Quantifying heterogeneity of stochastic gene expression.

Area of Science:

• Quantitative Biology
• Systems Biology
• Biophysics

Background:

• Stochastic gene expression, characterized by temporal fluctuations and cell-to-cell variability in gene products, is a key area of interest.
• Current models often lack analytical formulation and robust parameter estimation methods for real-world biological data.
• Bridging the gap between theoretical models and experimental observations in gene expression dynamics remains a challenge.

Purpose of the Study:

• To develop a theoretical framework for analyzing gene regulatory systems.
• To present a basic gene regulatory model and derive its steady-state solution.
• To introduce a Bayesian approach for parameter estimation using single-cell experimental data.

Main Methods:

• Development of a theoretical framework for gene regulatory systems.
• Derivation of a steady-state solution for the proposed model.
• Application of a Bayesian inference method for parameter estimation from single-cell data.

Main Results:

• A robust theoretical framework for modeling stochastic gene expression was established.
• The Bayesian approach effectively estimates model parameters from single-cell mRNA and protein level data.
• The framework demonstrated applicability across various experimental scales and cell types.

Conclusions:

• The proposed framework provides a powerful tool for understanding gene expression heterogeneity.
• The Bayesian method facilitates accurate parameter estimation, overcoming limitations of previous approaches.
• This work enables comparative analysis of kinetic parameters across different biological contexts, advancing systems and quantitative biology.