These examples are intended to transfer basic user know-how regarding most openCARP features in an efficient way. The scripts are designed as mini-experiments, which can also serve as basic building blocks for more complex experiments.
There is a number of examples dedicated to teaching openCARP fundamental know-how for those who are interested in building more complex experiments from scratch themselves or in extending pre-existing experiments. All executable examples are coded up in carputils to facilitate an easy execution of all experiments without significant additional effort and complex command line interactions.
Most examples can be run by simply copying the command from the corresponding example web page. It is recommended to inspect the generated command lines to understand what the simulation looks like in the plain command line by adding the option
--dry to the run script command line. You can download the examples from our repository.
The following examples illustrate how single cell modeling is performed using the tool
bench. Additionally you learn how to integrate a single cell model from CellML into our library
limpet using the math language EasyML
Basic single cell EP
This example introduces the basic steps of running EP simulations in an isolated myocytes
Action potenital duration (APD) restitution example in single cell. As pacing frequency is increased, APD shortens to maintain a one to one stimulus to responses
This example explains the basic usage of bench for performing voltage clamp experiments.
EasyML to C code
This tutorial explains the basics of using the code generation tool limpet_fe.py for generating ODE solver code for models of cellular dynamics.
Here you will get more information about the cell model math format EasyML
Here you learn how to import CellML data into openCARP and what problems might occur during this process
These examples should inform you about the most basic steps in developing simple tissue simulations using openCARP
Basic tissue EP
This example introduces to the basics of using the openCARP executable for simulating EP at the tissue and organ scale
In this example you learn how to stimulate a tissue from the extracellular space
Tuning Conduction Velocity
This tutorial introduces the background for the relationship between conduction velocity and tissue conductivity
Init tissue from cell
This tutorial demonstrates how to initialize a cardiac tissue with state variables obtained from a single-cell stimulation
This tutorial demonstrates how to adjust parameters in tissue simulations to match experimental data for conduction velocity, APD, and wavelength
This example demonstrates how to compute conduction velocity restitution in cardiac tissue
This example demonstrates how to adjust ionic model parameters to generate a specific action potential duration in your simulations
Regions are used to manage the assignment of heterogeneous tissue properties. This tutorial explains the different approches of how regions can be defined.
Region vs. gradient heterogeneities
This tutorial introduces the concepts of region-based and gradient-based heterogeneities for assigning spatially varying properties
Regions & conductivities
This tutorial details how to assign different conductivities to different parts of a simulated tissue slice using region-wise tagging
This example details how to assign different single cell dynamics to different parts of a simulated tissue slice using region-wise tagging
This example details how to output the values of state variables over time during a simulation
This example details how to assign a gradient of single cell properties using the adjustments interface
Periodic boundary conditions
Periodic boundary conditions connect the left edge of a sheet to the right, or the top to the bottom
Extracellular potentials and ECGs
This tutorial explains the background of computing extracellular potentials and ECG using different techniques
Local activation time
This example demonstrates how to compute local activation times (LATs) and action potential durations (APDs) of cells in a cardiac tissue
Computing Laplace-Dirichlet maps provide an elegant tool for describing the distance between defined boundaries
Unequal anisotropy ratios can be responsible for the formation of unexpectedly complex polarization patterns
This example shows how to use polling files to sweep parameters
Here you will learn how to use the visualization tools LimpetGUI for single cell results and Meshalizer for tissue results