(101g) Reverse Engineering the Human Platelet

Authors: 
Purvis, J. E., University of Pennsylvania
Chatterjee, M., University of Pennsylvania
Brass, L. F., University of Pennsylvania
Diamond, S. L., University of Pennsylvania


Computational modeling has enabled systematic analysis of complex biological signaling networks and can lead to the generation of testable hypotheses. Here, we report for the first time the integration of kinetic data, electrochemical calculations, multiple cellular compartments, population versus single-cell behavior, and modular organization of cell function to construct a high-resolution model of platelet homeostasis and activation signaling. The model has distinct modules for calcium release and uptake, phosphoinositide (PI) metabolism, P2Y1 G-protein signaling, and protein kinase C (PKC) regulation of phospholipase C-&[beta] (PLC-&[beta]). The full deterministic model accurately predicted (i) steady-state concentrations for intracellular calcium (Ca2+i), inositol 1,4,5-trisphosphate (IP3), diacylglycerol (DAG), phosphatidic acid (PA), phosphatidylinositol (PtdIns), phosphatidylinositol phosphate (PIP), and phosphatidylinositol 4,5-bisphosphate (PIP2), (ii) transient increases in Ca2+i, IP3, and Gq&[alpha]&[middot]GTP in response to ADP, and (iii) the volume of the platelet dense tubular system (DTS). A more stringent test of the model involved stochastic simulation of individual platelets which display an asynchronous calcium spiking behavior in response to ADP. The model reproduced the measured frequency distribution of spiking events and demonstrated that the asynchronous spiking was in part a consequence of stochastic fluctuations due to the small volume of the platelet. Given the role of platelets in mediating thrombosis and hemostasis, as well as their contribution to systemic disorders such as inflammation and cancer, the model provides a quantitative framework for predicting platelet behavior and designing patient-specific therapies.