Some of our lab’s “competitors” have a really nice article covering many many of the existing cardiac ionic models up on Scholarpedia, with illustrations and even java applets and movies.

The article is entitled Models of cardiac cell [sic].

Kudos to Drs. Fenton and Cherry for the excellent article, it looks like it was quite a lot of work to put together!

I needed the package mentioned in the title, but it fails to install due to some problems with the dependency configuration in the original package.

Following some instructions here and using the patch provided here (from this thread), I was finally able to get it working.

Here’s the resulting package.

python-qwt5-qt4_510dfsg-1ubuntu1_amd64

Steps to build:

Create some directory where you want to build this. Change to that directory. Then…

sudo apt-get build-dep python-qwt5-qt4

(installs dependencies for building)


sudo apt-get source python-qwt5-qt4


This should download the package source. Download the patch from the comment I linked to above, then change into the directory and patch the files. You may have to change the directory ownership so that your user can write to it (sudo chown -R yourusername .)


patch -p0 < ~/Inbox/pyqwt-final.diff

(of course your patch may be located in a different location)

Per the GPLv2, here's the patched source I used to build the .deb file (note -- I think you can just download this instead of patching it yourself).

brocks_patched_pywqt5_src.tar.bz2

Once it's patched, rebuild it with:

sudo dpkg-buildpackage -rfakeroot -uc -b


Go get a cup of coffee or whatever, it's a fairly involved build. When it's done, in the directory above you should find your .deb packages. A simple:

sudo dpkg -i python-qwt5-qt4_5.1.0.dfsg-2_amd64.deb

(replace the filename with whatever yours is, of course)

will install it for you. Hope this is helpful for someone, as I wasted a few hours trying to install this all from source and so on.

Today I’m really excited to finally show you something that’s been in the works, both in implementation and in the planning stages, for a long time. The CardioSolv Simulation Manager.

Running cardiac electrophysiology (and mechanics) simulations has traditionally been really complicated. It involved learning a bunch of UNIX command-line tricks, dealing with queuing systems and their associated script files, and so on. Furthermore, there are many, many options in a sophisticated cardiac simulator, and the novice user (and even the expert) can easily get lost in all of the choices.

We’ve taken years of experience setting up, running, and analyzing simulations to build a really cool (excuse my excitement) web interface that handles all of the dirty work, and guides the user through the important choices when running simulations.

The video below is my first demo. In it, I demonstrate how to create a plane wave moving across a sheet of tissue, then create a spiral wave, all from the web interface.

(more…)

The whole article is here.

The HPC service lets the small, five-employee company do the heavy lifting that would otherwise cost a fortune. “With what we could purchase out of pocket, we’d have to bootstrap very slowly, or look for VC [venture capital] funding,” said Dr. Brock Tice, the vice president of operations at Cardiosolv, a privately funded medical research firm. Instead, Tice uses a new HPC on-demand service from Penguin Computing called Penguin on Demand.

…

While Cardiosolv has its own small cluster on the premises for calculations, Tice estimates the resources he rents from Penguin would probably cost $500,000 to build, and other cloud options weren’t suitable.

“We can’t use [Amazon's Elastic Compute Cloud] EC2, since there’s a lot of latency between the nodes,” he said.

Here’s my two bits from the story:

Brock Tice is one of those scientists. As vp of operations at the Baltimore, Maryland-based CardioSolv, he works to model, yes, the heart – simulating its mechanical and electrical activity. And though he can run some simulations on Amazon’s cloud – or on individual local machines – more complex models require HPC. “We’re [sic] tried on Amazon and it just doesn’t scale,” he tells The Reg. “We can run on single EC2 instances, but if we need to scale up to a dog or human heart, it’s just impossible.

“The connections between Amazon’s machines are Gigabit Ethernet and they’re shared. If you fire up 10 machines and you want to run them like a cluster, some might be in the same rack, and others might be halfway across the data center, five or six switches away.”

You can find the full story here.