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Parameter Estimation of Ion Current Formulations Requires Hybrid Optimization Approach to Be Both Accurate and Reliable

Loewe, A.; Wilhelms, M.; Schmid, J.; Krause, M.J.; Fischer, F.; Thomas, D.; Scholz, E.P.; Dössel, O.; Seemann, G.

Computational models of cardiac electrophysiology provided insights into arrhythmogenesis and paved the way toward tailored therapies in the last years. To fully leverage in silico models in future research, these models need to be adapted to reflect pathologies, genetic alterations, or pharmacological effects, however. A common approach is to leave the structure of established models unaltered and estimate the values of a set of parameters. Today’s high-throughput patch clamp data acquisition methods require robust, unsupervised algorithms that estimate parameters both accurately and reliably. In this work, two classes of optimization approaches are evaluated: gradient-based trust-region-reflective and derivative-free particle swarm algorithms. Using synthetic input data and different ion current formulations from the Courtemanche et al. electrophysiological model of human atrial myocytes, we show that neither of the two schemes alone succeeds to meet all requirements. Sequential combination of the two algorithms did improve the performance to some extent but not satisfactorily. Thus, we propose a novel hybrid approach coupling the two algorithms in each iteration. ... mehr

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DOI: 10.5445/IR/1000052534
DOI: 10.3389/fbioe.2015.00209
Zugehörige Institution(en) am KIT Institut für Biomedizinische Technik (IBT)
Publikationstyp Zeitschriftenaufsatz
Jahr 2016
Sprache Englisch
Identifikator ISSN: 2296-4185
KITopen-ID: 1000052534
Erschienen in Frontiers in Bioengineering and Biotechnology
Band 3
Seiten 209/1-13
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
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