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Resonant model—A new paradigm for modeling an action potential of biological cells

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  • Sucheta Sehgal
  • Nitish D Patel
  • Avinash Malik
  • Partha S Roop
  • Mark L Trew

Abstract

Organ level simulation of bioelectric behavior in the body benefits from flexible and efficient models of cellular membrane potential. These computational organ and cell models can be used to study the impact of pharmaceutical drugs, test hypotheses, assess risk and for closed-loop validation of medical devices. To move closer to the real-time requirements of this modeling a new flexible Fourier based general membrane potential model, called as a Resonant model, is developed that is computationally inexpensive. The new model accurately reproduces non-linear potential morphologies for a variety of cell types. Specifically, the method is used to model human and rabbit sinoatrial node, human ventricular myocyte and squid giant axon electrophysiology. The Resonant models are validated with experimental data and with other published models. Dynamic changes in biological conditions are modeled with changing model coefficients and this approach enables ionic channel alterations to be captured. The Resonant model is used to simulate entrainment between competing sinoatrial node cells. These models can be easily implemented in low-cost digital hardware and an alternative, resource-efficient implementations of sine and cosine functions are presented and it is shown that a Fourier term is produced with two additions and a binary shift.

Suggested Citation

  • Sucheta Sehgal & Nitish D Patel & Avinash Malik & Partha S Roop & Mark L Trew, 2019. "Resonant model—A new paradigm for modeling an action potential of biological cells," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-25, May.
  • Handle: RePEc:plo:pone00:0216999
    DOI: 10.1371/journal.pone.0216999
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    References listed on IDEAS

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    1. Meron Gurkiewicz & Alon Korngreen, 2007. "A Numerical Approach to Ion Channel Modelling Using Whole-Cell Voltage-Clamp Recordings and a Genetic Algorithm," PLOS Computational Biology, Public Library of Science, vol. 3(8), pages 1-15, August.
    2. Willemijn Groenendaal & Francis A Ortega & Armen R Kherlopian & Andrew C Zygmunt & Trine Krogh-Madsen & David J Christini, 2015. "Cell-Specific Cardiac Electrophysiology Models," PLOS Computational Biology, Public Library of Science, vol. 11(4), pages 1-22, April.
    3. Daniel M Lombardo & Flavio H Fenton & Sanjiv M Narayan & Wouter-Jan Rappel, 2016. "Comparison of Detailed and Simplified Models of Human Atrial Myocytes to Recapitulate Patient Specific Properties," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-15, August.
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