The Maastricht Myocyte Toolkit, myokit for short, is a python-based software package designed to simplify the use of numerical models in the analysis of cardiac myocytes. Specifically, models that can be formulated as an ODE.
NOTE: Myokit is under active development. The currently available version is an alpha version, which means the interface may still change from time to time. While myokit has been tested succesfully on a number of platforms, it is not guaranteed to run on anything.
Last update: February 19th 2014
So what does it do?
Myokit provides a model definition syntax that allows you to write models without implementation details. The python API then runs the simulations directly in low-level C and returns the results to python for post-processing.
To do this, myokit takes advantage of python's distutils module to generate and compile C code for your models on the fly. With modern systems, this is a matter of milliseconds. As a backend, myokit uses the stiff-ode solver CVODE from the Sundials library, which is ideally suited for single myocyte simulations. An OpenCL based backend is provided to run multi-cellular simulations on a CPU or GPU device.
Myokit can import models from CellML or SBML and export to C, matlab, python, CUDA and OpenCL. By writing your own export modules you can set up experiments or analyses and re-use them for different models. Conversely, the separation of model code from engine allows you to run multiple types of analysis without having to rework your model.
Finally, a model editing GUI is provided as well as a growing number of analysis tools.
An example using the API
Myokit experiments are stored in an
.mmt file containing
- A model definition, in myokit’s model definition syntax This contains all model equations, comments, units and the initial values of the state variables.
- A pacing protocol, in a syntax described here This can be either a periodic stimulus, for ordinary pacing, or a more complex protocol that mimicks path-clamp experiments.
- A plot script, in plain python In this part, you set up the simulation, run it and process the results.
In the example below, we’re going to ignore the plot script and use only the model and protocol:
import matplotlib.pyplot as pl import myokit # Load a model, protocol and plot script m,p,x = myokit.load('example.mmt') # Set 25% IK1 block m.set_value('block.IK1', 0.25) # Create a simulation s = myokit.Simulation(m, p) # Pre-pace for 99 beats cl = 1000 # This should correspond to the cycle length of the protocol! s.pre(99 * cl) # Run the simulation for a single beat d = s.run(1 * cl) # Plot the results pl.figure() pl.title('Membrane potential') pl.plot(d['engine.time'], d['membrane.V']) pl.show()