Data-Driven Reduction of a Cardiac Myofilament Model
This manuscript presents a novel, data-driven approach to reduce a detailed cellular model of cardiac myofilament (MF) for efficient and accurate cellular simulations towards cell-to-organ computation. Based on 700 different sarcomere dynamics calculated using Rice model, we show through manifold learning that sarcomere force (SF) dynamics lays surprisingly in a linear manifold despite the non-linear equations of the MF model. Then, we learn a multivariate adaptive regression spline (MARS) model to predict SF from the Rice model parameters and sarcomere length dynamics. Evaluation on 300 testing data showed a prediction error of less than 0.4 nN/mm2 in terms of maximum force amplitude and 0.87 ms in terms of time to force peak, which is comparable to the differences observed with experimental data. Moreover, MARS provided insights on the driving parameters of the model, mainly MF geometry and cell mechanical passive properties. Thus, our approach may not only constitute a fast and accurate alternative to the original Rice model but also provide insights on parameter sensitivity.
KeywordsSarcomere Length Multivariate Adaptive Regression Spline Locally Linear Embedding Linear Manifold Manifold Learning
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