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Variability in cardiac electrophysiology: Using experimentally-calibrated populations of models to move beyond the single virtual physiological human paradigm.

Abstract:
Physiological variability manifests itself via differences in physiological function between individuals of the same species, and has crucial implications in disease progression and treatment. Despite its importance, physiological variability has traditionally been ignored in experimental and computational investigations due to averaging over samples from multiple individuals. Recently, modelling frameworks have been devised for studying mechanisms underlying physiological variability in cardiac electrophysiology and pro-arrhythmic risk under a variety of conditions and for several animal species as well as human. One such methodology exploits populations of cardiac cell models constrained with experimental data, or experimentally-calibrated populations of models. In this review, we outline the considerations behind constructing an experimentally-calibrated population of models and review the studies that have employed this approach to investigate variability in cardiac electrophysiology in physiological and pathological conditions, as well as under drug action. We also describe the methodology and compare it with alternative approaches for studying variability in cardiac electrophysiology, including cell-specific modelling approaches, sensitivity-analysis based methods, and populations-of-models frameworks that do not consider the experimental calibration step. We conclude with an outlook for the future, predicting the potential of new methodologies for patient-specific modelling extending beyond the single virtual physiological human paradigm.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.pbiomolbio.2015.12.002

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author


Publisher:
Elsevier
Journal:
Progress in biophysics and molecular biology More from this journal
Publication date:
2015-12-14
Acceptance date:
2015-12-02
DOI:
EISSN:
1873-1732
ISSN:
0079-6107


Language:
English
Keywords:
Pubs id:
pubs:587121
UUID:
uuid:aeab84ac-001d-440e-a91a-2c253cbcb232
Local pid:
pubs:587121
Source identifiers:
587121
Deposit date:
2016-02-22

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