Data belonging to the publication "Predicted effects of patient variability and Notch signaling on in situ vascular tissue engineering"

This study adopts a previously published multiscale computational framework for arterial growth and remodeling and adapts it for applications in in situ vascular tissue engineering. The framework consists of a constrained mixture model, capturing the mechanics and turnover of arterial constituents, and a cell-cell signaling model, describing Notch signaling dynamics among vascular smooth muscle cells. With the model, we computationally explored potential sources of tissue engineered vascular graft (TEVG) variability and effects of manipulating Notch, a key vascular signaling pathway. We simulated the evolution of a TEVG from a degradable scaffold under varying patient-specific conditions.<br>This dataset contains the computational codes for the multiscale framework and the raw data from the simulations.

本研究采用已发表的动脉生长与重塑多尺度计算框架，并将其适配至原位血管组织工程的应用场景。该框架由约束混合模型（constrained mixture model）与细胞间信号传导模型构成：前者可刻画动脉组成成分的力学特性与代谢更新过程，后者用于描述血管平滑肌细胞间的Notch信号传导动态。借助该模型，本研究通过计算模拟探究了组织工程血管移植物（TEVG）变异性的潜在来源，以及调控关键血管信号通路Notch所产生的影响。我们针对不同患者特异性条件，模拟了可降解支架演进为组织工程血管移植物的全过程。
本数据集包含该多尺度框架的计算代码，以及模拟所得的原始数据。