Online Machine Learning for Accelerating Molecular Dynamics Modeling of Cells

Zhang, Ziji; Zhang, Peng; Han, Changnian; Cong, Guojing; Yang, Chih-Chieh; Deng, YueFan

doi:10.3389/fmolb.2021.812248

Title: Online Machine Learning for Accelerating Molecular Dynamics Modeling of Cells

Journal Article · 27 January 2022 · Frontiers in Molecular Biosciences

DOI: https://doi.org/10.3389/fmolb.2021.812248 · OSTI ID:1860548

Zhang, Ziji ^[1]; Zhang, Peng ^[1]; Han, Changnian ^[1]; Cong, Guojing ^[2]; Yang, Chih-Chieh ^[3]; Deng, YueFan ^[4]

Stony Brook Univ., NY (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
IBM Thomas J. Watson Research Center, Yorktown Heights, NY (United States)
Stony Brook Univ., NY (United States); New York Univ., Abu Dhabi (United Arab Emirates)

We developed a biomechanics-informed online learning framework to learn the dynamics with ground truth generated with multiscale modeling simulation. It was built on Summit-like supercomputers, which were also used to benchmark and validate our framework on one physiologically significant modeling of deformable biological cells. We generalized the century-old equation of Jeffery orbits to a new equation of motion with additional parameters to account for the flow conditions and the cell deformability. Using simulation data at particle-based resolutions for flowing cells and the learned parameters from our framework, we validated the new equation by the motions, mostly rotations, of a human platelet in shear blood flow at various shear stresses and platelet deformability. Our online framework, which surrogates redundant computations in the conventional multiscale modeling by solutions of our learned equation, accelerates the conventional modeling by three orders of magnitude without visible loss of accuracy.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC); SUNY-IBM Consortium; IPDyna: Intelligent Platelet Dynamics

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1860548

Journal Information:: Frontiers in Molecular Biosciences, Journal Name: Frontiers in Molecular Biosciences Journal Issue: n/a Vol. 8; ISSN 2296-889X

Publisher:: Frontiers Media S.A.Copyright Statement

Country of Publication:: United States

Language:: English

References (37)

On the rheology of particle suspensions in viscoelastic fluids Shaqfeh, Eric S. G. AIChE Journal, Vol. 65, Issue 5 https://doi.org/10.1002/aic.16575	journal	February 2019
Berichtigung zu meiner Arbeit: „Eine neue Bestimmung der Moleküldimensionen” Einstein, A. Annalen der Physik, Vol. 339, Issue 3 https://doi.org/10.1002/andp.19113390313	journal	January 1911
Potential role of platelets in COVID‐19: Implications for thrombosis Koupenova, Milka Research and Practice in Thrombosis and Haemostasis, Vol. 4, Issue 5 https://doi.org/10.1002/rth2.12397	journal	June 2020
Fast Parallel Algorithms for Short-Range Molecular Dynamics Plimpton, Steve Journal of Computational Physics, Vol. 117, Issue 1 https://doi.org/10.1006/jcph.1995.1039	journal	March 1995
Multiscale Modeling Meets Machine Learning: What Can We Learn? Peng, Grace C. Y.; Alber, Mark; Buganza Tepole, Adrian Archives of Computational Methods in Engineering https://doi.org/10.1007/s11831-020-09405-5	journal	February 2020
Multiscale Particle-Based Modeling of Flowing Platelets in Blood Plasma Using Dissipative Particle Dynamics and Coarse Grained Molecular Dynamics Zhang, Peng; Gao, Chao; Zhang, Na Cellular and Molecular Bioengineering, Vol. 7, Issue 4 https://doi.org/10.1007/s12195-014-0356-5	journal	September 2014
A Multiscale Model for Recruitment Aggregation of Platelets by Correlating with In Vitro Results Gupta, Prachi; Zhang, Peng; Sheriff, Jawaad Cellular and Molecular Bioengineering, Vol. 12, Issue 4 https://doi.org/10.1007/s12195-019-00583-2	journal	July 2019
Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions Teichert, G. H.; Natarajan, A. R.; Van der Ven, A. Computer Methods in Applied Mechanics and Engineering, Vol. 353 https://doi.org/10.1016/j.cma.2019.05.019	journal	August 2019
Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks Kissas, Georgios; Yang, Yibo; Hwuang, Eileen Computer Methods in Applied Mechanics and Engineering, Vol. 358 https://doi.org/10.1016/j.cma.2019.112623	journal	January 2020
A multiscale biomechanical model of platelets: Correlating with in-vitro results Zhang, Peng; Zhang, Li; Slepian, Marvin J. Journal of Biomechanics, Vol. 50 https://doi.org/10.1016/j.jbiomech.2016.11.019	journal	January 2017
Parameterizing the Morse potential for coarse-grained modeling of blood plasma Zhang, Na; Zhang, Peng; Kang, Wei Journal of Computational Physics, Vol. 257 https://doi.org/10.1016/j.jcp.2013.09.040	journal	January 2014
A multiple time stepping algorithm for efficient multiscale modeling of platelets flowing in blood plasma Zhang, Peng; Zhang, Na; Deng, Yuefan Journal of Computational Physics, Vol. 284 https://doi.org/10.1016/j.jcp.2015.01.004	journal	March 2015
An evaluation of noise reduction algorithms for particle-based fluid simulations in multi-scale applications Zimoń, M. J.; Prosser, R.; Emerson, D. R. Journal of Computational Physics, Vol. 325 https://doi.org/10.1016/j.jcp.2016.08.021	journal	November 2016
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations Raissi, M.; Perdikaris, P.; Karniadakis, G. E. Journal of Computational Physics, Vol. 378 https://doi.org/10.1016/j.jcp.2018.10.045	journal	February 2019
Artificial intelligence for accelerating time integrations in multiscale modeling Han, Changnian; Zhang, Peng; Bluestein, Danny Journal of Computational Physics, Vol. 427 https://doi.org/10.1016/j.jcp.2020.110053	journal	February 2021
Role of high shear rate in thrombosis Casa, Lauren D. C.; Deaton, David H.; Ku, David N. Journal of Vascular Surgery, Vol. 61, Issue 4 https://doi.org/10.1016/j.jvs.2014.12.050	journal	April 2015
Kernel-based online machine learning and support vector reduction Agarwal, Sumeet; Vijaya Saradhi, V.; Karnick, Harish Neurocomputing, Vol. 71, Issue 7-9 https://doi.org/10.1016/j.neucom.2007.11.023	journal	March 2008
Rotational and orientational behaviour of three-dimensional spheroidal particles in Couette flows Qi, Dewei; Luo, Li-Shi Journal of Fluid Mechanics, Vol. 477 https://doi.org/10.1017/s0022112002003191	journal	February 2003
Hydromechanics of low-Reynolds-number flow. Part 1. Rotation of axisymmetric prolate bodies Chwang, Allen T.; Wu, T. Yao-Tsu Journal of Fluid Mechanics, Vol. 63, Issue 3 https://doi.org/10.1017/s0022112074001819	journal	April 1974
Fast machine-learning online optimization of ultra-cold-atom experiments Wigley, P. B.; Everitt, P. J.; van den Hengel, A. Scientific Reports, Vol. 6, Issue 1 https://doi.org/10.1038/srep25890	journal	May 2016
Shape-dependence of particle rotation in isotropic turbulence Byron, M.; Einarsson, J.; Gustavsson, K. Physics of Fluids, Vol. 27, Issue 3 https://doi.org/10.1063/1.4913501	journal	March 2015
Rotation of a spheroid in a simple shear at small Reynolds number Einarsson, J.; Candelier, F.; Lundell, F. Physics of Fluids, Vol. 27, Issue 6 https://doi.org/10.1063/1.4921543	journal	June 2015
Tumbling of asymmetric microrods in a microchannel flow Einarsson, J.; Mihiretie, B. M.; Laas, A. Physics of Fluids, Vol. 28, Issue 1 https://doi.org/10.1063/1.4938239	journal	January 2016
Simulation-assisted machine learning Deist, Timo M.; Patti, Andrew; Wang, Zhaoqi Bioinformatics, Vol. 35, Issue 20 https://doi.org/10.1093/bioinformatics/btz199	journal	March 2019
Ideal spatial adaptation by wavelet shrinkage Donoho, David L.; Johnstone, Iain M. Biometrika, Vol. 81, Issue 3 https://doi.org/10.1093/biomet/81.3.425	journal	September 1994
Predictive modelling of thrombus formation in diabetic retinal microaneurysms Li, He; Sampani, Konstantina; Zheng, Xiaoning Royal Society Open Science, Vol. 7, Issue 8 https://doi.org/10.1098/rsos.201102	journal	August 2020
Effect of weak fluid inertia upon Jeffery orbits Einarsson, J.; Candelier, F.; Lundell, F. Physical Review E, Vol. 91, Issue 4 https://doi.org/10.1103/PhysRevE.91.041002	journal	April 2015
Erratum: Role of inertia for the rotation of a nearly spherical particle in a general linear flow [Phys. Rev. E 91 , 053023 (2015)] Candelier, F.; Einarsson, J.; Lundell, F. Physical Review E, Vol. 92, Issue 5 https://doi.org/10.1103/PhysRevE.92.059901	journal	November 2015
Numerical analysis of the angular motion of a neutrally buoyant spheroid in shear flow at small Reynolds numbers Rosén, T.; Einarsson, J.; Nordmark, A. Physical Review E, Vol. 92, Issue 6 https://doi.org/10.1103/PhysRevE.92.063022	journal	December 2015
De-noising by soft-thresholding Donoho, D. L. IEEE Transactions on Information Theory, Vol. 41, Issue 3 https://doi.org/10.1109/18.382009	journal	May 1995
Pushing the Limit of Molecular Dynamics with Ab Initio Accuracy to 100 Million Atoms with Machine Learning Jia, Weile; Wang, Han; Chen, Mohan SC20: International Conference for High Performance Computing, Networking, Storage and Analysis https://doi.org/10.1109/SC41405.2020.00009	conference	November 2020
Online Diagnosis of Performance Variation in HPC Systems Using Machine Learning Tuncer, Ozan; Ates, Emre; Zhang, Yijia IEEE Transactions on Parallel and Distributed Systems, Vol. 30, Issue 4 https://doi.org/10.1109/TPDS.2018.2870403	journal	April 2019
Multiscale Modeling of Flow Induced Thrombogenicity With Dissipative Particle Dynamics and Molecular Dynamics Bluestein, Danny; Soares, João S.; Zhang, Peng Journal of Medical Devices, Vol. 7, Issue 4 https://doi.org/10.1115/1.4027347	journal	December 2013
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations Raissi, Maziar; Yazdani, Alireza; Karniadakis, George Em Science, Vol. 367, Issue 6481 https://doi.org/10.1126/science.aaw4741	journal	January 2020
Heart Disease and Stroke Statistics—2019 Update: A Report From the American Heart Association Benjamin, Emelia J.; Muntner, Paul; Alonso, Alvaro Circulation, Vol. 139, Issue 10 https://doi.org/10.1161/CIR.0000000000000659	journal	March 2019
Systems biology informed deep learning for inferring parameters and hidden dynamics Yazdani, Alireza; Lu, Lu; Raissi, Maziar PLOS Computational Biology, Vol. 16, Issue 11 https://doi.org/10.1371/journal.pcbi.1007575	journal	November 2020
Pathophysiology of Thrombosis in Peripheral Artery Disease Habib, Aida; Petrucci, Giovanna; Rocca, Bianca Current Vascular Pharmacology, Vol. 18, Issue 3 https://doi.org/10.2174/1570161117666190206234046	journal	April 2020

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