Data belonging to the publication "A multiscale computational model of arterial growth and remodeling including Notch signaling"

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. <br>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.

本研究提出了一种多尺度计算框架，该框架将用于表征动脉组织成分力学行为与更新代谢过程的约束混合模型（constrained mixture model），与描述血管平滑肌细胞间Notch信号（Notch signaling）动态变化的细胞间信号传导模型相耦合。组织更新过程同时受Notch活性与现象学贡献共同调控：其中Notch活性以人冠状动脉平滑肌细胞的体外实验数据为依据确定，而现象学贡献则涵盖了Notch信号通路以外的其他调控机制。该框架被用于预测高血压状态下动脉壁厚与动脉组织成分的变化，由此阐明了Notch信号通路及Notch干预手段对该过程的影响。
本数据集包含该多尺度框架的计算代码（即约束混合模型与Notch信号传导模型代码）、数据拟合与优化代码，以及用于模型校准的仿真实验与体外实验的原始数据。