(185u) Reverse Engineering the Human Platelet: A Computational Framework for Predicting Platelet Activation

Platelets respond to endothelial injury or activating agonists through a complex sequence of signaling mechanisms. To understand how these events are coordinated in time and space, we developed a computational model of the human platelet by combining existing kinetic information, electrochemical calculations, measurements of platelet ultrastructure, novel experimental results, and published single-cell data. The model accurately predicted (i) steady-state concentrations for intracellular calcium, inositol 1,4,5-trisphosphate, diacylglycerol, phosphatidic acid, phosphatidylinositol, phosphatidylinositol phosphate, and phosphatidylinositol 4,5-bisphosphate, (ii) transient increases in intracellular calcium, inositol 1,4,5-trisphosphate, and Gq∙GTP in response to ADP, and (iii) the volume of the platelet dense tubular system. A more stringent test of the model involved stochastic simulation of individual platelets, which are known to display an asynchronous calcium spiking behavior in response to ADP. The model reproduced the measured frequency distribution of spiking events and demonstrated that asynchronous spiking was a consequence of stochastic fluctuations due to the small volume of the platelet. These simulations offer new insight into platelet enzymatic regulation, receptor signaling, and the physical structure of the cell. As such, the model provides a novel approach for elucidating normal platelet signaling behavior as well as designing pharmacological strategies for platelet therapy.