Academic Beta Testers Sought for Cardiac Simulation VM

We’re developing a self-contained virtual machine image (using Sun’s VirtualBox platform) that will allow you to run an entire CardioSolv Simulation Manager, simulator, and model visualization tools (Meshalyzer) on your machine, just by loading it up in VirtualBox. We are looking for academic users interested in cardiac simulation that would like to test it out and are willing to give us feedback.

The image should be ready within the next two weeks. If you’d like to get on board for this trial of the most powerful, sophisticated, and thoroughly tested cardiac electrophysiology simulation package in the world, please fill out the registration form at the end of this post.

You must be

  • A student, postdoc, or faculty member at an educational institution, planning to use the software only for non-commercial purposes
  • Willing to install and use Sun’s VirtualBox software (which is free for personal and academic use)
  • Willing to give us feedback on your experience with the system

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Cardiac Simulation – Cellular (ionic) Models

This is the second post in a series of posts about the hows and whys of cardiac simulation, both electrophysiological and mechanical. The first and previous post was Conceptual Background.

In this post, I’ll catalog the ionic models and plug-ins currently available for use in the CARP simulator.

Is there a model you’d like to use with CARP that’s not on this list? Please let us know!

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Termination of Polymorphic Ventricular Tachycardia Using Trains of Low-Voltage Field Stimuli

Brock Tice, VP of Operations at CardioSolv, gave a talk on the titled topic at the University of Minnesota’s Department of Biomedical Engineering graduate seminar on October 19th, 2009. He covered some electrophysiology basics, then gave an overview of his last study as a graduate student (employing CardioSolv technology).

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Cardiac Simulation – Conceptual Background

This is the first in a series of posts about the hows and whys of cardiac simulation, both electrophysiological and mechanical.

The heart is an extremely complicated organ. It’s composed of many different cell types, including cardiomyocytes, conduction system cells such as those in Purkinje fibers, fibroblasts, neurons, and adipocytes. However, the cardiomyocytes make up most of the weight of the heart, and are well-connected to each other by gap junctions. As a result, cardiac tissue can be reasonably well modeled as a syncytium (even though it is not a true syncytium as skeletal muscle is). This is usually formalized using either the monodomain formulation (which only considers current within and between cells) or the bidomain formulation (which also models the current outside of the cells).

Activity within individual cells is modeled using systems of differential equations typically referred to as ‘ionic models’ or ‘Hodgkin-Huxley-type’ models. These systems model how a cell moves ions around to effect changes in its transmembrane potential. It is fortunate that these models have been well-developed from their humble beginnings in the squid giant axon. Very sophisticated models of everything from said axon up to human ventricular myocytes have been created, based on patch-clamp experiments.  Changes in transmembrane potential in one cell create a potential difference relative to neighboring cells, driving the flow of current from one cell to another. It is this effect that couples ionic models to the tissue model described above.

The next post in this series will delve into the different tissue and cell models offered by our CARP simulator.

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CellML- The pros.

My name is Rob Blake, and I’m on the team here at Cardiosolv. I work on both the GUI software you’ve seen in the previous posts and our modeling code.

One of the strengths of our cardiac modeling software is the variety of membrane kinetic models that our users can choose from while running a simulation. These “membrane kinetic models” or “ionic models” are ordinary differential equations that describe how ions flow through the cell membrane and within the cell itself.   In the literature you can find models describing the electrical behavior for nearly every different type of cell in the heart.  You can find them for ventricular, atrial, and purkinje cells in a variety of species and in a variety of disease states.

Implementing one of these models from scratch takes significant effort.  The newer models all build on previous work, which means tracing back through multiple paper citations to find the original equations.   These equations are themselves complex– looking quickly through some of our models, I see that most of our models are composed on average of 150-250 equations just to describe a nonlinear system of 15-30 differential variables.  Papers also aren’t the best vehicles for describing the exact equations used.  Prose in the methods section can be ambiguous, and there might be inconsistencies within the paper itself.

Even if you have all the equations laid out clearly before you, you can still make a mistake while transcribing it to a computer.  Amidst 200 equations, you have plenty of opportunities for typos and omissions.  A single missing minus sign can invalidate all your results.  If you’re lucky, a typo will just cause the model to blow up and produce obviously incorrect results.  If you’re unlucky, then a typo could disable an effect that only shows up in pathological circumstances and you’ll never realize your error. Furthermore, it’s very hard to get feedback as you are developing the model.  With the way these models are designed, they either work perfectly or they don’t work at all.  If one differential variable becomes corrupted, it will often affect every other differential variable within 1-2 small steps of the code.  This makes it very difficult to trace back to the cause of an error.

In recent years, implementing these ionic models has gotten easier thanks to CellML, developed by the Auckland Bioengineering Institute. CellML describes these membrane kinetic models unambiguously so you don’t have to trace back through the literature.  CellML group has hundreds of models from published papers available.  For each model, they provide a link to the academic reference, a list of all the equations of the model, and sample code that will help you get started using the model within a package such as CVODE.  From their homepage you can download programs that can read in the CellML file and do simple simulations.  They also mark up which CellML reproduce all traces from the papers in question.

CellML has come leaps in bounds in recent years.  The code generation and the validated traces have only emerged within the last 1-2 years, and with them the typos in CellML have decreased dramatically.  I used to set aside 2 weeks to implement a cellular model from scratch using the equations from CellML as a starting point.  Now I can convert a CellML model into working code in a matter of hours. The site is a boon to researchers.  It allows them to focus on the biology instead of programming.  It encourages collaboration and cooperation amongst scientists and makes their results more accessible for everyone.  I don’t have enough positive things to say about CellML.  I find it essential in my daily work.

Despite all the good things I have to say about CellML, I do have some minor gripes about its design from a end user’s standpoint.  I’ll cover these minor complaints in my next post, along with tips and tricks we use here at Cardiosolv to get around these problems.

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Using Cardiac Simulations to Compare Prototypes

Have you ever had an idea for changes to a device, or a new experimental protocol, and wanted to test it? Did you balk at doing an animal study, given the time and money required?

One excellent use of cardiac simulation is to test out new ideas and compare prototypes. It’s not usually necessary to set up a fully realistic simulation or use an animal model to find out if your intuition about an idea is right, or if making a certain change to a design has the desired effect. Using cardiac simulation with our web-based simulation manager, you can easily set up a test, start it running, and have some results when you get back from lunch (or even a coffee break, depending on the size of the simulation and your computational budget). Or you could start doing the paperwork for an animal study. Wouldn’t you rather the simulation scenario?

Of course, the initial creation of a model to match your prototype could be a bit difficult if you haven’t done it before. We can generate appropriate models and get everything set up for you.

It’s also important to recognize that simulation won’t replace an animal study for final vetting before working with human subjects; however, it can save you costly iterations with animals and leave the animal study for final validation.

If you’d like to discuss setting up simulations for your problem, please contact us at +1 (888) 525-2232 or

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CSM now running on POD

The CardioSolv Simulation Manager (see this and this), is now up and running on the POD cluster. With the CSM running on this system, you can simultaneously run many cardiac simulations and do post-processing analysis. It’s all handled on the back-end by the queuing system, and run on POD’s top-of-the-line 8-processor machines. These run simulations and analysis as fast as is currently possible.

Typically it would take a lot of learning, reading of manpages and trial and error, to get cardiac simulations up and running on such a powerful resource. We’ve made it easy for you to log in, create your simulation, go get coffee, and have results when you return. You could go set up some expensive and time-consuming animal trials and wet-lab experiments, or you could do a preliminary simulation study from your laptop. Wouldn’t you rather the latter?

When you’re ready to get started, just let us know. +1 (888) 525-2232 or

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Getting more information about experiments with simulations

Cardiac simulation can be useful on its own, but it’s especially powerful when paired with animal or tissue models. A simulation model can be set up that is analogous to the experimental model, validated against the experimental model, and used to infer information about the experiment that would be impossible to obtain directly.

For example, using an appropriate ion channel model, it’s possible to investigate individual ionic currents underlying a phenomenon observed in the experiment. Additionally, one can extend an experiment by testing things that are not possible experimentally in the simulation model. For instance, if a shock is applied in the experiment, but one wants to test what would happen if no shock were applied, one could run the shock in the experiment, and run analogous simulations both with and without the shock.

Finally, simulation experiments are not subject to the resolution and field-of-view limitations of electrode or optical transmembrane potential mapping. Therefore, if an interesting phenomenon is observed on the surface of the heart, or is suspected to be occurring within the wall of the heart based on experimental observations, simulations can be used to ‘zoom in’ or examine the depths of the tissue, respectively.

Do you have some experimental data you’d like to know more about? Call us at (888) 525-2232 or send us an email at to discuss how we can help you better understand your experimental results.

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Simulation Services vs. Self-Serve

We are really excited about our self-service CardioSolv Simulation Manager (CSM), featured here and here. The CardioSolv Simulation Manager makes it possible to run simulations with a minimal effect on your budget, and we continue to strive to make it easy for you to get up and running quickly using it. We also offer telephone and email support for the CSM if you’d like a hand getting started.

Nonetheless, you may want us to run simulations for you. We’re happy to do that. We’ll meet with you, carefully listen to your project and simulation needs, and advise you on the best way to proceed. We will then run all of the specified simulations, check them, and send them to you securely. We can also set things up for you in the CardioSolv Simulation Manager and let you run further simulations yourself.

If you have any questions or would otherwise like to discuss a project with us (free of charge), please email or call +1 (888) 525-2232.

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CardioSolv Simulation Manager Video – Activation/Repolarization/APD/DF Mapping

Below the cut, you will find the second screencast demo of our web-based simulation creator, manager, and analysis tool. In the video, a stable spiral wave in a monodomain sheet is analyzed using four techniques:

  1. Activation Mapping: The activation times of the nodes in the mesh are found
  2. Repolarization Mapping: The repolarization times of the nodes in the mesh are found
  3. Action Potential Duration (APD) Mapping: These maps display the APDs of the nodes in the mesh, which is just the repolarization times minus the activations times
  4. Dominant Frequency Mapping: The power spectra of the nodes are computed and the dominant frequency is shown in a map.

These analyses are done on the back-end, and when run on a cluster many can be computed simultaneously by using multiple compute nodes. It is easy for us to add other types of analysis, should they interest you. What kind of analysis would you like to see added?

For more information on the simulator, manager, or CardioSolv, please contact or +1 (888) 525-2232.

Note: If you’d like to watch the video in high definition, I recommend expanding it to full-screen mode.
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