Efficiency, accuracy and robustness of probability generating function based parameter inference method for...

Area of Science:

• Systems Biology
• Computational Biology
• Biophysics

Background:

• Biochemical reactions are stochastic, often modeled by chemical master equations (CMEs).
• Parameter inference from CMEs is computationally intensive due to the discrete nature of molecular states.
• Existing methods face challenges in efficiency and accuracy for complex biological systems.

Purpose of the Study:

• To introduce and evaluate a novel, computationally efficient inference method for CMEs.
• To leverage analytical solutions in probability generating function (PGF) space for parameter estimation.
• To extend the PGF framework for model selection in stochastic systems.

Main Methods:

• Developed a PGF-based inference method for CME parameter estimation.
• Evaluated method performance using numerical experiments on steady-state and time-resolved count data.
• Assessed robustness against data contamination and applied to model selection.

Main Results:

• The PGF-based method significantly outperforms existing approaches in computational efficiency and inference accuracy.
• The method demonstrates robustness even with contaminated data.
• Successfully extended the PGF framework to enable computationally feasible model selection.

Conclusions:

• The PGF-based inference method offers a superior alternative for analyzing stochastic biochemical kinetics.
• This approach enhances the ability to accurately model complex biological systems, such as gene expression.
• Enables reliable identification of complex models, including those with multiple gene states, using time-resolved data.