This study presents a multiscale computational framework coupling a constrained mixture model, capturing the mechanics and turnover of arterial constituents, to a cell-cell signaling model, describing Notch signaling dynamics among vascular smooth muscle cells. Tissue turnover was regulated by both Notch activity, informed by in vitro data obtained from human coronary artery smooth muscle cells, and a phenomenological contribution, accounting for mechanisms other than Notch. The framework was used to predict changes in wall thickness and arterial composition in response to hypertension and thereby demonstrated the effects of Notch signaling and Notch interventions on this process.
This dataset contains the computational codes for the multiscale framework (i.e. the constrained mixture model and the Notch signaling model), the codes for the data fitting and optimization, and the raw data from the simulations and the in vitro experiments used to inform the model.
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