Published December 6, 2022 | Version v1
Dataset Open

Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Heart Electromechanics Model Using Gaussian Processes Emulators - Training Datasets

Creators

  • 1. King's College London and Imperial College London
  • 2. King's College London
  • 3. Medical University of Graz and BioTechMed-Graz
  • 4. Medical University of Graz
  • 5. Johann Radon Institute for Computational and Applied Mathematics (RICAM)
  • 6. King's College London and Guy's and St Thomas' NHS Foundation Trust
  • 7. University of Bordeaux and IHU Lyric
  • 8. Newcastle University
  • 9. University of Nottingham
  • 10. King's College London, The Alan Turing Institute and Imperial College London

Description

This database contains all training datasets for the Gaussian processes emulators (GPEs) trained in the study entitled "Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Electromechanics Model Using Gaussian Processes Emulators", submitted to PLOS Computational Biology.

Every folder contains two csv files:

- parameters.csv: the rows are the samples and the columns represent the parameters that were varied in the analysis

- outputs.csv: the rows are the samples and the columns represent the values for the output features simulated for each sample

In ventricular_cell_model, there are four folders:

- ionic: ToR-ORd model samples used to train GPEs to predict the ventricular calcium transient features

- contraction_isometric_stretch1.0: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isometric contractions with no strain (or stretch 1.0).

- contraction_isometric_stretch1.1: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isometric contractions with 0.1 strain (or stretch 1.1).

- contraction_isotonic: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isotonic.

The folder atrial_contraction_model follows the same structure, but the ionic model was Courtemanche, used to represent an atrial rather than ventricular calcium transient.

The folder tissue_electrophysiology contains the training dataset for the GPEs to predict total atrial and ventricular activation times with an Eikonal model.

The folder passive_mechanics contains the training dataset for the GPEs to predict inflated volumes and mean atrial and ventricular fiber strains for a passive inflation.

The folder CircAdapt contains the training dataset for the GPEs to predict four-chamber pressure and volume features with the CircAdapt ODE model.

Finally, the folder fourchamber contains the samples generated with a 3D-0D four-chamber electromechanics model to predict pressure and volume biomarkers for cardiac function.

The details about the model can be found in the original publication.

Notes

This work was supported by the Wellcome/EPSRC Centre for Medical Engineering (WT 203148/Z/16/Z). SAN is supported by NIH R01-HL152256, ERC PREDICT-HF 453 (864055), BHF (RG/20/4/34803), EPSRC (EP/P01268X/1). This work was supported by the Technology Missions Fund under the EPSRC Grant EP/X03870X/1 & The Alan Turing Institute. This study received financial support from the French Government as part of the "Investments of the Future" program managed by the National Research Agency (ANR) (ANR-10-IAHU-04). This research has received funding from the European Union's Horizon 2020 research and innovation programme under the ERA-NET co-fund action No. 680969 (ERA-CVD SICVALVES) funded by the Austrian Science Fund (FWF), Grant I 4652-B to CMA. Dr Petras A. was partially supported by the State of Upper Austria. This work used the ARCHER2 UK National Supercomputing Service (https://www.archer2.ac.uk). Dr Neic A. is employed by NumeriCor GmbH, Graz, Austria.

Files

Files (1.1 MB)

Name Size Download all
md5:f0eb356585799aff5c79acefb9be280e
424.6 kB Download
md5:e3d2ee66ef8932693046fd32327662e3
79.8 kB Download
md5:665a1dc951ca21c3f0415f3e7893f955
80.4 kB Download
md5:b00377065da70e21caeebd9c2fcc7d94
9.7 kB Download
md5:ef4c21aef04568b43efad088d3f38b1c
5.2 kB Download
md5:3fb2b6ee5112ce9f846e259b62ea7073
544.2 kB Download

Additional details

Related works

Is cited by
Journal article: 10.1371/journal.pcbi.1011257 (DOI)

Funding

FWF Austrian Science Fund
SICVALVES - Multiscale Modeling of Valvular Heart Diseases I 4652
UK Research and Innovation
The Alan Turing Institute 22/23 - Core and Additional Funding EP/X03870X/1
UK Research and Innovation
Uncertainty Quantification in Prospective and Predictive Patient Specific Cardiac Models EP/P01268X/1
Agence Nationale de la Recherche
LIRYC - L'Institut de Rythmologie et modélisation Cardiaque ANR-10-IAHU-0004
European Commission
ERA-CVD - ERA-NET on cardiovascular diseases to implement joint transnational research projects and set up international cooperations 680969