Hierarchical optimization for the efficient parametrization of ODE models.

Area of Science:

• Systems Biology
• Computational Biology
• Mathematical Modeling

Background:

• Mathematical models are crucial for analyzing cellular process dynamics.
• Parameter estimation from experimental data often involves relative changes, introducing scaling and noise parameters.
• These nuisance parameters increase problem dimensionality and can cause convergence issues.

Purpose of the Study:

• To develop a hierarchical optimization approach for parameter estimation in ordinary differential equation (ODE) models using relative data.
• To address challenges posed by scaling and noise parameters in model calibration.
• To improve the accuracy, robustness, and computational efficiency of parameter estimation.

Main Methods:

• A hierarchical optimization strategy is proposed, restructuring the problem into lower-dimensional inner and outer subproblems.
• The inner subproblem can be solved analytically, simplifying the overall estimation process.
• The approach was evaluated on three signaling pathway models.

Main Results:

• The hierarchical optimization approach demonstrated improved convergence compared to standard methods.
• The proposed method required significantly lower computation time.
• Accuracy and robustness were validated across the studied signaling pathways.

Conclusions:

• The hierarchical optimization approach offers a powerful and widely applicable alternative for parameter estimation from relative data.
• It effectively handles nuisance parameters, leading to more efficient and reliable model calibration.
• This method enhances the analysis of cellular dynamics using mathematical models.