional degradation.
• To enable estimation of kinetic parameters from experimental data via inverse problem-solving.

Main Methods:

• Utilized the Gillespie algorithm for stochastic simulation of gene expression.
• Incorporated a two-state gene activation model with stepwise mRNA synthesis and degradation.
• Developed a genetic algorithm-based heuristic optimization for parameter estimation.

Main Results:

• The platform successfully simulates nascent and mature mRNA kinetics for single gene copies.
• Predicted fluorescent signals align with measurable outcomes from time-lapse single-cell mRNA imaging.
• The optimization algorithm accurately recovered transcriptional kinetics from both simulated and experimental data.

Conclusions:

• The developed platform provides a robust tool for analyzing detailed transcriptional kinetics in prokaryotes.
• It bridges the gap between quantitative imaging data and biophysical modeling of gene expression.
• The software package is publicly available for further research in gene regulation.