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dc.contributor.authorFedosov, Dmitry A.
dc.contributor.authorDao, Ming
dc.contributor.authorKarniadakis, George Em
dc.contributor.authorSuresh, Subra
dc.date.accessioned2016-10-06T22:12:54Z
dc.date.available2016-10-06T22:12:54Z
dc.date.issued2013-10
dc.date.submitted2013-07
dc.identifier.issn0090-6964
dc.identifier.issn1573-9686
dc.identifier.urihttp://hdl.handle.net/1721.1/104775
dc.description.abstractHematologic 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.en_US
dc.description.sponsorshipNational Institutes of Health (U.S.)en_US
dc.description.sponsorshipSingapore-MIT Alliance for Research and Technology (SMART)en_US
dc.description.sponsorshipUnited States. Dept. of Energy. Collaboratory on Mathematics for Mesoscopic Modeling of Materialsen_US
dc.description.sponsorshipUnited States. Dept. of Energy (INCITE Award)en_US
dc.publisherSpringer USen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s10439-013-0922-3en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceSpringer USen_US
dc.titleComputational Biorheology of Human Blood Flow in Health and Diseaseen_US
dc.typeArticleen_US
dc.identifier.citationFedosov, Dmitry A. et al. “Computational Biorheology of Human Blood Flow in Health and Disease.” Annals of Biomedical Engineering 42.2 (2014): 368–387.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Center for Materials Science and Engineeringen_US
dc.contributor.mitauthorDao, Ming
dc.relation.journalAnnals of Biomedical Engineeringen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2016-08-18T15:44:05Z
dc.language.rfc3066en
dc.rights.holderBiomedical Engineering Society
dspace.orderedauthorsFedosov, Dmitry A.; Dao, Ming; Karniadakis, George Em; Suresh, Subraen_US
dspace.embargo.termsNen
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


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