Monte Carlo simulation of the heterotypic aggregation kinetics of platelets and neutrophils.
Monte Carlo simulation of the heterotypic aggregation kinetics of platelets and neutrophils.
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DOI：
10.1016/s0006-3495(99)77019-0
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发表时间：
1999-09
影响因子：
3.4
通讯作者：
I. Laurenzi;S. Diamond
中科院分区：
文献类型：
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作者：
I. Laurenzi;S. Diamond
细胞混合物或胶体颗粒（如蛋白质）的异型聚集发生在各种环境中，如血栓形成、免疫学、细胞分离和诊断学。利用种群平衡方程组预测动态团聚体粒径和组成分布是不可行的。用于化学反应的吉莱斯皮的随机算法（吉莱斯皮，1976. Phys.22：403-434）重新制定以模拟聚集系统的动力学行为。由此产生的蒙特卡罗（MC）算法允许精确计算的衰减率的单体和时间演变的分布的大小和组成的聚集体。此外，它允许计算这些分布的所有时刻。使用这种方法，我们探索了完全活化的血小板和中性粒细胞在剪切速率G = 335 s-1的线性剪切流中的异型聚集。在血浆浓度下，同型聚集的血小板和中性粒细胞的半衰期分别为8.5和2.4秒。然而，对于异型聚集，血小板和中性粒细胞的半衰期分别降低至2.0和0.11 s，表明流动的中性粒细胞加速血小板捕获和聚集体生长。MC算法每个时间步长所需的计算次数通常是Ω1/2的一小部分，其中Ω是系统中粒子的初始数量，使其成为可用的最快MC方法。该算法的速度使得一般生物异型聚集过程的核反卷积成为可能。
The heterotypic aggregation of cell mixtures or colloidal particles such as proteins occurs in a variety of settings such as thrombosis, immunology, cell separations, and diagnostics. Using the set of population balance equations (PBEs) to predict dynamic aggregate size and composition distributions is not feasible. The stochastic algorithm of Gillespie for chemical reactions (Gillespie, 1976.J. Comput. Phys.22:403–434) was reformulated to simulate the kinetic behavior of aggregating systems. The resulting Monte Carlo (MC) algorithm permits exact calculation of the decay rates of monomers and the temporally evolving distribution of sizes and compositions of the aggregates. Moreover, it permits calculation of all moments of these distributions. Using this method, we explored the heterotypic aggregation of fully activated platelets and neutrophils in a linear shear flow of shear rateG=335s−1. At plasma concentrations, the half-lives of homotypically aggregating platelet and neutrophil singlets were 8.5 and 2.4s, respectively. However, for heterotypic aggregation, the half-lives for platelets and neutrophils decreased to 2.0 and 0.11s, respectively, demonstrating that flowing neutrophils accelerate capture of platelets and growth of aggregates. The required number of calculations per time step of the MC algorithm was typically a small fraction ofΩ1/2,where Ω is the initial number of particles in the system, making this the fastest MC method available. The speed of the algorithm makes feasible the deconvolution of kernels for general biological heterotypic aggregation processes.