Evidence map›Paper›PMID 29842853›Full record

ArticleProgress in biophysics and molecular biology2018

Reproducible model development in the cardiac electrophysiology Web Lab.

Aidan C Daly, Michael Clerx, Kylie A Beattie, Jonathan Cooper, David J Gavaghan, Gary R Mirams

Abstract read
In one paragraph

Article in Progress in biophysics and molecular biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Analysing and simulating energy-based models in biology using BondGraphTools.The European physical journal. E, Soft matter · 2021
    Article
  6. Article
  7. Article
  8. Calibration of ionic and cellular cardiac electrophysiology models.Wiley interdisciplinary reviews. Systems biology and medicine · 2020
    Review
  9. An audit of uncertainty in multi-scale cardiac electrophysiology models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020
    Review
  10. Considering discrepancy when calibrating a mechanistic electrophysiology model.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020
    Article
  11. Article
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Aidan C DalyComputational Biology, Department of Computer Science, University of Oxford, Oxford, UK.
Michael ClerxComputational Biology, Department of Computer Science, University of Oxford, Oxford, UK.
Kylie A BeattieComputational Biology, Department of Computer Science, University of Oxford, Oxford, UK.
Jonathan CooperResearch IT Services, University College London, London, UK.
David J GavaghanComputational Biology, Department of Computer Science, University of Oxford, Oxford, UK. Electronic address: david.gavaghan@cs.ox.ac.uk.
Gary R MiramsCentre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK. Electronic address: gary.mirams@nottingham.ac.uk.

Funding

Biotechnology and Biological Sciences Research Council BB/P010008/1Wellcome TrustWellcome Trust 101222/Z/13/Z
6 · The paper itself

Abstract

The modelling of the electrophysiology of cardiac cells is one of the most mature areas of systems biology. This extended concentration of research effort brings with it new challenges, foremost among which is that of choosing which of these models is most suitable for addressing a particular scientific question. In a previous paper, we presented our initial work in developing an online resource for the characterisation and comparison of electrophysiological cell models in a wide range of experimental scenarios. In that work, we described how we had developed a novel protocol language that allowed us to separate the details of the mathematical model (the majority of cardiac cell models take the form of ordinary differential equations) from the experimental protocol being simulated. We developed a fully-open online repository (which we termed the Cardiac Electrophysiology Web Lab) which allows users to store and compare the results of applying the same experimental protocol to competing models. In the current paper we describe the most recent and planned extensions of this work, focused on supporting the process of model building from experimental data. We outline the necessary work to develop a machine-readable language to describe the process of inferring parameters from wet lab datasets, and illustrate our approach through a detailed example of fitting a model of the hERG channel using experimental data. We conclude by discussing the future challenges in making further progress in this domain towards our goal of facilitating a fully reproducible approach to the development of cardiac cell models.

Indexed as

Electrophysiological PhenomenaInternetModels, CardiovascularHeartUser-Computer Interface

Identifiers

PMID29842853
PMCPMC6288479

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.