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A stochastic Molecular Dynamics method for multiscale modeling of blood platelet phenomena

A stochastic Molecular Dynamics method for multiscale modeling of blood platelet phenomena. PIs : G.E. Karniadakis, P.D. Richardson, M.R. Maxey Collaborators : Harvard Medical School, Imperial College, Ben Gurion. Arterioles/venules 50 microns. activated platelets.

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A stochastic Molecular Dynamics method for multiscale modeling of blood platelet phenomena

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  1. A stochastic Molecular Dynamics method for multiscale modeling of blood platelet phenomena • PIs: G.E. Karniadakis, P.D. Richardson, M.R. Maxey • Collaborators: Harvard Medical School, Imperial College, Ben Gurion • Arterioles/venules 50 microns activated platelets • Platelet diameter is 2-4 µm • Normal platelet concentration in blood is 300,000/mm3 • Functions: activation, adhesion to injured walls, and other platelets • Multiscale Simulation of Arterial Tree on TeraGrid

  2. Interaction with activated platelet, injured vessel wall Activation delay time, chosen randomly between 0.1 and 0.2 s ACTIVATED adhesive PASSIVE non-adhesive TRIGGERED non-adhesive If not adhered after 5 s - passive - triggered - activated Platelet Aggregation RBCs are treated as a continuum Pivkin, Richardson & Karniadakis, PNAS, 103 (46), 2006

  3. Small Aggregates, no RBCs The increase in volume of platelet aggregate is plotted semi-logarithmically against time. Effect of mean blood flow velocity on the growth rate constant (s-1) of platelet aggregate. Simulation results qualitatively agree with experimental data from Begent and Born Begent and Born, Nature, Vol. 227, No. 5261, pp. 926-930, 1970

  4. - passive - triggered - activated Platelet Aggregation and RBCs Model RBCs as rigid spheres of large diameter

  5. Small aggregates, RBCs The increase in volume of platelet aggregate is plotted semi-logarithmically against time. Effect of mean blood flow velocity on the growth rate constant (s-1) of platelet aggregate. Black curves – platelets only Red curves – platelets in the presence of large spheres

  6. Increase of aggregate growthfor large aggregates Black curves – platelets only Red curves – platelets in the presence of large spheres

  7. DPD MD Dissipative Particle Dynamics (DPD) • Dissipative Particle Dynamics (DPD) was introduced by Hoogerbrugge and Koelman in 1992 • Particles interact through a simple pair-wise potential • The DPD scheme consists of the calculation of the position and velocities of interacting particles over time. The time evolution of positions and velocities are given by: P.J. Hoogerbrugge and J.M.V.A. Koelman, Europhys.Lett.,19:155-160, 1992

  8. DPD MD Conservative Force From Forrest and Sutter, 1995 Soft potentials were obtained by averaging the molecular field over the rapidly fluctuating motions of atoms during short time intervals. This approach leads to an effective potential similar to one, used in DPD.

  9. F1random F1dissipative V1 V2 F2dissipative F2random Dissipative and Random Forces • Dissipative (friction) force is reducing the relative velocity of the pair of particles • Random force compensate for eliminated degrees of freedom • Dissipative and random forces form DPD thermostat • The magnitude of dissipative and random forces are defined by • fluctuation dissipation theorem

  10. Solid Objects in DPD Solid objects are modeled by collections of DPD particles.

  11. Deformable RBCs Membrane model: J. Li et al., Biophys.J, 88 (2005) bending and in-plane energies, constraints on surface area and volume • Coarse RBC model: • 500 DPD particles connected by links • Average length of the link is about 500 nm • RBCs are immersed into the DPD fluid • The RBC particles interact with fluid particles through DPD potentials • Temperature is controlled using DPD thermostat

  12. Biconcave and Cup Shapes Deflating sphere to 65% of volume

  13. Shear

  14. Microchannel Experiment by Stefano Guido, Università di Napoli Federico II

  15. RBCs

  16. Open Issues • Identify effective parameters for coarse RBC membrane • Investigate dimensional consistency in DPD • Validation with experiments, optical tweezers(MIT) or microchannel flow(MIT/Italy) • Stress-free constraint

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