Regression methods for parameter sensitivity analysis: applications to cardiac arrhythmia mechanisms.

Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, New York, NY, USA.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:4657-60. DOI: 10.1109/IEMBS.2011.6091153
Source: PubMed

ABSTRACT Mathematical models are used extensively in studies of cardiac electrophysiology and arrhythmia mechanisms. Models can generate novel predictions, suggest experiments, and provide a quantitative understanding of underlying mechanisms. Limitations of present modeling approaches, however, include non-uniqueness of both parameters and the models themselves, and difficulties in accounting for experimental variability. We describe new approaches that can begin to address these limitations, and show how these can provide novel insight into mathematical models of cardiac myocytes.

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    • "In this study, we investigate the ability of populations of human atrial AP models based on three recently published models to capture the inter-subject variability in human atrial AP, as exhibited in recordings from over 350 atrial trabeculae from sinus rhythm (SR) and chronic AF (cAF) patients. The human atria models within each population (based on one of the three models) share the same equations but include different combinations of sampled ionic current conductance values, as previously described [8], [9], [11], [13], [19]. The experimentally-calibrated human atrial AP model populations are then used to quantify the contribution of specific ionic currents to determining inter-subject variability in cellular human atrial AP duration (APD) and morphology in the populations, and to determine potential differences between the populations based on SR and cAF patients’ recordings. "
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    ABSTRACT: Aims Human atrial electrophysiology exhibits high inter-subject variability in both sinus rhythm (SR) and chronic atrial fibrillation (cAF) patients. Variability is however rarely investigated in experimental and theoretical electrophysiological studies, thus hampering the understanding of its underlying causes but also its implications in explaining differences in the response to disease and treatment. In our study, we aim at investigating the ability of populations of human atrial cell models to capture the inter-subject variability in action potential (AP) recorded in 363 patients both under SR and cAF conditions. Methods and Results Human AP recordings in atrial trabeculae (n = 469) from SR and cAF patients were used to calibrate populations of computational SR and cAF atrial AP models. Three populations of over 2000 sampled models were generated, based on three different human atrial AP models. Experimental calibration selected populations of AP models yielding AP with morphology and duration in range with experimental recordings. Populations using the three original models can mimic variability in experimental AP in both SR and cAF, with median conductance values in SR for most ionic currents deviating less than 30% from their original peak values. All cAF populations show similar variations in GK1, GKur and Gto, consistent with AF-related remodeling as reported in experiments. In all SR and cAF model populations, inter-subject variability in IK1 and INaK underlies variability in APD90, variability in IKur, ICaL and INaK modulates variability in APD50 and combined variability in Ito and IKur determines variability in APD20. The large variability in human atrial AP triangulation is mostly determined by IK1 and either INaK or INaCa depending on the model. Conclusion Experimentally-calibrated human atrial AP models populations mimic AP variability in SR and cAF patient recordings, and identify potential ionic determinants of inter-subject variability in human atrial AP duration and morphology in SR versus cAF.
    PLoS ONE 08/2014; 9(8-8):e105897. DOI:10.1371/journal.pone.0105897 · 3.23 Impact Factor