Regression Analysis for Constraining Free Parameters in Electrophysiological Models of Ventricular Cells

Abstract

hormones on QT interval in males versus females, we constructed ''male'' and ''female'' cell models using Faber-Rudy model of the guinea pig myocyte. The female model incorporated physiological concentrations of 17b-estradiol and progesterone measured in the follicular and luteal phases of the menstrual cycle, and predicts changes in APD at different stages of the menstrual cycle that are consistent with clinically observed QT interval fluctuations. The male model was developed to reflect changes induced by physiology concentrations of Testosterone. The models suggest protective effects of testosterone and progesterone to prevent APD prolongation and reduce QT interval, while estrogen significantly increase QT and susceptibility to drug-induced arrhythmias.

Variability and compensation of cardiomycoyte ionic conductances at the population level

“…In the present study, we have proposed a new methodology for searching for combinations of electrophysiological parameters representing different individuals in a genetically diverse population. While previous studies have used primarily AP features to constrain parameters Sarkar and Sobie (2009, 2010 ); Weiss et al (2012); Sarkar et al (2012); Britton et al (2013); Groenendaal et al (2015); Muszkiewicz et al (2016); Krogh-Madsen et al (2016) , we have chosen to constrain parameters using the Ca 2+ transient that plays a key role to regulate ion channel expression and activity. We have also examined the effect of additionally constraining the intracellular Na + concentration that is also known to modulate ion channel activity.…”

“…Those studies and related ones in the broader context of systems biology Daniels et al (2008); Gutenkunst et al (2007); Transtrum et al (2015) and cardiac electrophysiology Sarkar and So-bie (2009, 2010); Weiss et al (2012); Sarkar et al (2012); Britton et al (2013); Groenendaal et al (2015); Muszkiewicz et al (2016); Krogh-Madsen et al (2016) have produced a paradigm shift from population-averaged models, with unique fine-tuned parameter sets, to populations of models characterized by multiple parameter sets. In this new paradigm, each set representing a different individual in a population can produce a similar behavior under controlled conditions, but a starkly different response to perturbations for some individuals.…”

“…The results of this search, which has been carried out using various methods (e.g. random search Prinz et al (2004) , multivariate regression analysis Sarkar and Sobie (2009, 2010 ), or genetic algorithms Groenendaal et al (2015) ), depend critically on what outputs are selected to constrain model parameters. To date, parameter searches in a cardiac context have been “AP centric”, focusing primarily on features of the membrane voltage (V m ) signal.…”

“…To date, parameter searches in a cardiac context have been “AP centric”, focusing primarily on features of the membrane voltage (V m ) signal. Sarkar and Sobie showed that very different combinations of ion conductances can produce almost identical cardiac AP waveforms, albeit different CaT amplitudes, and that adding additional constraints on the V m and Ca 2+ signals can further constrain model parameters Sarkar and Sobie (2009, 2010 ). Groenendaal et al .…”

“…All those GES searches have relied in part or entirely on using measured AP features Sarkar and Sobie (2009, 2010); Sarkar et al (2012); Britton et al (2013); Groenendaal et al (2015); Muszkiewicz et al (2016); Krogh-Madsen et al (2016) to constrain model parameters. However, given that there is no known voltage sensing mechanisms regulating ion channel expression, it is unclear if natural biological variability can be predicted based on AP features.…”