iPSC-CM mathematical model calibration

This repository contains code to run simulations, model calibrations, and reproduce the figures in "Creating cell-specific computational models of stem cell-derived cardiomyocytes using optical experiments" by Yang J et al. This work was performed in the Cardiac Systems Pharmacology Lab of Dr. Eric A. Sobie in the Department of Pharmacological Sciences at the Icahn School of Medicine Mount Sinai.

• Yang J, Daily NJ, Pullinger TK, Wakatsuki T, and Sobie EA. “Creating Cell-Specific Computational Models of Stem Cell-Derived Cardiomyocytes Using Optical Experiments.” PLOS Computational Biology 20, no. 9 (September 11, 2024): e1011806. https://doi.org/10.1371/journal.pcbi.1011806.

Code requirements

MATLAB version 2019 or higher; version 2019a was used to create the Kernik model populations, and 2020b-2023b were used to perform analysis.

Models included within this repository:

• Kernik DC, Morotti S, Wu H, Garg P, Duff HJ, Kurokawa J, et al. A computational model of induced pluripotent stem‐cell derived cardiomyocytes incorporating experimental variability from multiple data sources. J Physiol. 2019;597: 4533–4564. doi:10.1113/JP277724
• O’Hara T, Virág L, Varró A, Rudy Y. Simulation of the Undiseased Human Cardiac Ventricular Action Potential: Model Formulation and Experimental Validation. PLoS Comput Biol. 2011;7: e1002061. doi:10.1371/journal.pcbi.1002061
• Paci M, Hyttinen J, Aalto-Setälä K, Severi S. Computational models of ventricular- and atrial-like human induced pluripotent stem cell derived cardiomyocytes. Ann Biomed Eng. 2013;41: 2334–2348. doi:10.1007/s10439-013-0833-3
• Paci M, Pölönen R-P, Cori D, Penttinen K, Aalto-Setälä K, Severi S, et al. Automatic Optimization of an in Silico Model of Human iPSC Derived Cardiomyocytes Recapitulating Calcium Handling Abnormalities. Front Physiol. 2018;9: 709. doi:10.3389/fphys.2018.00709
• Tomek J, Bueno-Orovio A, Passini E, Zhou X, Minchole A, Britton O, et al. Development, calibration, and validation of a novel human ventricular myocyte model in health, disease, and drug block. eLife. 8: e48890. doi:10.7554/eLife.48890

Repository structure

Primary folders and scripts

• GA: contains main scripts to run genetic algorithm for model parameter calibration
• Analysis: contains scripts and functions to run post-calibration analyses
• Pseudodataset: contains scripts used to create the in silico Kernik model dataset used for protocol optimization
• Classes_all: code to construct classes using the corresponding cardiomyocyte model
• dydts_all: ODEs used to simulate electrophysiology dynamics based on each cardiomyocyte model
• Other_helper_functions: functions for setting up protocol simulations, reading in experimental or simulated data for the genetic algorithm, extracting and aligning action potential (AP) and calcium transient (CaT) traces, plotting calibrated parameter values, running independent validation simulations, etc.
• Setup_and_parameters: functions for setting up parameter values for some model classes in Classes_all

Usage instructions

In MATLAB, open the project CMmodelcalibration.prj from the base folder. Generating a Kernik2019 model population and simulating APs & CaTs

• Note: to recreate the exact dataset used in the paper, skip steps 1-2 and start with step 3 using the conductance multipliers listed in Supplementary Table S2.

1. Use create_Kernik_cells function in Other_helper_functions to initialize a population of in silico iPSC-CMs. Save cells to Pseudodataset/saved_data/gaKernik_population_unfiltered.mat.
   • The non-default population parameters used in the paper were: N = 100, sigma = 0.2
2. Run population_filtering.m to filter the in silico population by spontaneous beating rate and randomly select a subset of model cells from the filtered population.
3. Run ga_pseudoDataset_protocols.m to simulate AP and CaT traces for the chosen cells under the experimental conditions listed in the paper.
4. Run ga_subset_processing.m to process and save the in silico dataset to Pseudodataset/saved_data

Running a single calibration to one dataset

1. Before starting, make sure your AP and CaT datasets are in either of these folders:
   • Pseudodataset/saved_data for in silico data
   • ExperimentalData for in vitro data
2. In the main script, GA/sga_baseline_k19.m: adjust the experiment information at the beginning of the script as needed, then run the entire script. Resulting calibrated parameter values and figures will be stored in GA/Results.
3. For scripts and instructions to run multiple calibration(s) on a job scheduler, please contact janice.yang@icahn.mssm.edu

Analysis and evaluation of calibrated models

• Analysis/ga_fit_stats.m: function to calculate calibration errors, calibration spreads, and other metrics from multiple GA runs
• Analysis/aggregate_stats.m: script to compile calibration error and spread metrics for multiple cells
• plotFinalParams.m: distribution plots of calibrated parameters from multiple GA runs
• validation_threshold.m: calculating predicted IKr block tolerance thresholds of calibrated models from GA runs
• Parameter sensitivity analysis scripts: to be added to repository