Computational biorheology of human blood flow in health and disease.
Computational biorheology of human blood flow in health and disease.
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DOI：
10.1007/s10439-013-0922-3
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发表时间：
2014-02
影响因子：
3.8
通讯作者：
Suresh, Subra
中科院分区：
文献类型：
--
作者：
Fedosov, Dmitry A.;Dao, Ming;Karniadakis, George Em;Suresh, Subra
由传染病、遗传因素和环境影响引起的血液病可导致红细胞（rbc）的形状、机械和物理特性以及血流的生物流变学发生重大变化，并受其影响。因此，血液学疾病的建模应该考虑到血液流动的多相性质，特别是在小动脉和毛细血管中。我们在此概述了基于耗散粒子动力学（DPD）的一般计算框架，该框架在细胞生物物理学中具有广泛的适用性，对各种人类疾病的诊断、治疗和药物疗效评估具有重要意义。这种计算方法经过独立实验结果的验证，能够模拟血液流动过程中全血及其单个成分的生物流变学，从而研究健康和疾病中的细胞机制过程。DPD是一种拉格朗日方法，可以从分子动力学的系统粗粒化中得到，但可以有效地扩展到小动脉，也可以用来模拟红细胞到光谱水平。我们从单个红细胞的实验测量开始提取相关的生物物理参数，使用单细胞测量方法，包括光学镊子，原子力显微镜和微移液管抽吸，以及涉及微流体装置的细胞群体实验。然后，我们使用这些验证的红细胞模型来预测健康或病理状态下全血的生物流变学行为，并将模拟结果与实验结果进行比较，包括表观粘度和其他相关参数。虽然这里讨论的方法足以解决包括某些类型的癌症在内的广泛的血液系统疾病，但本文专门处理使用该计算框架获得的结果，用于疟疾和镰状细胞性贫血的血流。
Hematologic disorders arising from infectious diseases, hereditary factors and environmental influences can lead to, and can be influenced by, significant changes in the shape, mechanical and physical properties of red blood cells (RBCs), and the biorheology of blood flow. Hence, modeling of hematologic disorders should take into account the multiphase nature of blood flow, especially in arterioles and capillaries. We present here an overview of a general computational framework based on dissipative particle dynamics (DPD) which has broad applicability in cell biophysics with implications for diagnostics, therapeutics and drug efficacy assessments for a wide variety of human diseases. This computational approach, validated by independent experimental results, is capable of modeling the biorheology of whole blood and its individual components during blood flow so as to investigate cell mechanistic processes in health and disease. DPD is a Lagrangian method that can be derived from systematic coarse-graining of molecular dynamics but can scale efficiently up to arterioles and can also be used to model RBCs down to the spectrin level. We start from experimental measurements of a single RBC to extract the relevant biophysical parameters, using single-cell measurements involving such methods as optical tweezers, atomic force microscopy and micropipette aspiration, and cell-population experiments involving microfluidic devices. We then use these validated RBC models to predict the biorheological behavior of whole blood in healthy or pathological states, and compare the simulations with experimental results involving apparent viscosity and other relevant parameters. While the approach discussed here is sufficiently general to address a broad spectrum of hematologic disorders including certain types of cancer, this paper specifically deals with results obtained using this computational framework for blood flow in malaria and sickle cell anemia.
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