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      Cellular cardiac electrophysiology modeling with Chaste and CellML

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          Abstract

          Chaste is an open-source C++ library for computational biology that has well-developed cardiac electrophysiology tissue simulation support. In this paper, we introduce the features available for performing cardiac electrophysiology action potential simulations using a wide range of models from the Physiome repository. The mathematics of the models are described in CellML, with units for all quantities. The primary idea is that the model is defined in one place (the CellML file), and all model code is auto-generated at compile or run time; it never has to be manually edited. We use ontological annotation to identify model variables describing certain biological quantities (membrane voltage, capacitance, etc.) to allow us to import any relevant CellML models into the Chaste framework in consistent units and to interact with them via consistent interfaces. This approach provides a great deal of flexibility for analysing different models of the same system. Chaste provides a wide choice of numerical methods for solving the ordinary differential equations that describe the models. Fixed-timestep explicit and implicit solvers are provided, as discussed in previous work. Here we introduce the Rush–Larsen and Generalized Rush–Larsen integration techniques, made available via symbolic manipulation of the model equations, which are automatically rearranged into the forms required by these approaches. We have also integrated the CVODE solvers, a ‘gold standard’ for stiff systems, and we have developed support for symbolic computation of the Jacobian matrix, yielding further increases in the performance and accuracy of CVODE. We discuss some of the technical details of this work and compare the performance of the available numerical methods. Finally, we discuss how this is generalized in our functional curation framework, which uses a domain-specific language for defining complex experiments as a basis for comparison of model behavior.

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          Most cited references34

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          The Physiome Model Repository 2.

          The Physiome Model Repository 2 (PMR2) software was created as part of the IUPS Physiome Project (Hunter and Borg, 2003), and today it serves as the foundation for the CellML model repository. Key advantages brought to the end user by PMR2 include: facilities for model exchange, enhanced collaboration and a detailed change history for each model. PMR2 is available under an open source license at http://www.cellml.org/tools/pmr/; a fully functional instance of this software can be accessed at http://models.physiomeproject.org/.
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            Na(+) channel mutation that causes both Brugada and long-QT syndrome phenotypes: a simulation study of mechanism.

            Complex physiological interactions determine the functional consequences of gene abnormalities and make mechanistic interpretation of phenotypes extremely difficult. A recent example is a single mutation in the C terminus of the cardiac Na(+) channel, 1795insD. The mutation causes two distinct clinical syndromes, long QT (LQT) and Brugada, leading to life-threatening cardiac arrhythmias. Coexistence of these syndromes is seemingly paradoxical; LQT is associated with enhanced Na(+) channel function, and Brugada with reduced function. Using a computational approach, we demonstrate that the 1795insD mutation exerts variable effects depending on the myocardial substrate. We develop Markov models of the wild-type and 1795insD cardiac Na(+) channels. By incorporating the models into a virtual transgenic cell, we elucidate the mechanism by which 1795insD differentially disrupts cellular electrical behavior in epicardial and midmyocardial cell types. We provide a cellular mechanistic basis for the ECG abnormalities observed in patients carrying the 1795insD gene mutation. We demonstrate that the 1795insD mutation can cause both LQT and Brugada syndromes through interaction with the heterogeneous myocardium in a rate-dependent manner. The results highlight the complexity and multiplicity of genotype-phenotype relationships, and the usefulness of computational approaches in establishing a mechanistic link between genetic defects and functional abnormalities.
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              Prediction of Thorough QT study results using action potential simulations based on ion channel screens

              Introduction Detection of drug-induced pro-arrhythmic risk is a primary concern for pharmaceutical companies and regulators. Increased risk is linked to prolongation of the QT interval on the body surface ECG. Recent studies have shown that multiple ion channel interactions can be required to predict changes in ventricular repolarisation and therefore QT intervals. In this study we attempt to predict the result of the human clinical Thorough QT (TQT) study, using multiple ion channel screening which is available early in drug development. Methods Ion current reduction was measured, in the presence of marketed drugs which have had a TQT study, for channels encoded by hERG, CaV1.2, NaV1.5, KCNQ1/MinK, and Kv4.3/KChIP2.2. The screen was performed on two platforms — IonWorks Quattro (all 5 channels, 34 compounds), and IonWorks Barracuda (hERG & CaV1.2, 26 compounds). Concentration–effect curves were fitted to the resulting data, and used to calculate a percentage reduction in each current at a given concentration. Action potential simulations were then performed using the ten Tusscher and Panfilov (2006), Grandi et al. (2010) and O'Hara et al. (2011) human ventricular action potential models, pacing at 1 Hz and running to steady state, for a range of concentrations. Results We compared simulated action potential duration predictions with the QT prolongation observed in the TQT studies. At the estimated concentrations, simulations tended to underestimate any observed QT prolongation. When considering a wider range of concentrations, and conventional patch clamp rather than screening data for hERG, prolongation of ≥ 5 ms was predicted with up to 79% sensitivity and 100% specificity. Discussion This study provides a proof-of-principle for the prediction of human TQT study results using data available early in drug development. We highlight a number of areas that need refinement to improve the method's predictive power, but the results suggest that such approaches will provide a useful tool in cardiac safety assessment.
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                Author and article information

                Contributors
                Journal
                Front Physiol
                Front Physiol
                Front. Physiol.
                Frontiers in Physiology
                Frontiers Media S.A.
                1664-042X
                25 November 2014
                06 January 2015
                2014
                : 5
                : 511
                Affiliations
                [1] 1Computational Biology, Department of Computer Science, University of Oxford Oxford, UK
                [2] 2Numerical Simulation Research Lab, Department of Computer Science, University of Saskatchewan Saskatoon, SK, Canada
                Author notes

                Edited by: Steve McKeever, Uppsala University, Sweden

                Reviewed by: Olga Solovyova, Institute of Immunology and Physiology of Ural Branch of Russian Academy of Sciences, Russia; Chris Patrick Bradley, University of Auckland, New Zealand

                *Correspondence: Jonathan Cooper, Computational Biology, Department of Computer Science, University of Oxford, Parks Road, Oxford, OX1 3QD, UK e-mail: jonathan.cooper@ 123456cs.ox.ac.uk

                This article was submitted to Computational Physiology and Medicine, a section of the journal Frontiers in Physiology.

                Article
                10.3389/fphys.2014.00511
                4285015
                25610400
                0d418f91-0a23-49dd-ad7e-94c0c3655950
                Copyright © 2015 Cooper, Spiteri and Mirams.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 31 October 2014
                : 09 December 2014
                Page count
                Figures: 6, Tables: 2, Equations: 7, References: 93, Pages: 16, Words: 13508
                Categories
                Physiology
                Technology Report Article

                Anatomy & Physiology
                cellml,cardiac,ode,electrophysiology,simulation,c++,software
                Anatomy & Physiology
                cellml, cardiac, ode, electrophysiology, simulation, c++, software

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