At the heart of computational modelling.

Area of Science:

• Computational physiology
• Biophysical modeling
• Cardiac electro-mechanics

Background:

• Computational simulation holds significant potential for physiological insight.
• A robust link between experimental data and biophysical models is essential for realizing this potential.
• The process of parameterizing models from data lacks sufficient scrutiny and analysis.

Purpose of the Study:

• To analyze the critical process of model parameterization from experimental data.
• To highlight specific challenges in model parameterization using cardiac electro-mechanics models.
• To propose a structured, three-stage framework for model parameterization.

Main Methods:

• Utilized detailed biophysically based mathematical models of cardiac electro-mechanics as a case study.
• Examined the stages involved in parameterizing models from experimental data.
• Proposed a framework separating parameterization into observation, fitting, and validation.

Main Results:

• Identified a lack of scrutiny in the parameterization process across physiological modeling.
• Demonstrated specific issues in parameterizing complex cardiac models.
• Proposed a clear, three-stage (observation, fitting, validation) approach to model parameterization.

Conclusions:

• A structured approach to model parameterization is necessary for reliable computational simulations.
• The proposed observation, fitting, and validation framework can improve the rigor of physiological models.
• Further research is needed to address challenges in model parameterization and validation.