Fedosov Dmitry A, Dao Ming, Karniadakis George Em, Suresh Subra
Institute of Complex Systems and Institute for Advanced Simulation, Forschungszentrum Jülich, 52425, Jülich, Germany.
Ann Biomed Eng. 2014 Feb;42(2):368-87. doi: 10.1007/s10439-013-0922-3. Epub 2013 Oct 12.
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.
由传染病、遗传因素和环境影响引起的血液系统疾病可导致红细胞（RBC）的形状、机械和物理性质发生显著变化，并受其影响，同时也会影响血流的生物流变学。因此，血液系统疾病的建模应考虑血流的多相性质，尤其是在小动脉和毛细血管中。我们在此概述一种基于耗散粒子动力学（DPD）的通用计算框架，该框架在细胞生物物理学中具有广泛的适用性，对多种人类疾病的诊断、治疗和药物疗效评估具有重要意义。这种计算方法已通过独立实验结果验证，能够对血流过程中全血及其各个成分的生物流变学进行建模，从而研究健康和疾病状态下的细胞机制过程。DPD是一种拉格朗日方法，可从分子动力学的系统粗粒化推导而来，但能高效地扩展到小动脉尺度，还可用于对红细胞进行建模，直至血影蛋白水平。我们从单个红细胞的实验测量开始，使用诸如光镊、原子力显微镜和微吸管抽吸等方法进行单细胞测量，以及涉及微流控装置的细胞群体实验，以提取相关的生物物理参数。然后，我们使用这些经过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，并将模拟结果与涉及表观粘度和其他相关参数的实验结果进行比较。虽然这里讨论的方法具有足够的通用性，可用于解决包括某些类型癌症在内的广泛血液系统疾病，但本文具体讨论使用该计算框架获得的疟疾和镰状细胞贫血血流的结果。
