skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences

Abstract

Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunityin this regard. However, classical machine learning techniques often ignore the fundamental laws of physics and result in ill-posed problems or non-physical solutions. Multiscale modeling is a successful strategy to integrate multiscale, multiphysics data and uncover mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large data sets from different sources and different levels of resolution. Here we demonstrate that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces. We critically review the current literature, highlight applications and opportunities, address open questions, and discuss potential challenges and limitations in four overarching topical areas: ordinary differential equations, partial differential equations, data-driven approaches, and theory-driven approaches. Towards these goals, we leverage expertisemore » in applied mathematics, computer science, computational biology, biophysics, biomechanics, engineering mechanics, experimentation, and medicine. Our multidisciplinary perspective suggests that integrating machine learning and multiscale modeling can provide new insights into disease mechanisms, help identify new targets and treatment strategies, and inform decision making for the benefit of human health.« less

Authors:
 [1];  [2]; ORCiD logo [3];  [4];  [5];  [6];  [7];  [5];  [8];  [9]
  1. Univ. of California, Riverside, CA (United States)
  2. Purdue Univ., West Lafayette, IN (United States)
  3. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  4. Rensselaer Polytechnic Inst., Troy, NY (United States)
  5. State Univ. of New York (SUNY), Brooklyn, NY (United States). Downstate Medical Center and Kings County Hospital
  6. Univ. of Michigan, Ann Arbor, MI (United States)
  7. Brown Univ., Providence, RI (United States)
  8. Stanford Univ., CA (United States)
  9. Univ. of California, Santa Barbara, CA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1597499
Report Number(s):
PNNL-SA-147139
Journal ID: ISSN 2398-6352
Grant/Contract Number:  
AC05-76RL01830
Resource Type:
Accepted Manuscript
Journal Name:
npj Digital Medicine
Additional Journal Information:
Journal Volume: 2; Journal Issue: 1; Journal ID: ISSN 2398-6352
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Alber, Mark, Buganza Tepole, Adrian, Cannon, William R., De, Suvranu, Dura-Bernal, Salvador, Garikipati, Krishna, Karniadakis, George E., Lytton, William W., Kuhl, Ellen, and Petzold, Linda. Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. United States: N. p., 2019. Web. doi:10.1038/s41746-019-0193-y.
Alber, Mark, Buganza Tepole, Adrian, Cannon, William R., De, Suvranu, Dura-Bernal, Salvador, Garikipati, Krishna, Karniadakis, George E., Lytton, William W., Kuhl, Ellen, & Petzold, Linda. Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. United States. doi:10.1038/s41746-019-0193-y.
Alber, Mark, Buganza Tepole, Adrian, Cannon, William R., De, Suvranu, Dura-Bernal, Salvador, Garikipati, Krishna, Karniadakis, George E., Lytton, William W., Kuhl, Ellen, and Petzold, Linda. Mon . "Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences". United States. doi:10.1038/s41746-019-0193-y. https://www.osti.gov/servlets/purl/1597499.
@article{osti_1597499,
title = {Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences},
author = {Alber, Mark and Buganza Tepole, Adrian and Cannon, William R. and De, Suvranu and Dura-Bernal, Salvador and Garikipati, Krishna and Karniadakis, George E. and Lytton, William W. and Kuhl, Ellen and Petzold, Linda},
abstractNote = {Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunityin this regard. However, classical machine learning techniques often ignore the fundamental laws of physics and result in ill-posed problems or non-physical solutions. Multiscale modeling is a successful strategy to integrate multiscale, multiphysics data and uncover mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large data sets from different sources and different levels of resolution. Here we demonstrate that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces. We critically review the current literature, highlight applications and opportunities, address open questions, and discuss potential challenges and limitations in four overarching topical areas: ordinary differential equations, partial differential equations, data-driven approaches, and theory-driven approaches. Towards these goals, we leverage expertise in applied mathematics, computer science, computational biology, biophysics, biomechanics, engineering mechanics, experimentation, and medicine. Our multidisciplinary perspective suggests that integrating machine learning and multiscale modeling can provide new insights into disease mechanisms, help identify new targets and treatment strategies, and inform decision making for the benefit of human health.},
doi = {10.1038/s41746-019-0193-y},
journal = {npj Digital Medicine},
number = 1,
volume = 2,
place = {United States},
year = {2019},
month = {11}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Save / Share:

Works referenced in this record:

A mathematical model for IL-6-mediated, stem cell driven tumor growth and targeted treatment
journal, January 2018

  • Nazari, Fereshteh; Pearson, Alexander T.; Nör, Jacques Eduardo
  • PLOS Computational Biology, Vol. 14, Issue 1
  • DOI: 10.1371/journal.pcbi.1005920

The Spectrum of Mechanism-Oriented Models and Methods for Explanations of Biological Phenomena
journal, May 2018

  • Hunt, C.; Erdemir, Ahmet; Lytton, William
  • Processes, Vol. 6, Issue 5
  • DOI: 10.3390/pr6050056

Uncertainty quantification of 2 models of cardiac electromechanics: Uncertainty quantification of cardiac electromechanics
journal, July 2017

  • Hurtado, Daniel E.; Castro, Sebastián; Madrid, Pedro
  • International Journal for Numerical Methods in Biomedical Engineering, Vol. 33, Issue 12
  • DOI: 10.1002/cnm.2894

A Dynamic Programming Approach to De Novo Peptide Sequencing via Tandem Mass Spectrometry
journal, June 2001

  • Chen, Ting; Kao, Ming-Yang; Tepel, Matthew
  • Journal of Computational Biology, Vol. 8, Issue 3
  • DOI: 10.1089/10665270152530872

Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
journal, January 2020

  • Kissas, Georgios; Yang, Yibo; Hwuang, Eileen
  • Computer Methods in Applied Mechanics and Engineering, Vol. 358
  • DOI: 10.1016/j.cma.2019.112623

Reinforcement learning in artificial and biological systems
journal, March 2019


Perspectives on biological growth and remodeling
journal, April 2011

  • Ambrosi, D.; Ateshian, G. A.; Arruda, E. M.
  • Journal of the Mechanics and Physics of Solids, Vol. 59, Issue 4
  • DOI: 10.1016/j.jmps.2010.12.011

NetPyNE, a tool for data-driven multiscale modeling of brain circuits
journal, April 2019

  • Dura-Bernal, Salvador; Suter, Benjamin A.; Gleeson, Padraig
  • eLife, Vol. 8
  • DOI: 10.7554/eLife.44494

Integrative, dynamic structural biology at atomic resolution—it's about time
journal, March 2015

  • van den Bedem, Henry; Fraser, James S.
  • Nature Methods, Vol. 12, Issue 4
  • DOI: 10.1038/nmeth.3324

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
journal, February 2019


Variational system identification of the partial differential equations governing the physics of pattern-formation: Inference under varying fidelity and noise
journal, November 2019

  • Wang, Z.; Huan, X.; Garikipati, K.
  • Computer Methods in Applied Mechanics and Engineering, Vol. 356
  • DOI: 10.1016/j.cma.2019.07.007

A physics-based model explains the prion-like features of neurodegeneration in Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis
journal, March 2019

  • Weickenmeier, Johannes; Jucker, Mathias; Goriely, Alain
  • Journal of the Mechanics and Physics of Solids, Vol. 124
  • DOI: 10.1016/j.jmps.2018.10.013

Turbulence Modeling in the Age of Data
journal, January 2019


Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling
journal, February 2017

  • Perdikaris, P.; Raissi, M.; Damianou, A.
  • Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 473, Issue 2198
  • DOI: 10.1098/rspa.2016.0751

Propagation of material behavior uncertainty in a nonlinear finite element model of reconstructive surgery
journal, August 2018

  • Lee, Taeksang; Turin, Sergey Y.; Gosain, Arun K.
  • Biomechanics and Modeling in Mechanobiology, Vol. 17, Issue 6
  • DOI: 10.1007/s10237-018-1061-4

Multiscale characterization of heart failure
journal, March 2019


Adversarial uncertainty quantification in physics-informed neural networks
journal, October 2019


Dose finding for new vaccines: The role for immunostimulation/immunodynamic modelling
journal, March 2019

  • Rhodes, Sophie J.; Knight, Gwenan M.; Kirschner, Denise E.
  • Journal of Theoretical Biology, Vol. 465
  • DOI: 10.1016/j.jtbi.2019.01.017

Metabolic Network Prediction of Drug Side Effects
journal, March 2016


A Novel Cloud-Based Framework for the Elderly Healthcare Services Using Digital Twin
journal, January 2019


Multiscale modeling in the clinic: diseases of the brain and nervous system
journal, May 2017


Deep Learning-Based Numerical Methods for High-Dimensional Parabolic Partial Differential Equations and Backward Stochastic Differential Equations
journal, November 2017

  • E., Weinan; Han, Jiequn; Jentzen, Arnulf
  • Communications in Mathematics and Statistics, Vol. 5, Issue 4
  • DOI: 10.1007/s40304-017-0117-6

Model inversion via multi-fidelity Bayesian optimization: a new paradigm for parameter estimation in haemodynamics, and beyond
journal, May 2016

  • Perdikaris, Paris; Karniadakis, George Em
  • Journal of The Royal Society Interface, Vol. 13, Issue 118
  • DOI: 10.1098/rsif.2015.1107

Solving high-dimensional partial differential equations using deep learning
journal, August 2018

  • Han, Jiequn; Jentzen, Arnulf; E., Weinan
  • Proceedings of the National Academy of Sciences, Vol. 115, Issue 34
  • DOI: 10.1073/pnas.1718942115

Deep learning detects impending organ injury in the clinic
journal, July 2019


Pharmacogenomics‐Driven Prediction of Antidepressant Treatment Outcomes: A Machine‐Learning Approach With Multi‐trial Replication
journal, April 2019

  • Athreya, Arjun P.; Neavin, Drew; Carrillo‐Roa, Tania
  • Clinical Pharmacology & Therapeutics, Vol. 106, Issue 4
  • DOI: 10.1002/cpt.1482

Tissue-scale, personalized modeling and simulation of prostate cancer growth
journal, November 2016

  • Lorenzo, Guillermo; Scott, Michael A.; Tew, Kevin
  • Proceedings of the National Academy of Sciences, Vol. 113, Issue 48
  • DOI: 10.1073/pnas.1615791113

A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data
journal, May 2018


Stochastic isotropic hyperelastic materials: constitutive calibration and model selection
journal, March 2018

  • Mihai, L. Angela; Woolley, Thomas E.; Goriely, Alain
  • Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 474, Issue 2211
  • DOI: 10.1098/rspa.2017.0858

Predicting the effect of aging and defect size on the stress profiles of skin from advancement, rotation and transposition flap surgeries
journal, April 2019

  • Lee, Taeksang; Gosain, Arun K.; Bilionis, Ilias
  • Journal of the Mechanics and Physics of Solids, Vol. 125
  • DOI: 10.1016/j.jmps.2019.01.012

Discovery of Nonlinear Multiscale Systems: Sampling Strategies and Embeddings
journal, January 2019

  • Champion, Kathleen P.; Brunton, Steven L.; Kutz, J. Nathan
  • SIAM Journal on Applied Dynamical Systems, Vol. 18, Issue 1
  • DOI: 10.1137/18M1188227

Digital Twins in Health Care: Ethical Implications of an Emerging Engineering Paradigm
journal, February 2018

  • Bruynseels, Koen; Santoni de Sio, Filippo; van den Hoven, Jeroen
  • Frontiers in Genetics, Vol. 9
  • DOI: 10.3389/fgene.2018.00031

Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics
journal, June 2016

  • Mangan, Niall M.; Brunton, Steven L.; Proctor, Joshua L.
  • IEEE Transactions on Molecular, Biological and Multi-Scale Communications, Vol. 2, Issue 1
  • DOI: 10.1109/TMBMC.2016.2633265

Dynamic causal modelling
journal, August 2003


Large-scale physical activity data reveal worldwide activity inequality
journal, July 2017

  • Althoff, Tim; Sosič, Rok; Hicks, Jennifer L.
  • Nature, Vol. 547, Issue 7663
  • DOI: 10.1038/nature23018

Simulation-assisted machine learning
journal, March 2019


Mechanotransduction and extracellular matrix homeostasis
journal, October 2014

  • Humphrey, Jay D.; Dufresne, Eric R.; Schwartz, Martin A.
  • Nature Reviews Molecular Cell Biology, Vol. 15, Issue 12
  • DOI: 10.1038/nrm3896

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling
journal, January 2018


A deep convolutional neural network for classification of red blood cells in sickle cell anemia
journal, October 2017


Methods of Model Reduction for Large-Scale Biological Systems: A Survey of Current Methods and Trends
journal, June 2017

  • Snowden, Thomas J.; van der Graaf, Piet H.; Tindall, Marcus J.
  • Bulletin of Mathematical Biology, Vol. 79, Issue 7
  • DOI: 10.1007/s11538-017-0277-2

Inverse molecular design using machine learning: Generative models for matter engineering
journal, July 2018


Multi-fidelity classification using Gaussian processes: Accelerating the prediction of large-scale computational models
journal, December 2019

  • Sahli Costabal, Francisco; Perdikaris, Paris; Kuhl, Ellen
  • Computer Methods in Applied Mechanics and Engineering, Vol. 357
  • DOI: 10.1016/j.cma.2019.112602

Computational homogenization of nonlinear elastic materials using neural networks: NEURAL NETWORKS-BASED COMPUTATIONAL HOMOGENIZATION
journal, June 2015

  • Le, B. A.; Yvonnet, J.; He, Q. -C.
  • International Journal for Numerical Methods in Engineering, Vol. 104, Issue 12
  • DOI: 10.1002/nme.4953

Machine learning of linear differential equations using Gaussian processes
journal, November 2017

  • Raissi, Maziar; Perdikaris, Paris; Karniadakis, George Em
  • Journal of Computational Physics, Vol. 348
  • DOI: 10.1016/j.jcp.2017.07.050

Machine learning in drug development: Characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification
journal, May 2019

  • Sahli Costabal, Francisco; Matsuno, Kristen; Yao, Jiang
  • Computer Methods in Applied Mechanics and Engineering, Vol. 348
  • DOI: 10.1016/j.cma.2019.01.033

Hidden physics models: Machine learning of nonlinear partial differential equations
journal, March 2018


Inferring solutions of differential equations using noisy multi-fidelity data
journal, April 2017

  • Raissi, Maziar; Perdikaris, Paris; Karniadakis, George Em
  • Journal of Computational Physics, Vol. 335
  • DOI: 10.1016/j.jcp.2017.01.060

High-performance medicine: the convergence of human and artificial intelligence
journal, January 2019


Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets
journal, August 2018


The Living Heart Project: A robust and integrative simulator for human heart function
journal, November 2014


Sparse identification of nonlinear dynamics for rapid model recovery
journal, June 2018

  • Quade, Markus; Abel, Markus; Nathan Kutz, J.
  • Chaos: An Interdisciplinary Journal of Nonlinear Science, Vol. 28, Issue 6
  • DOI: 10.1063/1.5027470

Bayesian calibration of computer models
journal, August 2001

  • Kennedy, Marc C.; O'Hagan, Anthony
  • Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 63, Issue 3
  • DOI: 10.1111/1467-9868.00294

A Shared Vision for Machine Learning in Neuroscience
journal, January 2018


Neural network based constitutive model for elastomeric foams
journal, July 2008


Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions
journal, August 2019

  • Teichert, G. H.; Natarajan, A. R.; Van der Ven, A.
  • Computer Methods in Applied Mechanics and Engineering, Vol. 353
  • DOI: 10.1016/j.cma.2019.05.019

Discovering governing equations from data by sparse identification of nonlinear dynamical systems
journal, March 2016

  • Brunton, Steven L.; Proctor, Joshua L.; Kutz, J. Nathan
  • Proceedings of the National Academy of Sciences, Vol. 113, Issue 15
  • DOI: 10.1073/pnas.1517384113

Multiphysics and multiscale modelling, data–model fusion and integration of organ physiology in the clinic: ventricular cardiac mechanics
journal, April 2016

  • Chabiniok, Radomir; Wang, Vicky Y.; Hadjicharalambous, Myrianthi
  • Interface Focus, Vol. 6, Issue 2
  • DOI: 10.1098/rsfs.2015.0083

Data-driven discovery of partial differential equations
journal, April 2017

  • Rudy, Samuel H.; Brunton, Steven L.; Proctor, Joshua L.
  • Science Advances, Vol. 3, Issue 4
  • DOI: 10.1126/sciadv.1602614

Using machine learning to characterize heart failure across the scales
journal, June 2019

  • Peirlinck, M.; Sahli Costabal, F.; Sack, K. L.
  • Biomechanics and Modeling in Mechanobiology, Vol. 18, Issue 6
  • DOI: 10.1007/s10237-019-01190-w

An MBSE Approach for Development of Resilient Automated Automotive Systems
journal, January 2019

  • D’Ambrosio, Joseph; Adiththan, Arun; Ordoukhanian, Edwin
  • Systems, Vol. 7, Issue 1
  • DOI: 10.3390/systems7010001

Multiscale Computational Models of Complex Biological Systems
journal, July 2013


Toward an Integration of Deep Learning and Neuroscience
journal, September 2016

  • Marblestone, Adam H.; Wayne, Greg; Kording, Konrad P.
  • Frontiers in Computational Neuroscience, Vol. 10
  • DOI: 10.3389/fncom.2016.00094

A Bayesian approach to selecting hyperelastic constitutive models of soft tissue
journal, July 2015

  • Madireddy, Sandeep; Sista, Bhargava; Vemaganti, Kumar
  • Computer Methods in Applied Mechanics and Engineering, Vol. 291
  • DOI: 10.1016/j.cma.2015.03.012

Multi-scale computational modelling in biology and physiology
journal, January 2008


Multiscale modeling of biomedical, biological, and behavioral systems (Part 1) [Introduction to the special issue
journal, March 2009

  • White, R. J.; Peng, G. C. Y.; Demir, S. S.
  • IEEE Engineering in Medicine and Biology Magazine, Vol. 28, Issue 2
  • DOI: 10.1109/MEMB.2009.932388

Model selection for hybrid dynamical systems via sparse regression
journal, March 2019

  • Mangan, N. M.; Askham, T.; Brunton, S. L.
  • Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 475, Issue 2223
  • DOI: 10.1098/rspa.2018.0534

Active learning of constitutive relation from mesoscopic dynamics for macroscopic modeling of non-Newtonian flows
journal, June 2018


Machine learning materials physics: Surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics
journal, February 2019

  • Teichert, Gregory H.; Garikipati, Krishna
  • Computer Methods in Applied Mechanics and Engineering, Vol. 344
  • DOI: 10.1016/j.cma.2018.10.025