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Enabling AI-Accelerated Multiscale Modeling of Thrombogenesis at Millisecond and Molecular Resolutions on Supercomputers

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High Performance Computing (ISC High Performance 2021)

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Abstract

We report the first congruent integration of HPC, AI, and multiscale modeling (MSM) for solving a mainstream biomechanical problem of thrombogenesis involving 6 million particles at record molecular-scale resolutions in space and at simulation rates of milliseconds per day. The two supercomputers, the IBM Summit-like AiMOS and our University’s SeaWulf, are used for scalability analysis of, and production runs with, the LAMMPS with our customization and AI augmentation and they attained optimal simulation speeds of 3,077 µs/day and 266 µs/day respectively. The long-time and large scales simulations enable the first study of the integrated platelet flowing, flipping, aggregating dynamics in one dynamically-coupled production run. The platelets’ angular and translational speeds, membrane particles’ speeds, and the membrane stress distributions are presented for the analysis of platelets’ aggregations.

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References

  1. Hodak, H.: The nobel prize in chemistry 2013 for the development of multiscale models of complex chemical systems: a tribute to Martin Karplus, Michael Levitt and Arieh Warshel. J. Mol. Biol. 426(1), 1–3 (2014). https://doi.org/10.1016/j.jmb.2013.10.037. ISSN 0022-2836

  2. Alber, M., et al.: Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. NPJ. Digit. Med. 2, 1–11 (2019)

    Article  Google Scholar 

  3. Virani, S.S., et al.: Heart disease and stroke statistics—2020 update: a report from the American Heart Association. Circulation E139-E596 (2020)

    Google Scholar 

  4. Bluestein, D., Yin, W., Affeld, K., Jesty, J.: Flow-induced platelet activation in a mechanical heart valve. J. Heart Valve Dis. 13, 501–508 (2004)

    Google Scholar 

  5. Poor, H.D., et al.: COVID‐19 critical illness pathophysiology driven by diffuse pulmonary thrombi and pulmonary endothelial dysfunction responsive to thrombolysis. Clin. Transl. Med. 10, e44 (2020)

    Google Scholar 

  6. Rapkiewicz, A.V., et al.: Megakaryocytes and platelet-fibrin thrombi characterize multi-organ thrombosis at autopsy in COVID-19: a case series. EClinicalMedicine 24, 100434 (2020)

    Article  Google Scholar 

  7. Wang, W., King, M.R.: Multiscale modeling of platelet adhesion and thrombus growth. Ann. Biomed. Eng. 40, 2345–2354 (2012)

    Article  Google Scholar 

  8. Zhang, P., Gao, C., Zhang, N., Slepian, M.J., Deng, Y., Bluestein, D.: 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 

  9. Han, C., Zhang, P., Bluestein, D., Cong, G., Deng, Y.: Artificial intelligence for accelerating time integrations in multiscale modeling. J. Comput. Phys. 427, 110053 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  10. Dror, R.O., Dirks, R.M., Grossman, J., Xu, H., Shaw, D.E.: Biomolecular simulation: a computational microscope for molecular biology. Annu. Rev. Biophys. 41, 429–452 (2012)

    Article  Google Scholar 

  11. Shaw, D.E., et al.: Anton, a special-purpose machine for molecular dynamics simulation. Commun. ACM 51, 91–97 (2008)

    Article  Google Scholar 

  12. Shaw, D.E., et al.: Anton 2: raising the bar for performance and programmability in a special-purpose molecular dynamics supercomputer. In: SC 2014: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 41–53 (2014)

    Google Scholar 

  13. Yang, C., et al.: Fully integrated FPGA molecular dynamics simulations. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1–31 (2019)

    Google Scholar 

  14. Zhang, T.: SW_GROMACS: accelerate GROMACS on sunway TaihuLight. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1–14 (2019)

    Google Scholar 

  15. Jia, W., et al.: Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1–14 (2020)

    Google Scholar 

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

    Article  Google Scholar 

  17. Fogelson, A.L., Guy, R.D.: Immersed-boundary-type models of intravascular platelet aggregation. Comput. Methods Appl. Mech. Eng. 197, 2087–2104 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  18. Sweet, C.R., Chatterjee, S., Xu, Z., Bisordi, K., Rosen, E.D., Alber, M.: Modelling platelet–blood flow interaction using the subcellular element Langevin method. J. R. Soc. Interface 8, 1760–1771 (2011)

    Article  Google Scholar 

  19. Grinberg, L., et al.: A new computational paradigm in multiscale simulations: application to brain blood flow. In: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1–5 (2011)

    Google Scholar 

  20. Wu, Z., Xu, Z., Kim, O., Alber, M.: Three-dimensional multi-scale model of deformable platelets adhesion to vessel wall in blood flow. Philos. Trans. Royal Soc. A Math. Phys. Eng. Sci. 372, 20130380 (2014)

    Article  MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  23. Shiozaki, S., Takagi, S., Goto, S.: Prediction of molecular interaction between platelet glycoprotein Ibα and von Willebrand factor using molecular dynamics simulations. J. Atheroscl. Thrombosis 32458 (2015)

    Google Scholar 

  24. Zhang, P., Zhang, L., Slepian, M.J., Deng, Y., Bluestein, D.: A multiscale biomechanical model of platelets: Correlating with in-vitro results. J. Biomech. 50, 26–33 (2017)

    Article  Google Scholar 

  25. Gupta, P., Zhang, P., Sheriff, J., Bluestein, D., Deng, Y.: A multiscale model for recruitment aggregation of platelets by correlating with in vitro results. Cell. Mol. Bioeng. 12, 327–343 (2019)

    Article  Google Scholar 

  26. Zhang, P., Zhang, N., Deng, Y., Bluestein, D.: A multiple time stepping algorithm for efficient multiscale modeling of platelets flowing in blood plasma. J. Comput. Phys. 284, 668–686 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  27. Han, C., Zhang, P., Deng, Y.: AI-guided adaptive multiscale modeling of platelet dynamics. In: ACM Student Research Competition Poster of the International Conference for High Performance Computing, Networking, Storage and Analysis (2020)

    Google Scholar 

  28. Hanson, W.A.: The CORAL supercomputer systems. IBM J. Res. Dev. 64, 1:1–1:10 (2019)

    Google Scholar 

  29. Sheriff, J., Bluestein, D.: Platelet dynamics in blood flow. In: Dynamics of Blood Cell Suspensions in Microflows, pp. 215–256. CRC Press (2019)

    Google Scholar 

  30. Slepian, M.J., et al.: Shear-mediated platelet activation in the free flow: perspectives on the emerging spectrum of cell mechanobiological mechanisms mediating cardiovascular implant thrombosis. J. Biomech. 50, 20–25 (2017)

    Article  Google Scholar 

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Acknowledgement

The project is supported by the SUNY-IBM Consortium Award, IPDyna: Intelligent Platelet Dynamics, FP00004096 (PI: Y. Deng, Co-PI: P. Zhang). The simulations were conducted on the AiMOS at Rensselaer Polytechnic Institute and the SeaWulf at Stony Brook University.

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Correspondence to Yuefan Deng .

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Zhu, Y., Zhang, P., Han, C., Cong, G., Deng, Y. (2021). Enabling AI-Accelerated Multiscale Modeling of Thrombogenesis at Millisecond and Molecular Resolutions on Supercomputers. In: Chamberlain, B.L., Varbanescu, AL., Ltaief, H., Luszczek, P. (eds) High Performance Computing. ISC High Performance 2021. Lecture Notes in Computer Science(), vol 12728. Springer, Cham. https://doi.org/10.1007/978-3-030-78713-4_13

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  • DOI: https://doi.org/10.1007/978-3-030-78713-4_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78712-7

  • Online ISBN: 978-3-030-78713-4

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