Modeling an Excitable Biosynthetic Tissue with Inherent Variability for Paired Computational-Experimental Studies

Area of Science:

• Biophysics
• Computational Biology
• Cardiovascular Research

Background:

• Understanding cellular electrical dynamics is key to studying arrhythmias.
• Current cardiomyocyte models use neonatal cells with poorly characterized properties.
• Biosynthetic excitable tissues from HEK293 cells offer a stable alternative.

Purpose of the Study:

• To develop and validate a computational model for excitable HEK293 (Ex293) cells.
• To investigate sources of variation influencing cellular electrical behavior and tissue conduction.
• To create a framework for paired computational-experimental studies of excitable tissues.

Main Methods:

• Developed a computational model for Ex293 cells using electrophysiological data.
• Employed a genetic search algorithm for model parameterization.
• Incorporated random cell-to-cell and inter-monolayer variations in conductances and conductivity.

Main Results:

• The model accurately reproduces experimental observations, including action potential shape and macroscopic conduction variability.
• Random variations in ionic conductances and tissue conductivity were necessary to match experimental data.
• The spatial organization of cell-to-cell conductance variation did not significantly impact macroscopic behavior.

Conclusions:

• The developed computational Ex293 cell and tissue model provides a novel framework for studying excitable tissue dynamics.
• This modeling approach accurately predicts normal and drug-modified conduction.
• The methodology for modeling variation is applicable to other excitable cell types.