Experimentally calibrated population of models predicts and explains intersubject variability in cardiac cellular electrophysiology

Edited by Eve Marder, Brandeis University, Waltham, MA, and approved April 18, 2013 (received for review March 12, 2013)
May 20, 2013
110 (23) E2098-E2105

Significance

Causes of intersubject variability in electrophysiological activity are unknown. We describe a methodology to unravel the ionic determinants of variability exhibited in experimental cardiac action potential recordings, based on the construction and calibration of populations of models. We show that 213 of 10,000 candidate models are consistent with the control experimental dataset. Ionic properties across the model population cover a wide range of values, and particular combinations of ionic properties determine shape, amplitude, and rate dependence of specific action potentials. Finally, we demonstrate that the calibrated model population quantitatively predicts effects caused by four concentrations of a potassium channel blocker.

Abstract

Cellular and ionic causes of variability in the electrophysiological activity of hearts from individuals of the same species are unknown. However, improved understanding of this variability is key to enable prediction of the response of specific hearts to disease and therapies. Limitations of current mathematical modeling and experimental techniques hamper our ability to provide insight into variability. Here, we describe a methodology to unravel the ionic determinants of intersubject variability exhibited in experimental recordings, based on the construction and calibration of populations of models. We illustrate the methodology through its application to rabbit Purkinje preparations, because of their importance in arrhythmias and safety pharmacology assessment. We consider a set of equations describing the biophysical processes underlying rabbit Purkinje electrophysiology, and we construct a population of over 10,000 models by randomly assigning specific parameter values corresponding to ionic current conductances and kinetics. We calibrate the model population by closely comparing simulation output and experimental recordings at three pacing frequencies. We show that 213 of the 10,000 candidate models are fully consistent with the experimental dataset. Ionic properties in the 213 models cover a wide range of values, including differences up to ±100% in several conductances. Partial correlation analysis shows that particular combinations of ionic properties determine the precise shape, amplitude, and rate dependence of specific action potentials. Finally, we demonstrate that the population of models calibrated using data obtained under physiological conditions quantitatively predicts the action potential duration prolongation caused by exposure to four concentrations of the potassium channel blocker dofetilide.

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Acknowledgments

This study was supported by a Medical Research Council Industry Partnership award and Research Grant from Janssen Pharmaceutica NV. O.J.B. is supported by an Engineering and Physical Sciences Research Council-funded Systems Biology Doctoral Training Centre studentship, B.R. holds a Medical Research Council Career Development Award, and the contribution to this work by A.B.-O. was supported by Award KUK-C1-013-04 from the King Abdullah University of Science and Technology.

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Information & Authors

Information

Published in

Go to Proceedings of the National Academy of Sciences
Go to Proceedings of the National Academy of Sciences
Proceedings of the National Academy of Sciences
Vol. 110 | No. 23
June 4, 2013
PubMed: 23690584

Classifications

Submission history

Published online: May 20, 2013
Published in issue: June 4, 2013

Keywords

  1. cardiac electrophysiology
  2. computational biology
  3. mathematical modeling
  4. systems biology
  5. drug

Acknowledgments

This study was supported by a Medical Research Council Industry Partnership award and Research Grant from Janssen Pharmaceutica NV. O.J.B. is supported by an Engineering and Physical Sciences Research Council-funded Systems Biology Doctoral Training Centre studentship, B.R. holds a Medical Research Council Career Development Award, and the contribution to this work by A.B.-O. was supported by Award KUK-C1-013-04 from the King Abdullah University of Science and Technology.

Notes

This article is a PNAS Direct Submission.

Authors

Affiliations

Oliver J. Britton
Department of Computer Science, University of Oxford, Oxford OX1 3QD, United Kingdom;
Alfonso Bueno-Orovio
Oxford Centre for Collaborative Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX1 3LB, United Kingdom; and
Karel Van Ammel
Translational Sciences, Safety Pharmacology Research, Janssen Research and Development, Janssen Pharmaceutica NV, B-2340 Beerse, Belgium
Hua Rong Lu
Translational Sciences, Safety Pharmacology Research, Janssen Research and Development, Janssen Pharmaceutica NV, B-2340 Beerse, Belgium
Rob Towart
Translational Sciences, Safety Pharmacology Research, Janssen Research and Development, Janssen Pharmaceutica NV, B-2340 Beerse, Belgium
David J. Gallacher
Translational Sciences, Safety Pharmacology Research, Janssen Research and Development, Janssen Pharmaceutica NV, B-2340 Beerse, Belgium
Blanca Rodriguez1 [email protected]
Department of Computer Science, University of Oxford, Oxford OX1 3QD, United Kingdom;

Notes

1
To whom correspondence should be addressed. E-mail: [email protected].
Author contributions: O.J.B., A.B.-O., and B.R. designed research; O.J.B. performed research; O.J.B., K.V.A., H.R.L., R.T., and D.J.G. contributed new reagents/analytic tools; O.J.B., A.B.-O., and B.R. analyzed data; and O.J.B., A.B.-O., and B.R. wrote the paper.

Competing Interests

The authors declare no conflict of interest.

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    Experimentally calibrated population of models predicts and explains intersubject variability in cardiac cellular electrophysiology
    Proceedings of the National Academy of Sciences
    • Vol. 110
    • No. 23
    • pp. 9185-9613

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