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Multiscale Modeling of Diseases: Overview

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Handbook of Materials Modeling

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Abstract

Some of the most recent advances in multiscale modeling of human diseases are briefly summarized. These progresses benefited from the rapid development of advanced multiscale modeling techniques as well as the development and availability of new mechanobiology experimental techniques and diverse imaging modalities. The close interdisciplinary collaborations between modeling scientists, experimentalists, and medical doctors enabled unprecedented quantitative understanding of human pathologies. Two specific examples are given on the recent advances in multiscale modeling of malaria – an infectious disease and sickle cell disease – a genetic disease. Future outlook is presented, followed by a brief introduction of the chapters included in this section.

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Acknowledgments

The authors acknowledge funding support from NIH Grant U01HL114476.

Author information

Authors and Affiliations

  1. Division of Applied Mathematics, Brown University, Providence, RI, USA

    George E. Karniadakis

  2. Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Ming Dao

Authors
  1. George E. Karniadakis
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  2. Ming Dao
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Corresponding author

Correspondence to George E. Karniadakis .

Editor information

Editors and Affiliations

  1. Institute of Physics, Swiss Federal Institute of Technology – Lausanne, Lausanne, Switzerland

    Wanda Andreoni

  2. Department of Nuclear Science and Engineering, Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sidney Yip

Section Editor information

  1. Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Ming Dao

  2. Division of Applied Mathematics, Brown University, Providence, RI, USA

    George E Karniadakis

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Karniadakis, G.E., Dao, M. (2020). Multiscale Modeling of Diseases: Overview. In: Andreoni, W., Yip, S. (eds) Handbook of Materials Modeling. Springer, Cham. https://doi.org/10.1007/978-3-319-44680-6_62

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