A Novel Bi-Level Framework for Fitting the Parameters in Cardiac Cellular Models.

Area of Science:

• Computational Biology
• Biophysics
• Cardiovascular Research

Background:

• Cardiac models are essential for understanding heart function and disease.
• Accurate parameter tuning is critical for the reliability of cardiac models.
• Existing methods face challenges with nonlinearity and initial parameter dependency.

Purpose of the Study:

• To present a novel bilevel parameter tuning framework for cardiac models.
• To improve the accuracy and robustness of cardiac model parameter estimation.
• To reduce the variability and error in fitting model parameters to reference data.

Main Methods:

• A bilevel optimization framework combining Mixed Integer Genetic Algorithm (MIGA) and Linear Least Squares (LLS).
• Upper-level optimization uses MIGA to fit the action potential (AP) to reference data.
• Lower-level optimization employs LLS with a clamp concept for total current mismatch minimization.

Main Results:

• The MIGA-LLS framework significantly reduces Normalized Root-Mean-Squared Error (NRMSE) for AP and total current compared to GA-based methods.
• The proposed method shows considerably decreased variability in resulting parameter values.
• The bilevel approach effectively addresses initial parameter dependency and linearization sensitivity.

Conclusions:

• The novel MIGA-LLS bilevel framework offers a robust and accurate method for cardiac model parameter tuning.
• This technique enhances the reliability of cardiac models for studying human and animal heart mechanisms and disorders.
• The reduced parameter variability improves the consistency and reproducibility of cardiac modeling studies.