Human-based approaches to pharmacology and cardiology: an interdisciplinary and intersectorial workshop

Both biomedical research and clinical practice rely on complex datasets for the physiological and genetic characterization of human hearts in health and disease. Given the complexity and variety of approaches and recordings, there is now growing recognition of the need to embed computational methods in cardiovascular medicine and science for analysis, integration and prediction. This paper describes a Workshop on Computational Cardiovascular Science that created an international, interdisciplinary and inter-sectorial forum to define the next steps for a human-based approach to disease supported by computational methodologies. The main ideas highlighted were (i) a shift towards human-based methodologies, spurred by advances in new in silico, in vivo, in vitro, and ex vivo techniques and the increasing acknowledgement of the limitations of animal models. (ii) Computational approaches complement, expand, bridge, and integrate in vitro, in vivo, and ex vivo experimental and clinical data and methods, and as such they are an integral part of human-based methodologies in pharmacology and medicine. (iii) The effective implementation of multi- and interdisciplinary approaches, teams, and training combining and integrating computational methods with experimental and clinical approaches across academia, industry, and healthcare settings is a priority. (iv) The human-based cross-disciplinary approach requires experts in specific methodologies and domains, who also have the capacity to communicate and collaborate across disciplines and cross-sector environments. (v) This new translational domain for human-based cardiology and pharmacology requires new partnerships supported financially and institutionally across sectors. Institutional, organizational, and social barriers must be identified, understood and overcome in each specific setting

Chaste : Cancer, Heart and Soft Tissue Environment

Funding: UK Engineering and Physical Sciences Research Council [grant number EP/N509711/1 (J.K.)].Chaste (Cancer, Heart And Soft Tissue Environment) is an open source simulation package for the numerical solution of mathematical models arising in physiology and biology. To date, Chaste development has been driven primarily by applications that include continuum modelling of cardiac electrophysiology (‘Cardiac Chaste’), discrete cell-based modelling of soft tissues (‘Cell-based Chaste’), and modelling of ventilation in lungs (‘Lung Chaste’). Cardiac Chaste addresses the need for a high-performance, generic, and verified simulation framewor kfor cardiac electrophysiology that is freely available to the scientific community. Cardiac chaste provides a software package capable of realistic heart simulations that is efficient, rigorously tested, and runs on HPC platforms. Cell-based Chaste addresses the need for efficient and verified implementations of cell-based modelling frameworks, providing a set of extensible tools for simulating biological tissues. Computational modelling, along with live imaging techniques, plays an important role in understanding the processes of tissue growth and repair. A wide range of cell-based modelling frameworks have been developed that have each been successfully applied in a range of biological applications. Cell-based Chaste includes implementations of the cellular automaton model, the cellular Potts model, cell-centre models with cell representations as overlapping spheres or Voronoi tessellations, and the vertex model. Lung Chaste addresses the need for a novel, generic and efficient lung modelling software package that is both tested and verified. It aims to couple biophysically-detailed models of airway mechanics with organ-scale ventilation models in a package that is freely available to the scientific community.Publisher PDFPeer reviewe

Human-based approaches to pharmacology and cardiology: an interdisciplinary and intersectorial workshop.

Both biomedical research and clinical practice rely on complex datasets for the physiological and genetic characterization of human hearts in health and disease. Given the complexity and variety of approaches and recordings, there is now growing recognition of the need to embed computational methods in cardiovascular medicine and science for analysis, integration and prediction. This paper describes a Workshop on Computational Cardiovascular Science that created an international, interdisciplinary and inter-sectorial forum to define the next steps for a human-based approach to disease supported by computational methodologies. The main ideas highlighted were (i) a shift towards human-based methodologies, spurred by advances in new in silico, in vivo, in vitro, and ex vivo techniques and the increasing acknowledgement of the limitations of animal models. (ii) Computational approaches complement, expand, bridge, and integrate in vitro, in vivo, and ex vivo experimental and clinical data and methods, and as such they are an integral part of human-based methodologies in pharmacology and medicine. (iii) The effective implementation of multi- and interdisciplinary approaches, teams, and training combining and integrating computational methods with experimental and clinical approaches across academia, industry, and healthcare settings is a priority. (iv) The human-based cross-disciplinary approach requires experts in specific methodologies and domains, who also have the capacity to communicate and collaborate across disciplines and cross-sector environments. (v) This new translational domain for human-based cardiology and pharmacology requires new partnerships supported financially and institutionally across sectors. Institutional, organizational, and social barriers must be identified, understood and overcome in each specific setting

A computational investigation of the electrocardiogram with healthy and diseased human ventricles

See the included readme.tx

A computational investigation of the electrocardiogram with healthy and diseased human ventricles

Cardiovascular diseases are the leading cause of death worldwide, and are estimated to kill over 17 million people each year, about 31&amp;percnt; of all deaths. In the clinic, the first diagnostic procedure for a suspected cardiac abnormality is often acquisition of an electrocardiogram (ECG), which measures the electrical potential of the heart at the body surface. Understanding the mechanisms underlying generation of the ECG waveforms is crucial for optimal clinical benefit. Computer simulations possess several strengths as a tool to gain this understanding, particularly in terms of human-specificity, flexibility, repeatability, and ethics. The ventricles make up the majority of the cardiac volume and are therefore responsible for the majority of ECG waveforms. Ventricular disorders are the most life-threatening, because the ventricles are responsible for pumping blood to the body. Due to their size it has only recently become possible to perform biophysically detailed simulations of the ventricles and torso using supercomputers. In this thesis, multiscale, mathematical models of the ventricles and torso using the Chaste software library are simulated on high performance computing systems. A description is included of the performance enhancements made in Chaste to improve resource efficiency and accelerate job turnaround, particularly in data storage and the auxiliary tasks of post-processing and data conversion. A novel model of ventricular activation is presented and parametrized using multi-modal human data, and successfully used to simulate normal and pathological QRS complexes. Similarly, repolarization gradients are imposed based on the literature and result in a variety of T waves. Finally, the developed human whole-ventricular and torso models are utilized to gain new insights into possible ionic mechanisms underlying the clinical manifestations of the early repolarization syndrome. Overall, this thesis presents a novel framework for simulation of the human ECG using high performance computers, with possible applications in basic science and computational medicine.</p

Human ventricular activation sequence and the simulation of the electrocardiographic QRS complex and its variability in healthy and intraventricular block conditions

Data underlying the figures in our research paper. See readme.txt for usage information

A computational investigation of the electrocardiogram with healthy and diseased human ventricles

Cardiovascular diseases are the leading cause of death worldwide, and are estimated to kill over 17 million people each year, about 31&percnt; of all deaths. In the clinic, the first diagnostic procedure for a suspected cardiac abnormality is often acquisition of an electrocardiogram (ECG), which measures the electrical potential of the heart at the body surface. Understanding the mechanisms underlying generation of the ECG waveforms is crucial for optimal clinical benefit. Computer simulations possess several strengths as a tool to gain this understanding, particularly in terms of human-specificity, flexibility, repeatability, and ethics. The ventricles make up the majority of the cardiac volume and are therefore responsible for the majority of ECG waveforms. Ventricular disorders are the most life-threatening, because the ventricles are responsible for pumping blood to the body. Due to their size it has only recently become possible to perform biophysically detailed simulations of the ventricles and torso using supercomputers. In this thesis, multiscale, mathematical models of the ventricles and torso using the Chaste software library are simulated on high performance computing systems. A description is included of the performance enhancements made in Chaste to improve resource efficiency and accelerate job turnaround, particularly in data storage and the auxiliary tasks of post-processing and data conversion. A novel model of ventricular activation is presented and parametrized using multi-modal human data, and successfully used to simulate normal and pathological QRS complexes. Similarly, repolarization gradients are imposed based on the literature and result in a variety of T waves. Finally, the developed human whole-ventricular and torso models are utilized to gain new insights into possible ionic mechanisms underlying the clinical manifestations of the early repolarization syndrome. Overall, this thesis presents a novel framework for simulation of the human ECG using high performance computers, with possible applications in basic science and computational medicine.</p

A computational investigation of the electrocardiogram with healthy and diseased human ventricles

Cardiovascular diseases are the leading cause of death worldwide, and are estimated to kill over 17 million people each year, about 31&percnt; of all deaths. In the clinic, the first diagnostic procedure for a suspected cardiac abnormality is often acquisition of an electrocardiogram (ECG), which measures the electrical potential of the heart at the body surface. Understanding the mechanisms underlying generation of the ECG waveforms is crucial for optimal clinical benefit. Computer simulations possess several strengths as a tool to gain this understanding, particularly in terms of human-specificity, flexibility, repeatability, and ethics. The ventricles make up the majority of the cardiac volume and are therefore responsible for the majority of ECG waveforms. Ventricular disorders are the most life-threatening, because the ventricles are responsible for pumping blood to the body. Due to their size it has only recently become possible to perform biophysically detailed simulations of the ventricles and torso using supercomputers. In this thesis, multiscale, mathematical models of the ventricles and torso using the Chaste software library are simulated on high performance computing systems. A description is included of the performance enhancements made in Chaste to improve resource efficiency and accelerate job turnaround, particularly in data storage and the auxiliary tasks of post-processing and data conversion. A novel model of ventricular activation is presented and parametrized using multi-modal human data, and successfully used to simulate normal and pathological QRS complexes. Similarly, repolarization gradients are imposed based on the literature and result in a variety of T waves. Finally, the developed human whole-ventricular and torso models are utilized to gain new insights into possible ionic mechanisms underlying the clinical manifestations of the early repolarization syndrome. Overall, this thesis presents a novel framework for simulation of the human ECG using high performance computers, with possible applications in basic science and computational medicine.</p

Strategies of data layout and cache writing for input-output optimization in high performance scientific computing: Applications to the forward electrocardiographic problem.

Input-output (I/O) optimization at the low-level design of data layout on disk drastically impacts the efficiency of high performance computing (HPC) applications. However, such a low-level optimization is in general challenging, especially when using popular scientific file formats designed with an emphasis on portability and flexibility. To reconcile these two aspects, we present a novel low-level data layout for HPC applications, fully independent of the number of dimensions in the dataset. The new data layout improves reading and writing efficiency in large HPC applications using many processors, and in particular during parallel post-processing. Furthermore, its combination with a cached write mode, in order to aggregate multiple writes into larger ones, substantially decreased the writing times of the proposed strategy. When applied to our simulation framework for the forward calculation of the human electrocardiogram, the combined strategy resulted in drastic improvements in I/O performance, of up to 40% in writing and 93-98% in reading for post-processing tasks. Given the generality of the proposed strategies and scientific file formats used, our results may represent significant improvements in I/O performance of HPC applications across multiple disciplines, reducing execution and post-processing times and leading to a more efficient use of HPC resource envelopes