Facilitating arrhythmia simulation: the method of quantitative cellular automata modeling and parallel running

Area of Science:

• Computational biology
• Cardiac electrophysiology
• Biophysics

Background:

• Cardiac arrhythmias originate from abnormal electrical activity at ionic channel and cell levels, evolving spatio-temporally.
• Understanding these complex dynamics requires a whole-heart model linking cellular activity to organ-level phenomena.

Purpose of the Study:

• To develop a method for building large-scale electrophysiological models of the whole heart.
• To simulate the association between channel/cell-level activities and organ-level electrophysiological phenomena.
• To investigate arrhythmias by linking cellular activity to ECG waveforms.

Main Methods:

• Utilized extended cellular automata for quantitative computing in large-scale electrophysiological models.
• Constructed a whole-heart model using Visible Human Project data.
• Implemented parallelization on a shared memory cluster using OpenMP and MPI hybrid programming.
• Developed a simulation algorithm to link cellular activity with ECG.

Main Results:

• Demonstrated the ability to trace and capture electrical activities at channel, cell, and organ levels within the extended cellular automaton system.
• Provided examples of simulated ECG waveforms using a 2-D slice to validate the ECG simulation algorithm.
• Presented a performance evaluation of the 3-D whole-heart model on a four-node cluster.

Conclusions:

• Quantitative multicellular modeling with extended cellular automata is efficient for integrating experimental data into computational models.
• This method is applicable to investigating complex biological activities beyond traditional differential equations or discrete computation.
• Transparent cluster computing facilitates time-consuming simulations, enabling effective simulation of arrhythmias.