Considering discrepancy when calibrating a mechanistic electrophysiology model

Chon Lok Lei*, Sanmitra Ghosh, Dominic G. Whittaker, Yasser Aboelkassem, Kylie A. Beattie, Chris D. Cantwell, Tammo Delhaas, Charles Houston, Gustavo Montes Novaes, Alexander V. Panfilov, Pras Pathmanathan, Marina Riabiz, Rodrigo Weber dos Santos, John Walmsley, Keith Worden, Gary R. Mirams, Richard D. Wilkinson

*Corresponding author for this work

Research output: Contribution to journal(Systematic) Review article peer-review


Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterize uncertainty in model inputs and how that propagates through to outputs or predictions; examples of this can be seen in the papers of this issue. In this review and perspective piece, we draw attention to an important and under-addressed source of uncertainty in our predictions-that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here, we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes and autoregressive-moving-average models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided.

This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

Original languageEnglish
Article number20190349
Number of pages23
JournalPhilosophical Transactions of the Royal Society A: mathematical Physical and Engineering Sciences
Issue number2173
Publication statusPublished - 12 Jun 2020


  • Bayesian inference
  • cardiac model
  • model discrepancy
  • uncertainty quantification


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