Monte Carlo simulation of the heterotypic aggregation kinetics of platelets and neutrophils.
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 (. 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 rate G = 335 s(-1). At plasma concentrations, the half-lives of homotypically aggregating platelet and neutrophil singlets were 8.5 and 2.4 s, respectively. However, for heterotypic aggregation, the half-lives for platelets and neutrophils decreased to 2.0 and 0.11 s, 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 of Omega(1/2), where Omega 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.
Laurenzi
I J
IJ
Institute for Medicine and Engineering, Department of Chemical Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104 USA.
Diamond
S L
SL
D001792:Blood Platelets / Q000502:physiology*; D002448:Cell Adhesion; D002449:Cell Aggregation / Q000502:physiology; D003198:Computer Simulation; D006801:Humans; D007700:Kinetics; D008954:Models, Biological; D009010:Monte Carlo Method; D009504:Neutrophils / Q000502:physiology*; D010973:Platelet Adhesiveness; D010974:Platelet Aggregation / Q000502:physiology*; D011336:Probability; D013269:Stochastic Processes