Gillespie

Gillespie simulates the stochastic kinetics of nascent and mature RNA using the Gillespie algorithm to model transcriptional dynamics including synthesis, stepwise elongation, release into the cytoplasm, and degradation.

Key Features:

• Two-State Gene Regulation: Models gene activation and inactivation as a two-state system capturing stochastic switching between active and inactive promoter states.
• RNA Synthesis and Degradation: Simulates RNA synthesis initiation, stepwise elongation, release into the cytoplasm, and stepwise degradation at single-molecule resolution.
• Simulation of Fluorescent Signals: Predicts fluorescent probe signals that are measurable by single-cell RNA imaging techniques for direct comparison with experimental data.
• Parameter Estimation: Addresses the inverse problem in a five-dimensional parameter space using optimization heuristics to recover reaction rates from simulated gene expression turn-on data.

Scientific Applications:

• Experimental Validation: Enables comparison of simulated fluorescent signals with single-cell RNA imaging data to validate models of transcriptional kinetics.
• Parameter Optimization: Facilitates estimation and tuning of reaction rates and kinetic parameters critical for understanding gene regulation mechanisms.
• Educational Use: Illustrates principles of transcriptional kinetics and stochastic gene expression dynamics for teaching and demonstration purposes.

Methodology:

Stochastic simulation of RNA kinetics using the Gillespie algorithm to model discrete random events in transcription and RNA dynamics.