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.

Carusi A, Burrage K, Rodríguez B. Bridging experiments, models and simulations: an integrative approach to validation in computational cardiac electrophysiology. Am J Physiol Heart Circ Physiol 303: H144 -H155, 2012. First published May 11, 2012; doi:10.1152/ajpheart.01151.2011.-Computational models in physiology often integrate functional and structural information from a large range of spatiotemporal scales from the ionic to the whole organ level. Their sophistication raises both expectations and skepticism concerning how computational methods can improve our understanding of living organisms and also how they can reduce, replace, and refine animal experiments. A fundamental requirement to fulfill these expectations and achieve the full potential of computational physiology is a clear understanding of what models represent and how they can be validated. The present study aims at informing strategies for validation by elucidating the complex interrelations among experiments, models, and simulations in cardiac electrophysiology. We describe the processes, data, and knowledge involved in the construction of whole ventricular multiscale models of cardiac electrophysiology. Our analysis reveals that models, simulations, and experiments are intertwined, in an assemblage that is a system itself, namely the model-simulation-experiment (MSE) system. We argue that validation is part of the whole MSE system and is contingent upon 1) understanding and coping with sources of biovariability; 2) testing and developing robust techniques and tools as a prerequisite to conducting physiological investigations; 3) defining and adopting standards to facilitate the interoperability of experiments, models, and simulations; 4) and understanding physiological validation as an iterative process that contributes to defining the specific aspects of cardiac electrophysiology the MSE system targets, rather than being only an external test, and that this is driven by advances in experimental and computational methods and the combination of both. computational physiology; systems biology; computer simulations; whole ventricular models; cardiac electrophysiology COMPUTATIONAL PHYSIOLOGY belongs to the broad family of research in the life sciences referred to as systems biology. As with other domains of systems biology, it considers biological processes as systems of interacting components, and draws upon mathematical and computational modeling to bring these into new configurations with experimentation. It shares the basic commitments of systems biology to nonreductionist or integrative principles, geared towards the exploration of emergence and nonlinear interactions among components and between levels (12,16,41,49,53,71,88). From a sociological point of view, it is also a mode of research that depends on a high degree of interdisciplinary collaboration, where the increased sophistication of computational modeling in the life sciences can elicit high expectations but also skepticism sometimes, thus making collaboration more difficult (19,20,...

HighlightsThe AOP framework aims to increase efficiency of chemical safety assessments.The stakeholder community for AOPs, however, is broader than chemical risk assessors.There are scientific and social challenges to successfully engage all stakeholders.Multi-faceted communication and governance strategies will address these challenges.

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