Literature DB >> 21703295

Considerations for the use of cellular electrophysiology models within cardiac tissue simulations.

Jonathan Cooper1, Alberto Corrias, David Gavaghan, Denis Noble.   

Abstract

The use of mathematical models to study cardiac electrophysiology has a long history, and numerous cellular scale models are now available, covering a range of species and cell types. Their use to study emergent properties in tissue is also widespread, typically using the monodomain or bidomain equations coupled to one or more cell models. Despite the relative maturity of this field, little has been written looking in detail at the interface between the cellular and tissue-level models. Mathematically this is relatively straightforward and well-defined. There are however many details and potential inconsistencies that need to be addressed, in order to ensure correct operation of a cellular model within a tissue simulation. This paper will describe these issues and how to address them. Simply having models available in a common format such as CellML is still of limited utility, with significant manual effort being required to integrate these models within a tissue simulation. We will thus also discuss the facilities available for automating this in a consistent fashion within Chaste, our robust and high-performance cardiac electrophysiology simulator. It will be seen that a common theme arising is the need to go beyond a representation of the model mathematics in a standard language, to include additional semantic information required in determining the model's interface, and hence to enhance interoperability. Such information can be added as metadata, but agreement is needed on the terms to use, including development of appropriate ontologies, if reliable automated use of CellML models is to become common.
Copyright © 2011 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2011        PMID: 21703295     DOI: 10.1016/j.pbiomolbio.2011.06.002

Source DB:  PubMed          Journal:  Prog Biophys Mol Biol        ISSN: 0079-6107            Impact factor:   3.667


  12 in total

Review 1.  Human cardiac systems electrophysiology and arrhythmogenesis: iteration of experiment and computation.

Authors:  Katherine M Holzem; Eli J Madden; Igor R Efimov
Journal:  Europace       Date:  2014-11       Impact factor: 5.214

2.  Minimum Information about a Cardiac Electrophysiology Experiment (MICEE): standardised reporting for model reproducibility, interoperability, and data sharing.

Authors:  T A Quinn; S Granite; M A Allessie; C Antzelevitch; C Bollensdorff; G Bub; R A B Burton; E Cerbai; P S Chen; M Delmar; D Difrancesco; Y E Earm; I R Efimov; M Egger; E Entcheva; M Fink; R Fischmeister; M R Franz; A Garny; W R Giles; T Hannes; S E Harding; P J Hunter; G Iribe; J Jalife; C R Johnson; R S Kass; I Kodama; G Koren; P Lord; V S Markhasin; S Matsuoka; A D McCulloch; G R Mirams; G E Morley; S Nattel; D Noble; S P Olesen; A V Panfilov; N A Trayanova; U Ravens; S Richard; D S Rosenbaum; Y Rudy; F Sachs; F B Sachse; D A Saint; U Schotten; O Solovyova; P Taggart; L Tung; A Varró; P G Volders; K Wang; J N Weiss; E Wettwer; E White; R Wilders; R L Winslow; P Kohl
Journal:  Prog Biophys Mol Biol       Date:  2011-07-06       Impact factor: 3.667

3.  A web portal for in-silico action potential predictions.

Authors:  Geoff Williams; Gary R Mirams
Journal:  J Pharmacol Toxicol Methods       Date:  2015-05-09       Impact factor: 1.950

4.  Implementation of Contraction to Electrophysiological Ventricular Myocyte Models, and Their Quantitative Characterization via Post-Extrasystolic Potentiation.

Authors:  Yanyan Claire Ji; Richard A Gray; Flavio H Fenton
Journal:  PLoS One       Date:  2015-08-28       Impact factor: 3.240

5.  Cellular cardiac electrophysiology modeling with Chaste and CellML.

Authors:  Jonathan Cooper; Raymond J Spiteri; Gary R Mirams
Journal:  Front Physiol       Date:  2015-01-06       Impact factor: 4.566

6.  Variability in high-throughput ion-channel screening data and consequences for cardiac safety assessment.

Authors:  Ryan C Elkins; Mark R Davies; Stephen J Brough; David J Gavaghan; Yi Cui; Najah Abi-Gerges; Gary R Mirams
Journal:  J Pharmacol Toxicol Methods       Date:  2013-05-05       Impact factor: 1.950

7.  Chaste: an open source C++ library for computational physiology and biology.

Authors:  Gary R Mirams; Christopher J Arthurs; Miguel O Bernabeu; Rafel Bordas; Jonathan Cooper; Alberto Corrias; Yohan Davit; Sara-Jane Dunn; Alexander G Fletcher; Daniel G Harvey; Megan E Marsh; James M Osborne; Pras Pathmanathan; Joe Pitt-Francis; James Southern; Nejib Zemzemi; David J Gavaghan
Journal:  PLoS Comput Biol       Date:  2013-03-14       Impact factor: 4.475

Review 8.  Combining wet and dry research: experience with model development for cardiac mechano-electric structure-function studies.

Authors:  T Alexander Quinn; Peter Kohl
Journal:  Cardiovasc Res       Date:  2013-01-17       Impact factor: 10.787

9.  Computational model of erratic arrhythmias in a cardiac cell network: the role of gap junctions.

Authors:  Aldo Casaleggio; Michael L Hines; Michele Migliore
Journal:  PLoS One       Date:  2014-06-18       Impact factor: 3.240

10.  Prediction of Thorough QT study results using action potential simulations based on ion channel screens.

Authors:  Gary R Mirams; Mark R Davies; Stephen J Brough; Matthew H Bridgland-Taylor; Yi Cui; David J Gavaghan; Najah Abi-Gerges
Journal:  J Pharmacol Toxicol Methods       Date:  2014-07-31       Impact factor: 1.950

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