Calibrating a parameterized stochastic Boolean network model of gene regulation using a single steady-state gene...

Area of Science:

• Computational Biology
• Systems Biology
• Genomics

Background:

• Boolean networks (BNs) model complex biological dynamics.
• Gene regulatory network (GRN) models simulate perturbation effects.
• Tailored GRNs are crucial for drug discovery and personalized medicine.

Purpose of the Study:

• To develop a novel methodology for estimating parameters of parameterized stochastic BN models.
• To utilize single steady-state gene expression measurements for GRN parameterization.
• To address the computational challenges in GRN analysis.

Main Methods:

• A simulation-based approach was employed for parameter estimation.
• Simplifying assumptions reformulated the problem into linear equations.
• Ergodicity and unique solution existence were ensured.

Main Results:

• The study presents an efficient simulation-based method for parameter estimation.
• The approach was validated on random BNs and non-small cell lung cancer (NSCLC) cell lines.
• Personalized BNs were successfully established for NSCLC.

Conclusions:

• The developed methodology offers a practical solution for parameterizing stochastic BNs.
• This approach has significant implications for drug target discovery and personalized therapies.
• The method enhances the utility of GRN models in precision medicine.