Matthias Reumann

Preventive Ablation Strategies in a Biophysical Model of Atrial Fibrillation Based on Realistic Anatomical Data

Ablation strategies to prevent episodes of paroxysmal atrial fibrillation (AF) have been subject to many clinical studies. The issues mainly concern pattern and transmurality of the lesions. This paper investigates ten different ablation strategies on a multilayered 3-D anatomical model of the atria with respect to 23 different setups of AF initiation in a biophysical computer model. There were 495 simulations carried out showing that circumferential lesions around the pulmonary veins (PVs) yield the highest success rate if at least two additional linear lesions are carried out. The findings compare with clinical studies as well as with other computer simulations. The anatomy and the setup of ectopic beats play an important role in the initiation and maintenance of AF as well as the resulting therapy. The computer model presented in this paper is a suitable tool to investigate different ablation strategies. By including individual patient anatomy and electrophysiological measurement, the model could be parameterized to yield an effective tool for future investigation of tailored ablation strategies and their effects on atrial fibrillation.

Use of Mutant-Specific Ion Channel Characteristics for Risk Stratification of Long QT Syndrome Patients

Mutations that slow the opening of potassium channels in the heart can predict risk for long QT syndrome, a heart arrhythmia that can cause sudden death. Keeping the Heart in Tune The decades-long, 24/7 beating of the human heart is sustained by a symphony of ion channels rhythmically opening and closing on cue. Hearts of those born with mutations in these channels can occasionally hit a bad note, which can cause heart palpitations or—sometimes—sudden death. This so-called long QT syndrome can be diagnosed from an electrocardiogram (ECG) and other clinical parameters, but the distance between the Q and the T waves of the ECG predicts disease well only when the gap is very long—more than 500 ms. Now, Jons et al. have found an electrical characteristic of the IKs channel—the mutation-specific rate at which it opens—that allows the accurate diagnosis of individuals with a QT interval less than 500 ms. Seventeen different mutations in the IKs channel were identified in a group of 387 patients with long QT syndrome. To scrutinize the details of these mutation-carrying channels, the authors expressed the mutated subunits in frog oocytes and then analyzed their function with electrophysiological electrodes and stimulation. The mutated channels carried about 30% less current and tended to activate (open) more slowly than the wild-type ones, but in contrast, the aberrant channels deactivated (closed) at the same rate as their normal counterparts. By using multivariate regression, the authors showed that the diminished amount of current contributed directly to the longer QT interval seen in these patients (and so did not add to the information provided by an ECG), but the slow activation was an independent parameter relative to the QT gap. Further, when the authors analyzed the clinical history of the patients carrying these mutations, they found that the extent of the slowing of channel activation correlated positively with episodes of cardiac dysfunction—syncope (loss of consciousness), cardiac arrest requiring defibrillation, and sudden death—before age 30. But would the slowing of activation of IKs channels really disturb the beating heart enough to cause these cardiac problems? The authors used a computer model to find out. This analysis revealed that beating heart cells that carry the slowly activating mutant channels exhibit prolonged action potentials, which would compromise the cell’s ability to recover from any early beats experienced by the heart. This impairment could trigger arrhythmias such as those seen in the patients. These results could ensure better care for some patients with long QT syndrome, particularly those with only modest increases in their QT intervals. Right now, such patients sometimes remain untreated. Screening patients for channel mutations that cause slower activation could help to identify those at greatest risk, allowing proper intervention to prevent the electrical dissonance that can lead to lethal cardiac arrhythmias. Inherited long QT syndrome (LQTS) is caused by mutations in ion channels that delay cardiac repolarization, increasing the risk of sudden death from ventricular arrhythmias. Currently, the risk of sudden death in individuals with LQTS is estimated from clinical parameters such as age, gender, and the QT interval, measured from the electrocardiogram. Even though a number of different mutations can cause LQTS, mutation-specific information is rarely used clinically. LQTS type 1 (LQT1), one of the most common forms of LQTS, is caused by mutations in the slow potassium current (IKs) channel α subunit KCNQ1. We investigated whether mutation-specific changes in IKs function can predict cardiac risk in LQT1. By correlating the clinical phenotype of 387 LQT1 patients with the cellular electrophysiological characteristics caused by an array of mutations in KCNQ1, we found that channels with a decreased rate of current activation are associated with increased risk of cardiac events (hazard ratio = 2.02), independent of the clinical parameters usually used for risk stratification. In patients with moderate QT prolongation (a QT interval less than 500 ms), slower activation was an independent predictor for cardiac events (syncope, aborted cardiac arrest, and sudden death) (hazard ratio = 2.10), whereas the length of the QT interval itself was not. Our results indicate that genotype and biophysical phenotype analysis may be useful for risk stratification of LQT1 patients and suggest that slow channel activation is associated with an increased risk of cardiac events.

Computer model for the optimization of AV and VV delay in cardiac resynchronization therapy

An optimal electrode position, atrio-ventricular (AV) and interventricular (VV) delay in cardiac resynchronization therapy (CRT) improves its success. An optimization strategy does not yet exist. A computer model of the Visible Man and a patient heart was used to simulate an atrio-ventricular and a left bundle branch block with 0%, 20% and 40% reduction in interventricular conduction velocity, respectively. The minimum error between physiological excitation and pathology/therapy was automatically computed for 12 different electrode positions. AV and VV delay timing was adjusted accordingly. The results show the importance of individually adjusting the electrode position as well as the timing delays to the patient’s anatomy and pathology, which is in accordance with current clinical studies. The presented methods and strategy offer the opportunity to carry out non-invasive, automatic optimization of CRT preoperatively. The model is subject to validation in future clinical studies.

WGS Analysis and Interpretation in Clinical and Public Health Microbiology Laboratories: What Are the Requirements and How Do Existing Tools Compare?

Recent advances in DNA sequencing technologies have the potential to transform the field of clinical and public health microbiology, and in the last few years numerous case studies have demonstrated successful applications in this context. Among other considerations, a lack of user-friendly data analysis and interpretation tools has been frequently cited as a major barrier to routine use of these techniques. Here we consider the requirements of microbiology laboratories for the analysis, clinical interpretation and management of bacterial whole-genome sequence (WGS) data. Then we discuss relevant, existing WGS analysis tools. We highlight many essential and useful features that are represented among existing tools, but find that no single tool fulfils all of the necessary requirements. We conclude that to fully realise the potential of WGS analyses for clinical and public health microbiology laboratories of all scales, we will need to develop tools specifically with the needs of these laboratories in mind.

Towards real-time simulation of cardiac electrophysiology in a human heart at high resolution

We have developed the capability to rapidly simulate cardiac electrophysiological phenomena in a human heart discretised at a resolution comparable with the length of a cardiac myocyte. Previous scientific investigation has generally invoked simplified geometries or coarse-resolution hearts, with simulation duration limited to 10s of heartbeats. Using state-of-the-art high-performance computing techniques coupled with one of the most powerful computers available (the 20 PFlop/s IBM BlueGene/Q at Lawrence Livermore National Laboratory), high-resolution simulation of the human heart can now be carried out over 1200 times faster compared with published results in the field. We demonstrate the utility of this capability by simulating, for the first time, the formation of transmural re-entrant waves in a 3D human heart. Such wave patterns are thought to underlie Torsades de Pointes, an arrhythmia that indicates a high risk of sudden cardiac death. Our new simulation capability has the potential to impact a multitude of applications in medicine, pharmaceuticals and implantable devices.

Enzymology of Bacterial Lysine Biosynthesis

Lysine is an essential amino acid in the mammalian diet, but can be synthesised de novo in bacteria, plants and some fungi (Dogovski et al., 2009; Hutton et al., 2007). In bacteria, the lysine biosynthesis pathway, also known as the diaminopimelate (DAP) pathway (Fig. 1), yields the important metabolites meso-2,6-diaminopimelate (meso-DAP) and lysine. Lysine is utilised for protein synthesis in bacteria and forms part of the peptidoglycan cross-link structure in the cell wall of most Gram-positive species; whilst meso-DAP is the peptidoglycan cross-linking moiety in the cell wall of Gram-negative bacteria and also Gram-positive Bacillus species (Burgess et al., 2008; Mitsakos et al., 2008; Voss et al., 2010) (Fig. 1).

Performance of Hybrid Programming Models for Multiscale Cardiac Simulations: Preparing for Petascale Computation

Future multiscale and multiphysics models that support research into human disease, translational medical science, and treatment can utilize the power of high-performance computing (HPC) systems. We anticipate that computationally efficient multiscale models will require the use of sophisticated hybrid programming models, mixing distributed message-passing processes [e.g., the message-passing interface (MPI)] with multithreading (e.g., OpenMP, Pthreads). The objective of this study is to compare the performance of such hybrid programming models when applied to the simulation of a realistic physiological multiscale model of the heart. Our results show that the hybrid models perform favorably when compared to an implementation using only the MPI and, furthermore, that OpenMP in combination with the MPI provides a satisfactory compromise between performance and code complexity. Having the ability to use threads within MPI processes enables the sophisticated use of all processor cores for both computation and communication phases. Considering that HPC systems in 2012 will have two orders of magnitude more cores than what was used in this study, we believe that faster than real-time multiscale cardiac simulations can be achieved on these systems.

Computational modeling of cardiac disease: potential for personalized medicine

Cardiovascular diseases are leading causes of death, reduce life quality and consume almost half a trillion dollars in healthcare expenses in the USA alone. Cardiac modeling and simulation technologies hold promise as important tools to improve cardiac care and are already in use to elucidate the fundamental mechanisms of cardiac physiology and pathophysiology. However, the emphasis has been on simulating average or exemplar cases. This report describes two classes of cardiac modeling efforts. First, electrophysiological models of channelopathies simulate the altered electrical activity that is thought to promote arrhythmias. Second, electromechanical models attempt to capture both the electrophysiological and mechanical aspects of heart function. One goal of the community is to develop models with sufficient patient customization to assist in personalized treatment planning. Some model aspects can be customized with existing data collection techniques to more closely represent individual patients while other model aspects will likely remain based on generic data. Despite important challenges, cardiac models hold promise to be important enablers of personalized medicine.

Strong scaling and speedup to 16,384 processors in cardiac electro — Mechanical simulations

High performance computing is required to make feasible simulations of whole organ models of the heart with biophysically detailed cellular models in a clinical setting. Increasing model detail by simulating electrophysiology and mechanical models increases computation demands. We present scaling results of an electro — mechanical cardiac model of two ventricles and compare them to our previously published results using an electrophysiological model only. The anatomical data-set was given by both ventricles of the Visible Female data-set in a 0.2 mm resolution. Fiber orientation was included. Data decomposition for the distribution onto the distributed memory system was carried out by orthogonal recursive bisection. Load weight ratios for non — tissue vs. tissue elements used in the data decomposition were 1:1, 1:2, 1:5, 1:10, 1:25, 1:38.85, 1:50 and 1:100. The ten Tusscher et al. (2004) electrophysiological cell model was used and the Rice et al. (1999) model for the computation of the calcium transient dependent force. Scaling results for 512, 1024, 2048, 4096, 8192 and 16,384 processors were obtained for 1 ms simulation time. The simulations were carried out on an IBM Blue Gene/L supercomputer. The results show linear scaling from 512 to 16,384 processors with speedup factors between 1.82 and 2.14 between partitions. The most optimal load ratio was 1:25 for on all partitions. However, a shift towards load ratios with higher weight for the tissue elements can be recognized as can be expected when adding computational complexity to the model while keeping the same communication setup. This work demonstrates that it is potentially possible to run simulations of 0.5 s using the presented electro-mechanical cardiac model within 1.5 hours.

Toward real-time modeling of human heart ventricles at cellular resolution: simulation of drug-induced arrhythmias

We have developed a highly efficient and scalable cardiac electrophysiology simulation capability that supports groundbreaking resolution and detail to elucidate the mechanisms of sudden cardiac death from arrhythmia. We can simulate thousands of heartbeats at a resolution of 0.1 mm, comparable to the size of cardiac cells, thereby enabling scientific inquiry not previously possible. Based on scaling results from the partially deployed Sequoia IBM Blue Gene/Q machine at Lawrence Livermore National Laboratory and planned optimizations, we estimate that by SC12 we will simulate 8 -- 10 heartbeats per minute -- a time-to-solution 400 -- 500 times faster than the state-of-the-art. Performance between 8 and 11 PFlop/s on the full 1,572,864 cores is anticipated, representing 40 -- 55 percent of peak. The power of the model is demonstrated by illuminating the subtle arrhythmogenic mechanisms of anti-arrhythmic drugs that paradoxically increase arrhythmias in some patient populations.