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Creating a Structurally Realistic Finite Element Geometric Model of a Cardiomyocyte to Study the Role of Cellular Architecture in Cardiomyocyte Systems Biology

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Improved Accuracy of Cardiac Tissue-Level Simulations by Considering Membrane Resistance as a Cellular-Level

E Pouranbarani, L A Berg, R S Oliveira

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|October 6, 2020

Summary

This summary is machine-generated.

Incorporating membrane resistance (Rm) into cardiac cell models significantly enhances tissue simulation accuracy. This optimization improves predictions of wave propagation, crucial for understanding cardiac electrophysiology.

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Area of Science:

Computational biology
Cardiac electrophysiology
Biophysics

Background:

Cardiac cellular models are fundamental for tissue simulation but often lack accuracy due to incomplete parameterization.
Previous work introduced a framework incorporating membrane resistance (Rm) for cellular model fitting, accounting for inter-cellular electrotonic effects.

Purpose of the Study:

To investigate if including membrane resistance (Rm) as an optimization objective enhances the accuracy of cardiac tissue-level modeling.
To evaluate the impact of Rm optimization on predicting wave propagation dynamics in cardiac tissue.

Main Methods:

Performed cellular-level optimization of a cardiac cell model.
Investigated source-sink mismatch configurations in a 2D cardiac tissue model.
Assessed the improvement in accuracy by including Rm in the optimization protocol.

Main Results:

Including membrane resistance (Rm) in the optimization process substantially improved the accuracy of the critical transition border.
The critical transition border, representing wave propagation limits, showed reduced relative error when Rm was considered.
This indicates enhanced precision in modeling wave propagation dynamics.

Conclusions:

Considering membrane resistance (Rm) as an additional optimization objective is a viable strategy to improve cardiac tissue-level model accuracy.
Model developers can leverage this approach during parameter tuning for more precise simulations of cardiac electrophysiology.
This method offers a pathway to more reliable computational models for cardiac research.