IBM Research: Coding the human heart

Editor’s note: This article is by Dr. Jeremy Rice a computational physiologist at IBM’s Thomas J. Watson Research Center, as told to Chris Nay, IBM Research Communications.

The Journal of the American College of Cardiology reported in 2006 that about two of 1,000 people, worldwide, die of a ventricular arrhythmia every year – the most common cause of sudden cardiac death. Predicting who will die suddenly from a ventricular arrhythmia is a huge challenge, but current computer simulations that could help cardiologists find effective therapies take hours or even days to run a single heartbeat.

To address this need for faster simulations, my team at IBM Research is working with the Lawrence Livermore National Lab on Cardioid, a code that simulates the human heart on the 20 Petaflop Blue Gene/Q, Sequoia. The 96-rack installation at LLNL can run Cardioid roughly 1,200 times faster than other published results to simulate in exquisite detail the electrophysiology of up to three billion heart cells (similar to the number in real heart) and their cell-to-cell electric coupling.

Our hope is that cardiac specialists could eventually model how their patients will react to certain drugs and how genetic variations predispose some patients to arrhythmias.

What’s happening in the heart

Every heart beat electrically excites every heart cell (unlike skeletal muscle that recruits more or fewer muscles cells depending on the demands). Each heart cell is a small excitable system, and electrical activation spreads in a salutatory fashion to its neighbors.

But during a heart beat, there isn’t a clear separation between the activation of a cell and the interaction between the cells. Hence, the problem of a heart simulation needs a two-fold solution that requires a computer with the ability to track the individual cells and their interactions, up to 10,000 times for each heart beat (the number of interactions between cells, per second). This inter-processor communication is exactly what Sequoia does well.

So, what does Cardiod, running on Sequoia, reveal?

The heart beats at a constantly changing rate, and we found that arrhythmia occurs much more at elevated drug concentrations and at slower heart rates (termed bradycardia), or abnormally skipped heart beats. Our mathematical models can complement clinical work to allow cardiac specialists and researchers to extract more data from experimental studies.

Experiments don’t let us look inside a beating heart to see what is happening at the level of the individual cells. But our simulations let us predict the individual cellular responses. In fact, our simulations suggest arrhythmias can arise from subtle and complex cellular interactions that cannot be resolved with experimental techniques, now or in the foreseeable future.

Building a billion heart cells

Models of single cardiac cells have been developed since the 1960s, and three-dimensional anatomic (or geometric) models of various cardiac structures have been developed since the 1990s. And our work, with the University of Rochester, last year showed that models can greatly complement experimental and clinical data to reveal more about how genetic variations affect a person’s susceptibility to arrhythmia. However, this work only modeled single cells or small networks of cells that lack the complexity of the whole heart.

Our work to model the entire heart actually started in 1998, harnessing Sequoia’s early predecessor, the Blue Gene/L. Matthias Reumann, a post-doctoral student at IBM’s Thomas J Watson Research Lab at the time (now at IBM Research – Australia), developed a code to decompose the heart into small pieces and distribute the work to up to 32,000 processors – an unprecedented number at the time.

Cardioid is today’s version of that code. It can scale to more than 100 times the number of cores than the original – and runs about 13,000 times faster overall on Sequoia. This power means simulations of between 180 million and three billion heart cells, depending on the level of spatial detail we want to model.

Cardioid runs fast enough to allow scientific inquires that were previously impractical with the existing modeling platforms. We have the potential to model natural variations seen across a real population of patients. For example, we could measure drug effects highly dependent on individual characteristics such as age, gender, and disease history.

Also, patients’ drug concentrations constantly change from the time the drug is administered until it eventually leaves their bodies (or another dose is administered). Simulating these time scales were simply impracticable before Cardioid.

We hope to see Cardioid used by companies in the pharmaceutical industry and medical device companies to help with clinical decision support in treating deadly diseases, such as arrhythmias and congestive heart failure. Longer-term applications could include virtual drug trials over simulated patient population.

This video shows how a medication can impact the heart to promote arrhythmias. Here we see a block of the heart wall in a common experimental preparation, with the simulated drug on the left and control case on the right. The red color shows activated tissue, and the blue shows recovered tissue. The interface between red and blue shows a wavefront of activation that spreads from cell to cell. The two sequential stimulations (applied at the location of the boxes on the left surface) produce an arrhythmia known as a spiral wave, where the wavefront can continually spin. Without drug, the pattern will die out quickly whereas with the drug, the cycle is sustained and is potentially fatal.