Skip to main content
SpringerLink
Log in

A Multiscale Model for Recruitment Aggregation of Platelets by Correlating with In Vitro Results

  • Published:
Cellular and Molecular Bioengineering Aims and scope Submit manuscript

Abstract

Introduction

We developed a multiscale model to simulate the dynamics of platelet aggregation by recruitment of unactivated platelets flowing in viscous shear flows by an activated platelet deposited onto a blood vessel wall. This model uses coarse grained molecular dynamics for platelets at the microscale and dissipative particle dynamics for the shear flow at the macroscale. Under conditions of relatively low shear, aggregation is mediated by fibrinogen via αIIbβ3 receptors.

Methods

The binding of αIIbβ3 and fibrinogen is modeled by a molecular-level hybrid force field consisting of Morse potential and Hooke law for the nonbonded and bonded interactions, respectively. The force field, parametrized in two different interaction scales, is calculated by correlating with the platelet contact area measured in vitro and the detaching force between αIIbβ3 and fibrinogen.

Results

Using our model, we derived, the relationship between recruitment force and distance between the centers of mass of two platelets, by integrating the molecular-scale inter-platelet interactions during recruitment aggregation in shear flows. Our model indicates that assuming a rigid-platelet model, underestimates the contact area by 89% and the detaching force by 93% as compared to a model that takes into account the platelet deformability leading to a prediction of a significantly lower attachment during recruitment.

Conclusions

The molecular-level predictive capability of our model sheds a light on differences observed between transient and permanent platelet aggregation patterns. The model and simulation framework can be further adapted to simulate initial thrombus formation involving multiple flowing platelets as well as deposition and adhesion onto blood vessels.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13

Similar content being viewed by others

References

  1. Benjamin, E. J., S. S. Virani, C. W. Callaway, A. M. Chamberlain, A. R. Chang, S. Cheng, S. E. Chiuve, M. Cushman, F. N. Delling, R. Deo, S. D. de Ferranti, J. F. Ferguson, M. Fornage, C. Gillespie, C. R. Isasi, M. C. Jimenez, L. C. Jordan, S. E. Judd, D. Lackland, J. H. Lichtman, L. Lisabeth, S. Liu, C. T. Longenecker, P. L. Lutsey, J. S. Mackey, D. B. Matchar, K. Matsushita, M. E. Mussolino, K. Nasir, M. O’Flaherty, L. P. Palaniappan, A. Pandey, D. K. Pandey, M. J. Reeves, M. D. Ritchey, C. J. Rodriguez, G. A. Roth, W. D. Rosamond, U. K. A. Sampson, G. M. Satou, S. H. Shah, N. L. Spartano, D. L. Tirschwell, C. W. Tsao, J. H. Voeks, J. Z. Willey, J. T. Wilkins, J. H. Wu, H. M. Alger, S. S. Wong, P. Muntner, and E. American Heart Association Council on, C. Prevention Statistics, and S. Stroke Statistics. Heart disease and stroke statistics-2018 update: a report from the American Heart Association. Circulation. 137:e67–e492, 2018.

    Article  Google Scholar 

  2. Campbell, R. A., M. Aleman, L. D. Gray, M. R. Falvo, and A. S. Wolberg. Flow profoundly influences fibrin network structure: implications for fibrin formation and clot stability in haemostasis. Thromb. Haemost. 104:1281–1284, 2010.

    Article  Google Scholar 

  3. Carvalho, F. A., and N. C. Santos. Atomic force microscopy-based force spectroscopy-biological and biomedical applications. IUBMB Life 64:465–472, 2012.

    Article  Google Scholar 

  4. Espanol, P., and P. Warren. Statistical mechanics of dissipative particle dynamics. Europhys. Lett. 30:191, 1995.

    Article  Google Scholar 

  5. Fedosov, D. A., and G. E. Karniadakis. Triple-decker: interfacing atomistic-mesoscopic-continuum flow regimes. J. Comput. Phys. 228:1157–1171, 2009.

    Article  MathSciNet  MATH  Google Scholar 

  6. Fogelson, A. L. Continuum models of platelet-aggregation: formulation and mechanical-properties. SIAM J. Appl. Math. 52:1089–1110, 1992.

    Article  MathSciNet  MATH  Google Scholar 

  7. Frojmovic, M. M. Flow cytometric analysis of platelet activation and fibrinogen binding. Platelets 7:9–21, 1996.

    Article  Google Scholar 

  8. Gao, C., P. Zhang, G. Marom, Y. F. Deng, and D. Bluestein. Reducing the effects of compressibility in DPD-based blood flow simulations through severe stenotic microchannels. J. Comput. Phys. 335:812–827, 2017.

    Article  MathSciNet  Google Scholar 

  9. Girdhar, G., M. Xenos, Y. Alemu, W. C. Chiu, B. E. Lynch, J. Jesty, S. Einav, M. J. Slepian, and D. Bluestein. Device thrombogenicity emulation: a novel method for optimizing mechanical circulatory support device thromboresistance. PLoS ONE 7:e32463, 2012.

    Article  Google Scholar 

  10. Goldsmith, H. L., F. A. McIntosh, J. Shahin, and M. M. Frojmovic. Time and force dependence of the rupture of glycoprotein IIb–IIIa-fibrinogen bonds between latex spheres. Biophys. J. 78:1195–1206, 2000.

    Article  Google Scholar 

  11. Groot, R. D., and P. B. Warren. Dissipative particle dynamics: bridging the gap between atomistic and mesoscopic simulation. J. Chem. Phys. 107:4423–4435, 1997.

    Article  Google Scholar 

  12. Haga, J. H., A. J. Beaudoin, J. G. White, and J. Strony. Quantification of the passive mechanical properties of the resting platelet. Ann. Biomed. Eng. 26:268–277, 1998.

    Article  Google Scholar 

  13. Huang, P. Y., and J. D. Hellums. Aggregation and disaggregation kinetics of human blood platelets: Part I. Development and validation of a population balance method. Biophys. J. 65:334–343, 1993.

    Article  Google Scholar 

  14. Huang, P. Y., and J. D. Hellums. Aggregation and disaggregation kinetics of human blood platelets: part II. Shear-induced platelet aggregation. Biophys. J. 65:344–353, 1993.

    Article  Google Scholar 

  15. Huang, P. Y., and J. D. Hellums. Aggregation and disaggregation kinetics of human blood platelets: part III. The disaggregation under shear stress of platelet aggregates. Biophys. J. 65:354–361, 1993.

    Article  Google Scholar 

  16. Jackson, S. P. The growing complexity of platelet aggregation. Blood 109:5087–5095, 2007.

    Article  Google Scholar 

  17. Jackson, S. P., W. S. Nesbitt, and E. Westein. Dynamics of platelet thrombus formation. J. Thromb. Haemost. 7(Suppl 1):17–20, 2009.

    Article  Google Scholar 

  18. Lakes, R. Materials science: a broader view of membranes. Nature 414:503–504, 2001.

    Article  Google Scholar 

  19. Leiderman, K., and A. L. Fogelson. Grow with the flow: a spatial-temporal model of platelet deposition and blood coagulation under flow. Math. Med. Biol. 28:47–84, 2011.

    Article  MathSciNet  MATH  Google Scholar 

  20. Liang, X. M., S. J. Han, J. A. Reems, D. Gao, and N. J. Sniadecki. Platelet retraction force measurements using flexible post force sensors. Lab. Chip. 10:991–998, 2010.

    Article  Google Scholar 

  21. Litvinov, R. I., D. H. Farrell, J. W. Weisel, and J. S. Bennett. The platelet integrin alphaIIbbeta3 differentially interacts with fibrin versus fibrinogen. J. Biol. Chem. 291:7858–7867, 2016.

    Article  Google Scholar 

  22. Martinez, E. J., Y. Lanir, and S. Einav. Effects of contact-induced membrane stiffening on platelet adhesion. Biomech. Mod. Mechanobiol. 2:157–167, 2004.

    Article  Google Scholar 

  23. Maxwell, M. J., E. Westein, W. S. Nesbitt, S. Giuliano, S. M. Dopheide, and S. P. Jackson. Identification of a 2-stage platelet aggregation process mediating shear-dependent thrombus formation. Blood 109:566–576, 2007.

    Article  Google Scholar 

  24. Michelson, A. D. Platelets (3rd ed.). Cambridge: Academic Press, 2013.

    Google Scholar 

  25. Mody, N. A., and M. R. King. Three-dimensional simulations of a platelet-shaped spheroid near a wall in shear flow. Phys. Fluids. 17:113302, 2005.

    Article  MATH  Google Scholar 

  26. Mody, N. A., and M. R. King. Platelet adhesive dynamics. Part I: Characterization of platelet hydrodynamic collisions and wall effects. Biophys. J. 95:2539–2555, 2008.

    Article  Google Scholar 

  27. Mody, N. A., and M. R. King. Platelet adhesive dynamics. Part II: high shear-induced transient aggregation via GPIbalpha-vWF-GPIbalpha bridging. Biophys. J. 95:2556–2574, 2008.

    Article  Google Scholar 

  28. Nesbitt, W. S., E. Westein, F. J. Tovar-Lopez, E. Tolouei, A. Mitchell, J. Fu, J. Carberry, A. Fouras, and S. P. Jackson. A shear gradient-dependent platelet aggregation mechanism drives thrombus formation. Nat. Med. 15:665–673, 2009.

    Article  Google Scholar 

  29. Parise, L. V., and D. R. Phillips. Reconstitution of the purified platelet fibrinogen receptor. Fibrinogen binding properties of the glycoprotein IIb–IIIa complex. J. Biol. Chem. 260:10698–10707, 1985.

    Google Scholar 

  30. Pivkin, I. V., P. D. Richardson, and G. Karniadakis. Blood flow velocity effects and role of activation delay time on growth and form of platelet thrombi. Proc. Natl. Acad. Sci. USA 103:17164–17169, 2006.

    Article  Google Scholar 

  31. Pivkin, I., P. Richardson, and G. Karniadakis. Effect of red blood cells on platelet aggregation. IEEE Eng. Med. Biol. Mag. 28:32–37, 2009.

    Article  Google Scholar 

  32. Plimpton, S., A. Thompson, and P. Crozier. LAMMPS Molecular Dynamics Simulator. http://lammps.sandia.gov, 2012.

  33. Pothapragada, S., P. Zhang, J. Sheriff, M. Livelli, M. J. Slepian, Y. Deng, and D. Bluestein. A phenomenological particle-based platelet model for simulating filopodia formation during early activation. Int. J. Numer. Method Biomed. Eng. 31:e02702, 2015.

    Article  Google Scholar 

  34. Qiu, Y., J. Ciciliano, D. R. Myers, R. Tran, and W. A. Lam. Platelets and physics: how platelets “feel” and respond to their mechanical microenvironment. Blood Rev. 29:377–386, 2015.

    Article  Google Scholar 

  35. Ruggeri, Z. M. Von. Willebrand factor, platelets and endothelial cell interactions. J. Thromb. Haemost. 1:1335–1342, 2003.

    Article  Google Scholar 

  36. Ruggeri, Z. M., J. N. Orje, R. Habermann, A. B. Federici, and A. J. Reininger. Activation-independent platelet adhesion and aggregation under elevated shear stress. Blood 108:1903–1910, 2006.

    Article  Google Scholar 

  37. Sheriff, J., J. S. Soares, M. Xenos, J. Jesty, M. J. Slepian, and D. Bluestein. Evaluation of shear-induced platelet activation models under constant and dynamic shear stress loading conditions relevant to devices. Ann. Biomed. Eng. 41:1279–1296, 2013.

    Article  Google Scholar 

  38. Shiozaki, S., S. Takagi, and S. Goto. Prediction of molecular interaction between platelet glycoprotein Ibalpha and von Willebrand factor using molecular dynamics simulations. J Atheroscler. Thromb. 23:455–464, 2016.

    Article  Google Scholar 

  39. Smyth, S. S., and L. V. Parise. Regulation of ligand binding to glycoprotein IIb–IIIa (integrin alpha IIb beta 3) in isolated platelet membranes. Biochem. J. 292(Pt 3):749–758, 1993.

    Article  Google Scholar 

  40. Soares, J. S., C. Gao, Y. Alemu, M. Slepian, and D. Bluestein. Simulation of platelets suspension flowing through a stenosis model using a dissipative particle dynamics approach. Ann. Biomed. Eng. 41:2318–2333, 2013.

    Article  Google Scholar 

  41. Tosenberger, A., F. Ataullakhanov, N. Bessonov, M. Panteleev, A. Tokarev, and V. Volpert. The role of platelets in blood coagulation during thrombus formation in flow. HAL. 2012.

  42. Tosenberger, A., F. Ataullakhanov, N. Bessonov, M. Panteleev, A. Tokarev, and V. Volpert. Modelling of thrombus growth and growth stop in flow by the method of dissipative particle dynamics. Russ. J. Numer. Anal. Math. 27:507–522, 2012.

    MathSciNet  MATH  Google Scholar 

  43. Weisel, J. W., C. Nagaswami, G. Vilaire, and J. S. Bennett. Examination of the platelet membrane glycoprotein IIb–IIIa complex and its interaction with fibrinogen and other ligands by electron microscopy. J. Biol. Chem. 267:16637–16643, 1992.

    Google Scholar 

  44. Willemsen, S. M., H. C. J. Hoefsloot, and P. D. Iedema. No-slip boundary condition in dissipative particle dynamics. Int. J. Mod. Phys. C 11:881–890, 2000.

    Google Scholar 

  45. Yazdani, A., H. Li, J. D. Humphrey, and G. E. Karniadakis. A general shear-dependent model for thrombus formation. PLoS Comput. Biol. 13:e1005291, 2017.

    Article  Google Scholar 

  46. Zhang, P., C. Gao, N. Zhang, M. J. Slepian, Y. F. Deng, and D. Bluestein. Multiscale particle-based modeling of flowing platelets in blood plasma using dissipative particle dynamics and coarse grained molecular dynamics. Cell. Mol. Bioeng. 7:552–574, 2014.

    Article  Google Scholar 

  47. Zhang, J., P. C. Johnson, and A. S. Popel. Red blood cell aggregation and dissociation in shear flows simulated by lattice Boltzmann method. J. Biomech. 41:47–55, 2008.

    Article  Google Scholar 

  48. Zhang, P., L. Zhang, M. J. Slepian, Y. Deng, and D. Bluestein. A multiscale biomechanical model of platelets: correlating with in vitro results. J. Biomech. 50:26–33, 2017.

    Article  Google Scholar 

Download references

Acknowledgements

This publication was made possible by grants from the National NIH U01 HL131052-014 (PI: Danny Bluestein, Co-Investigators: Yuefan Deng, Marvin J. Slepian). The simulations in this study used the XSEDE computing resource award DMS150011 at the SDSC Comet supercomputer (PI: Peng Zhang) and LI-Red supercomputer, Stony Brook University.

Conflict of interest

Prachi Gupta, Peng Zhang, Jawaad Sheriff, Danny Bluestein and Yuefan Deng declare that they have no conflict of interest.

Human Studies

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000 (5). Informed consent was obtained from all patients for being included in the study. No animal studies were carried out by the authors for this article.

Author information

Authors and Affiliations

  1. Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, 11794-3600, USA

    Prachi Gupta & Yuefan Deng

  2. Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA

    Peng Zhang, Jawaad Sheriff & Danny Bluestein

Authors
  1. Prachi Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jawaad Sheriff
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Danny Bluestein
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuefan Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuefan Deng.

Additional information

Associate Editor Michael R. King oversaw the review of this article.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gupta, P., Zhang, P., Sheriff, J. et al. A Multiscale Model for Recruitment Aggregation of Platelets by Correlating with In Vitro Results. Cel. Mol. Bioeng. 12, 327–343 (2019). https://doi.org/10.1007/s12195-019-00583-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12195-019-00583-2

Keywords

Search

Navigation