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Cellular cardiac electrophysiology modeling with Chaste and CellML

Overview of attention for article published in Frontiers in Physiology, January 2015
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  • High Attention Score compared to outputs of the same age and source (83rd percentile)

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Title
Cellular cardiac electrophysiology modeling with Chaste and CellML
Published in
Frontiers in Physiology, January 2015
DOI 10.3389/fphys.2014.00511
Pubmed ID
Authors

Jonathan Cooper, Raymond J. Spiteri, Gary R. Mirams

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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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 5%
Netherlands 1 2%
Russia 1 2%
Unknown 37 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 37%
Student > Ph. D. Student 11 27%
Student > Master 5 12%
Student > Doctoral Student 2 5%
Student > Bachelor 2 5%
Other 5 12%
Unknown 1 2%
Readers by discipline Count As %
Engineering 10 24%
Computer Science 7 17%
Biochemistry, Genetics and Molecular Biology 5 12%
Agricultural and Biological Sciences 4 10%
Mathematics 4 10%
Other 7 17%
Unknown 4 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 April 2015.
All research outputs
#5,864,924
of 23,322,258 outputs
Outputs from Frontiers in Physiology
#2,691
of 14,050 outputs
Outputs of similar age
#77,587
of 355,171 outputs
Outputs of similar age from Frontiers in Physiology
#20
of 115 outputs
Altmetric has tracked 23,322,258 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 14,050 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one has done well, scoring higher than 80% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 355,171 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 115 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.