Department of Biomedical Engineering, University of Western Ontario, London, Ontario, Canada.
Department of Physics, University of Guelph, Guelph, Ontario, Canada.
J Appl Physiol (1985). 2024 Jun 1;136(6):1303-1321. doi: 10.1152/japplphysiol.00896.2023. Epub 2024 Apr 11.
Blood flow regulation within the microvasculature reflects a complex interaction of regulatory mechanisms and varies spatially and temporally according to conditions such as metabolism, growth, injury, and disease. Understanding the role of microvascular flow distributions across conditions is of interest to investigators spanning multiple disciplines; however, data collection within networks can be labor-intensive and challenging due to limited resolution. To overcome these experimental challenges, computational network models that can accurately simulate vascular behavior are highly beneficial. Constrained constructive optimization (CCO) is a commonly used algorithm for vascular simulation, particularly well known for its adaptability toward vascular modeling across tissues. The present work demonstrates an implementation of CCO aimed to simulate a branching arteriolar microvasculature in healthy skeletal muscle, validated against literature including comprehensive rat gluteus maximus vasculature datasets, and reviews a list of user-specified adjustable model parameters to understand how their variability affects the simulated networks. Network geometric properties, including mean element diameters, lengths, and numbers of bifurcations per order, Horton's law ratios, and fractal dimension, demonstrate good validation once model parameters are adjusted to experimental data. This model successfully demonstrates hemodynamic properties such as Murray's law and the network Fahraeus effect. Application of centrifugal and Strahler ordering schemes results in divergent descriptions of identical simulated networks. This work introduces a novel CCO-based model focused on generating branching skeletal muscle microvascular arteriolar networks based on adjustable model parameters, thus making it a valuable tool for investigations into skeletal muscle microvascular structure and tissue perfusion. The present work introduces a CCO-based algorithm for generating branching arteriolar networks, with adjustable model parameters to enable modeling in varying skeletal muscle tissues. The geometric and hemodynamic parameters of the generated networks have been comprehensively validated using experimental data collected previously in-house and from literature. This is one of few validated CCO-based models to specialize in skeletal muscle microvasculature and acts as a beneficial tool for investigating the microvasculature for hypothesis testing and validation.
微血管内的血流调节反映了调节机制的复杂相互作用，并且根据代谢、生长、损伤和疾病等条件在空间和时间上有所不同。了解跨条件的微血管流量分布的作用对于跨越多个学科的研究人员都很有意义；然而，由于分辨率有限，网络内的数据收集可能非常费力和具有挑战性。为了克服这些实验挑战，能够准确模拟血管行为的计算网络模型非常有益。约束构造优化 (CCO) 是一种常用于血管模拟的算法，特别是因其在跨组织的血管建模方面的适应性而广为人知。本工作展示了一种旨在模拟健康骨骼肌分支动脉微脉管系统的 CCO 实现，该实现针对包括全面的大鼠臀大肌血管数据集在内的文献进行了验证，并回顾了一系列可调节的模型参数，以了解它们的变化如何影响模拟网络。网络几何特性，包括平均元素直径、长度和每个阶的分叉数、Horton 定律比和分形维数，在调整模型参数以适应实验数据后，验证效果良好。该模型成功地演示了血流动力学特性，如 Murray 定律和网络 Fahraeus 效应。离心和 Strahler 排序方案的应用导致对相同模拟网络的描述不一致。本工作引入了一种基于 CCO 的新模型，该模型专注于基于可调节模型参数生成分支骨骼肌微血管动脉网络，从而使其成为研究骨骼肌微血管结构和组织灌注的有价值的工具。本工作介绍了一种基于 CCO 的生成分支动脉网络的算法，具有可调节的模型参数，以实现不同骨骼肌组织的建模。使用以前在内部和文献中收集的实验数据对生成网络的几何和血流动力学参数进行了全面验证。这是少数专门研究骨骼肌微血管的经过验证的 CCO 模型之一，是用于研究微血管进行假设检验和验证的有益工具。
