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@ARTICLE{Fedosov:154031,
      author       = {Fedosov, Dmitry and Dao, M. and Karniadakis, G. E. and
                      Suresh, S.},
      title        = {{C}omputational biorheology of human blood flow in health
                      and disease},
      journal      = {Annals of biomedical engineering},
      volume       = {42},
      number       = {2},
      issn         = {0191-5649},
      address      = {Dordrecht [u.a.]},
      publisher    = {Springer Science + Business Media B.V},
      reportid     = {FZJ-2014-03445},
      pages        = {368-387},
      year         = {2014},
      abstract     = {Hematologic disorders arising from infectious diseases,
                      hereditary factors and environmental influences can lead to,
                      and can be influenced by, significant changes in the shape,
                      mechanical and physical properties of red blood cells
                      (RBCs), and the biorheology of blood flow. Hence, modeling
                      of hematologic disorders should take into account the
                      multiphase nature of blood flow, especially in arterioles
                      and capillaries. We present here an overview of a general
                      computational framework based on dissipative particle
                      dynamics (DPD) which has broad applicability in cell
                      biophysics with implications for diagnostics, therapeutics
                      and drug efficacy assessments for a wide variety of human
                      diseases. This computational approach, validated by
                      independent experimental results, is capable of modeling the
                      biorheology of whole blood and its individual components
                      during blood flow so as to investigate cell mechanistic
                      processes in health and disease. DPD is a Lagrangian method
                      that can be derived from systematic coarse-graining of
                      molecular dynamics but can scale efficiently up to
                      arterioles and can also be used to model RBCs down to the
                      spectrin level. We start from experimental measurements of a
                      single RBC to extract the relevant biophysical parameters,
                      using single-cell measurements involving such methods as
                      optical tweezers, atomic force microscopy and micropipette
                      aspiration, and cell-population experiments involving
                      microfluidic devices. We then use these validated RBC models
                      to predict the biorheological behavior of whole blood in
                      healthy or pathological states, and compare the simulations
                      with experimental results involving apparent viscosity and
                      other relevant parameters. While the approach discussed here
                      is sufficiently general to address a broad spectrum of
                      hematologic disorders including certain types of cancer,
                      this paper specifically deals with results obtained using
                      this computational framework for blood flow in malaria and
                      sickle cell anemia.},
      cin          = {IAS-2 / ICS-2},
      ddc          = {610},
      cid          = {I:(DE-Juel1)IAS-2-20090406 / I:(DE-Juel1)ICS-2-20110106},
      pnm          = {451 - Soft Matter Composites (POF2-451)},
      pid          = {G:(DE-HGF)POF2-451},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000331976300011},
      doi          = {10.1007/s10439-013-0922-3},
      url          = {https://juser.fz-juelich.de/record/154031},
}