Computer models have become more and more a research tool to obtain mechanistic insight in the effects of dyssynchrony and heart failure. Increasing computational power in combination with increasing amounts of experimental and clinical data enables the development of mathematical models that describe electrical and mechanical behavior of the heart. By combining models based on data at the molecular and cellular level with models that describe organ function, so-called multi-scale models are created that describe heart function at different length and time scales. In this review, we describe basic modules that can be identified in multi-scale models of cardiac electromechanics. These modules simulate ionic membrane currents, calcium handling, excitation–contraction coupling, action potential propagation, and cardiac mechanics and hemodynamics. In addition, we discuss adaptive modeling approaches that aim to address long-term effects of diseases and therapy on growth, changes in fiber orientation, ionic membrane currents, and calcium handling. Finally, we discuss the first developments in patient-specific modeling. While current models still have shortcomings, well-chosen applications show promising results on some ultimate goals: understanding mechanisms of dyssynchronous heart failure and tuning pacing strategy to a particular patient, even before starting the therapy.

The cytoskeleton is a dynamic scaffold in living cells even in the absence of externally imposed forces. In this study on cytoskeletal deformation, the applicability of hierarchical feature vector matching (HFVM), a new matching method, currently applied in space research and three-dimensional surface reconstruction, was investigated. Stably transfected CHO-K1 cells expressing green fluorescent protein (GFP) coupled to vimentin were used to visualize spontaneous movement of the vimentin cytoskeleton of individual cells using a confocal laser scanning system. We showed that, with proper parameter and configuration settings, HFVM could recognize and trace 60-70% of all image points in artificially translated, rotated, or deformed images. If only points belonging to the cytoskeleton were selected for matching purposes, the percentage of matched points increased to 98%. This high percentage of recognition also could be reached in a time series of images, in which a certain degree of bleaching of the fluorescence over the recording time of 30 min was inevitable. In these images, HFVM allowed the detection as well as the quantification of spontaneous cytoskeletal movements of up to 10% of the cell width. Therefore, HFVM appears to be a reliable method of quantifying dynamic cytoskeletal behavior in living cells.

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