- Author:
- nima <nafs080@aucklanduni.ac.nz>
- Date:
- 2021-10-20 17:28:58+13:00
- Desc:
- Updated documentation
- Permanent Source URI:
- https://models.cellml.org/workspace/702/rawfile/8d9afa926cbad3701cc08bffd0467667ae3a9fce/Overview.rst
About this model
*****************
:Original publication: `Kernik et al. (2019)`_:
"A computational model of induced pluripotent stem-cell derived cardiomyocytes \
incorporating experimental variability from multiple data sources" J Physiol. 2019 Sep 1; 597(17): 4533-4564.
:DOI: https://dx.doi.org/10.1113%2FJP277724
Model status
**************
The current CellML model implementation runs OpenCOR_.
The simulation setting saved in SED-ML file. \
SED-ML file then loaded into Python script in order for the code to go through \
the loop and capture the results for different initial conditions and inputs.
CellML file in the navigation panel along with SED-ML file and python script can reproduce \
one of the figure in the primary published paper.
Model overview
*****************
This workspace holds a CellML_ and Python encoding of the `Kernik et al. (2019)`_
model. The original model developed a whole cell model of
induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs)
composed of simple model components comprising ion channel models with single exponential
voltage-dependent gating variable rate constants. The model were parameterized to fit experimental
iPSC-CM data from multiple laboratories for all major ionic currents. The resulting population
of cellular models predicts robust inter-subject variability in iPSC-CMs.
This approach links molecular mechanisms to known cellular-level iPSC-CM phenotypes,
as shown by comparing immature and mature subpopulations of models to analyse the contributing
factors underlying each phenotype.
.. figure:: schematic-diagram.jpg
:width: 85%
:align: center
:alt: Schematics of the model
A diagrammatic representation of the Kernik et al. (2019) model.
.. _CellML: https://www.cellml.org/
.. _OpenCOR: https://opencor.ws/
.. _GitHub: https://github.com/ClancyLabUCD/IPSC-model/
Modular description
********************
Components
***********
CellML can build a model in a modular way which divides the model
into distinct modules, which can be re-used.
The main CellML files:
- `Components <Components>`_ which include:
- Main file that put all the other required files together: `Channels <Components/Channels.cellml>`_
this file is the main CellML file which is the top model in the hierarchical modular
presentation and rest of the files need to be imported here in order to run the simulation.
- This file has the formulation for the current calculation (:math:`I_{K1}`): `Current <Components/Current>`_
- This file contains the formulation for Nernst potential: `Nernst_potential <Components/Nernst_potential.cellml>`_
- Different protocol to choose a value for intracellular potassium: `Protocol <Components/Protocol.cellml>`_
- Probability of channels gates being open or close: `act_inact <Channels/act_inact.cellml>`_
this file is the main CellML file for calculation the probability of channels gates being
open or close and also the activation/inactivation time constants.
some other files need to be imported here in order to run the simulation.
- Hodgkin-Huxley-type gating formulations are provided in a single module here: `gatting <Channels/gatting.cellml>`_
- General file for required parameters: `parameter <Channels/parameter.cellml>`_
This file is a general file, specific parameters for each channel are
presented in associated python script.
- All the required units for this simulation: `unit <Channels/unit.cellml>`_
Experiments
**************
Here the model run the simulation for each channel in the primary paper in order to reproduce the figures.
In each section in the navigation panel, simulation calculates
the probability of that channel being open or close. Each figure includes one python script
which can load the SED-ML file and provide the
simulation results. In each figure parameters for voltage-dependent activation and inactivation gates
were optimized to iPSC-CM experimental data from various laboratories.
This workspace has three sets of experiments and corresponding simulation results, we just provided
the simulation results here in order to check the reproducibility of figures in the primary paper:
1. :math:`I_Na` : `Sodium current model optimization <https://models.physiomeproject.org/e/769/Fig3/fig3-new.py/view>`_
2. :math:`I_CaL` : `Calcium current model optimization <https://models.physiomeproject.org/e/769/Fig4/fig4-new.py/view>`_
3. :math:`I_Kr` : `Rapid delayed rectifier potassium current model optimization <https://models.physiomeproject.org/e/769/Fig5/fig5-new.py/view>`_
4. :math:`I_to` : `Transient outward potassium current model optimization <https://models.physiomeproject.org/e/769/Fig6/fig6-new.py/view>`_
5. :math:`I_Ks` : `Slow delayed rectifier potassium current model optimization <https://models.physiomeproject.org/e/769/Fig7/fig7-new.py/view>`_
6. :math:`I_f` : `Pacemaker/funny current model optimization <https://models.physiomeproject.org/e/769/Fig8/fig8-new.py/view>`_
Simulation settings
*********************
Simulation settings (solver, duration of the simulation, etc) are stored in SED-ML files.
The Python scripts contains the required parameters and conditions for each channel
to run simulation and then plot the results with Matplotlib library to reproduce the figures
in the original paper. The name of each scripts presents the Figure number in the primary paper.
For example, fig3-new.py is used to generate the simulation and reproduces the graph shown in
Figure 3 in the original study.
In order to reproduce Figure 3, once all the files are downloaded to the same folder,
execute the following script from the command line (command prompt):
cd [PathToThisFile]
[PathToOpenCOR]/pythonshell Figure3A-new.py
Model History
*******************
The original model of **induced pluripotent stem-cell derived cardiomyocytes incorporating
experimental variability from multiple data sources** was built in MATLAB which can be
downloaded from GitHub_.
.. _`Kernik et al. (2019)`: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6767694/
