Finite state projection based bounds to compare chemical master equation models using single-cell data

Area of Science:

• Systems Biology
• Computational Biology
• Biophysics

Background:

• Single-cell analysis techniques enable precise quantification of molecular distributions.
• Rapid advancements necessitate improved and efficient computational modeling tools.
• Stochastic models, particularly the chemical master equation, are crucial for understanding cellular processes.

Purpose of the Study:

• To derive novel bounds for assessing likelihoods in discrete stochastic models of single-cell responses.
• To develop computationally efficient methods for discriminating between biological models.
• To enhance parameter inference and model identification in single-cell systems biology.

Main Methods:

• Derivation of strict upper and lower bounds on likelihoods using a finite state projection approach.
• Monotonic convergence of bounds to the exact likelihood value.
• Integration of bounds into stochastic model identification and parameter inference algorithms.

Main Results:

• The derived bounds provide a rigorous method for model discrimination with minimal computational cost.
• The approach demonstrates improved accuracy and efficiency in analyzing and predicting single-cell behavior.
• Successful application to simulated data and experimental measurements of yeast transcriptional responses.

Conclusions:

• The developed bounds offer a powerful tool for advancing the analysis of single-cell, single-molecule data.
• This approach facilitates more accurate and efficient stochastic modeling in systems biology.
• The methods are broadly applicable to various biological systems exhibiting stochastic behavior.