ComputationalCardiology

Research Areas

• Drug induced arrhythmias
• Computational cardiology
• iPSC disease/population modelling

Research Overview

Sudden cardiac death, primarily as a result of disorders of the electrical system of the heart, account for ~15 per cent of all deaths in our community. The normal flow of electrical currents in the heart depends on the very tightly regulated opening and closing of ion channels – akin to components in an electrical circuit, or the light switches in our homes - which in turn triggers the contractions that pumps blood around the body. The Computational Cardiology Laboratory uses an integrated, multidisciplinary approach that combines high performance computing and cutting-edge in vitro experimentation to understand how dysfunction of these molecular machines, either because of gene defects or the side effects of drugs, can result in potentially fatal cardiac arrhythmias. In doing this, our research is aimed at advancing mechanistic understanding of heart rhythm disorders to develop more effective treatment and better patient management through an understanding of who is at risk of cardiac arrhythmia and when these events might happen. 

There are 4 key areas of research in the Computational Cardiology Laboratory, led by Dr Adam Hill:

1. Using high performance computing to probe cardiac electrical dysfunction

To understand the emergent consequences on the hearts electrical system of changes that occur at the molecular level, it is necessary to integrate experimental data describing cellular and/or molecular physiology and pharmacology into the electrical and structural environment of cardiac tissue. We employ a computational approach to achieve this, ranging in scale from simulations of individual cells to full-scale realistic representations of the heart. Amongst other things, we use these simulations to 

i) Understand the mechanisms of arrhythmic disease and the molecular basis of their presentation on ECG; 

ii) Examine how electrical defects interact with structural abnormalities to contribute to arrhythmogenesis; 

ii) Quantify how subtle person-to-person differences in the ‘background’ electrical environment of the cell account for variability in phenotypic presentation for common disease genotypes and 

iv) Characterise how the side effects of some drugs cause rhythm disturbances in the heart.

2. Drug induced arrhythmias 

Drug block of the human ether à-go-go related gene K+ channel (hERG) is the most common cause of acquired or drug induced long QT syndrome, a disorder of cardiac repolarization that may result in ventricular tachycardia and sudden cardiac death. Risk of drug induced arrhythmias is the most common reason for withdrawal of drugs from preclinical development, yet there is currently no test that is both sensitive and specific for detecting this risk. We are part of a major international initiate (the Comprehensive in vitro proarrhythmic assay (CiPA)) driven by the US FDA to develop better tests for use in preclinical screening. As part of this we are investigating the origin of the promiscuity of drug binding to the hERG channels with an emphasis on understanding the kinetics and state-dependence of drug binding to the channel measured using patch clamp electrophysiology. The overall aim of this work is to use this data to develop a better understanding of drug binding to hERG and other ion channels in order to develop better tests for preclinical screening.

3. Population level modelling of disease and drug response using induced pluripotent stem cells.

The emerging concept of precision medicine aims at providing treatment tailored to the individual patient. For inherited disorders, this involves delivery of therapy directed against mutation-specific pathogenicity such that patients receive an appropriate treatment for their level of risk. However, a critical issue for such targeted therapy is that even in supposedly simple monogenic disorders, the clinical phenotype can vary substantially due to genetic and environmental modulators of the primary disease. As a result, even patients with the same mutation can have dramatically different outcomes. How, then, can we assign appropriate therapy to the individual patient? In order to predict phenotype, and consequently assign the right treatment, it is critical that we develop a quantitative understanding not just of the effect of the primary mutation, but also the variability in emergence of proarrhythmic phenotypes associated with disease genes. To do this we examine how the effects of individual specific proarrhythmic mutations or drugs manifest in vitro in populations of induced pluripotent stem cell derived cardiomyocytes from patients with diverse genetic backgrounds. By combining this approach with cutting edge automated, high throughout phenotyping of cellular electrophysiology and calcium homeostasis this work aims to reveal the origins of variable presentation and incomplete penetrance of disease and improve our ability to assign risk in patient populations with cardiovascular disease.

4. High performance computing techniques

A major problem associated with simulation of complex systems is the computational load that is incurred as a result of increased complexity. In the case of computational cardiology, this bottleneck has often been prohibitive in achieving realistic organ scale simulations of sufficient resolution to be clinically useful. The last decade has seen dramatic increases in available computational power available for simulation of physiological systems as well as development of new approaches such as General Purpose Computing on Graphics Processing Units (GPUGPU) that has allowed massive parallelisation of computational tasks. We have developed algorithms to take advantage of new heterogeneous  processor architectures as well as GPU cluster implementations of cardiac simulation environments in collaboration with the CHASTE consortium. Through collaboration with CSIRO, we have access to their world class high performance computing infrastructure to help drive our work. We see this ongoing technological development as a critical parallel that enables our cardiac research.

Lab Members 

Stewart Heitmann, Senior Staff Scientist

Jordan Thorpe, Senior Postdoctoral Scientist

Monique Windley, Senior Postdoctoral Scientist

Azadeh Zahabi, Senior Research Assistant

Ben Ng, Research Assistant

George Parker, Research Assistant

Jessica Farr, Scientific Software Engineer

Jeya Ramalingam, Research Assistant

Michelle Xu, PhD Student

Cliff Tebay, PhD Student

Lauren Cook, PhD Student

Sutapa Saha, PhD Student

Collaborators

National

Prof Eddy Kiana (WIMR)

Prof Glenn King (IMB)

A/Prof Juliana Hamzah (Curtin)

Prof Livia Hool (UWA)

Prof Chris Semsarian (Cetenary)

Prof David Adam (IHMRI)

International

Prof Gary Mirams

Prof Jon Da Silva

Dr Annika Winbo

Dr Trine Krogh-Madsen

CiPA Partners

1. Nikolova-Krstevski, V., Wagner, S., Yu, Z. Y., Cox, C. D., Cvetkovska, J., Hill, A. P., et al. Endocardial TRPC6 channels act as atrial mechanosensors and load-dependent modulators of endocardial/myocardial cross-talk. JACC Basic and Translational Science. Accepted May 2017
2. Windley MJ, Abi-Gerges N, Fermini B, Hancox JC, Vandenberg JI, Hill AP. Measuring kinetics and potency of hERG block for CiPA. J Pharmacol Toxicol Methods. 2017 Feb 10. pii: S1056-8719(16)30194-0. doi: 10.1016/j.vascn.2017.02.017.
3. Hill AP. Boosting the reserves: additive regulation of cardiac repolarisation. J Physiol. 2017 Jan 17. doi: 10.1113/JP273940.
4. Mann SA, Imtiaz MS, Winbo A, Rydberg A, Perry MD, Couderc JP, Polonsky B, McNitt S, Zareba W, Hill AP*, Vandenberg JI*. Convergence of models of human ventricular myocyte electrophysiology after global optimization to recapitulate clinical long QT phenotypes. Journal of Molecular and Cellular Cardiology. 2016. 100, 25-34.
5. Immanuel SA, Sadrieh A, Baumert M, Couderc JP, Zareba W, Hill AP, Vandenberg JI. T-wave morphology can distinguish healthy controls from LQTS patients. Physiological Measurement 2016. 37 (9), 1456. 
6. Bavi N, Cortes MD, Cox CD, Rohde PR, Liu W, Deitmer JW , Strop P, Hill AP, Rees D, Corry B, Perozo , Martinac B. The role of MscL amphipathic N terminus indicates a blueprint for bilayer mediated gating of mechanosensitive channels. Nature Comms. 2016. doi: 10.1038/NCOMMS11984.  
7. Hill AP, Perry MD, Abi-Gerges N, Couderc J-P, Fermini B, Hancox JC, Knollmann BC, Mirams GR, Skinner J, Zareba W, Vandenberg JI. Computational Cardiology and Risk Stratification for Sudden Cardiac Death: One of the Grand Challenges for Cardiology in the 21st Century. J Physiol 2016. doi: 10.1113/JP272015.
8. Windley MJ, Mann SA, Vandenberg JI, Hill AP. Temperature effects on kinetics of Kv11.1 drug block have important consequences for in silico proarrhythmic risk prediction. Mol Pharmacol 2016 Jul;90(1):1-11. doi: 10.1124/mol.115.103127.
9. Vandenberg JI, Hill AP. An 'alternans' way to quantify arrhythmogenic substrates. J Physiol. 2016 May 1;594(9):2375-6. doi: 10.1113/JP271838.
10. Hodkinson EC, Neijts M, Sadrieh A, Imtiaz MS, Mathias Baumert, Subbiah RN, Hayward CS, Boomsma D, Willemsen G, Vandenberg JI, Hill AP*,de Geus E*. Heritability of ECG Biomarkers in the Netherlands Twin Registry Measured from Holter ECGs. Front Physiol. 2016 Apr 29;7:154. doi: 10.3389/fphys.2016.00154.
11. Lee W, Mann SA, Windley MJ, Imtiaz MS., Vandenberg JI, Hill AP. In silico assessment of kinetics and state dependent binding properties of drugs causing acquired LQTS. Prog Biophys Mol Biol. 2016 Jan;120(1-3):89-99 
12. Sadrieh A, Domanski L, Pitt-Francis J, Mann SA, Hodkinson E, Ng CA, Perry MD, Taylor JA, Gavaghan D, Subbiah RN, Vandenberg JI, Hill AP. Multiscale cardiac modelling reveals the origins of notched T waves in long QT syndrome type 2. Nat. Comm. 2014; 5, 5069. 
13. Hill AP, Perrin MJ, Heide J, Campbell T, Mann S, Vandenberg JI. Kinetics of Drug Interaction with the Kv11.1 Potassium Channel. Mol Pharmacol. 2014; 85 (5), 769-776. 
14. Sadrieh A, Mann SA, Subbiah RN, Domanski L, Taylor JA, Vandenberg JI, Hill AP. Quantifying the origins of population variability in cardiac electrical activity through sensitivity analysis of the electrocardiogram. J Physiol. 2013; 591:4207-4222. 
15. Tan PS, Perry MD, Ng CA, Vandenberg JI, Hill AP. Voltage-sensing domain mode shift is coupled to the activation gate by the N-terminal tail of hERG channels. J Gen Physiol. 2012 Sep;140(3):293-306. 
16. Vandenberg JI, Perry MD, Perrin MJ, Mann SA, Ke Y, Hill AP. hERG K(+) channels: structure, function, and clinical significance. Physiol Rev. 2012 Jul;92(3):1393-478. 
17. *Wang DT, *Hill AP, Mann SA, Tan PS, Vandenberg JI. Mapping the sequence of conformational changes underlying selectivity ^ilter gating in the K(v)11.1 potassium channel. Nat Struct Mol Biol. 2011;18(1):35-41.   
18. Szekely D, Vandenberg JI, Dokos S, Hill AP. An improved curvilinear gradient method for parameter optimization in complex biological models. Med Biol Eng Comput. 2011 Mar;49(3): 289-96. 
19. Clarke OB, Caputo AT, Hill AP, Vandenberg JI, Smith BJ, Gulbis JM. Domain reorientation and rotation of an intracellular assembly regulate conduction in Kir potassium channels. Cell. 2010 Jun 11;141(6):1018-29. 
20. *Zhao JT, *Hill AP, Varghese A, Cooper AA, Swan H, Laitinen-Forsblom PJ , Rees MI, Skinner JR, Campbell TJ, Vandenberg JI. Not all hERG pore domain mutations have a severe phenotype: G584S has an inactivation gating defect with mild phenotype compared to G572S, which has a dominant negative trafficking defect and a severe phenotype. J Cardiovasc Electrophysiol. 2009 Aug;20(8):923-30.