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dc.contributor.authorYurkovich, James T.
dc.contributor.authorBordbar, Aarash
dc.contributor.authorSigurjónsson, Ólafur E.
dc.contributor.authorYurkovich, James T.
dc.date.accessioned2018-05-23T13:14:03Z
dc.date.available2018-05-23T13:14:03Z
dc.date.issued2018-03-07
dc.date.submitted2018
dc.identifier.citationSystems biology as an emerging paradigm in transfusion medicine 2018, 12 (1) BMC Systems Biologyen
dc.identifier.issn1752-0509
dc.identifier.doi10.1186/s12918-018-0558-x
dc.identifier.urihttp://hdl.handle.net/2336/620562
dc.descriptionTo access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Filesen
dc.description.abstractBlood transfusions are an important part of modern medicine, delivering approximately 85 million blood units to patients annually. Recently, the field of transfusion medicine has started to benefit from the "omic" data revolution and corresponding systems biology analytics. The red blood cell is the simplest human cell, making it an accessible starting point for the application of systems biology approaches.In this review, we discuss how the use of systems biology has led to significant contributions in transfusion medicine, including the identification of three distinct metabolic states that define the baseline decay process of red blood cells during storage. We then describe how a series of perturbations to the standard storage conditions characterized the underlying metabolic phenotypes. Finally, we show how the analysis of high-dimensional data led to the identification of predictive biomarkers.The transfusion medicine community is in the early stages of a paradigm shift, moving away from the measurement of a handful of chosen variables to embracing systems biology and a cell-scale point of view.
dc.description.sponsorshipEuropean Research Council National Heart, Lung, and Blood Institute Landspitali University Hospital Research Funden
dc.language.isoenen
dc.publisherBioMed Centralen
dc.relation.urlhttps://bmcsystbiol.biomedcentral.com/articles/10.1186/s12918-018-0558-xen
dc.rightsArchived with thanks to BMC Systems Biologyen
dc.subjectBlóðgjöfen
dc.subjectBlóðkornen
dc.subjectBAB12en
dc.subject.meshSystems Biologyen
dc.subject.meshTransfusion Medicineen
dc.titleSystems biology as an emerging paradigm in transfusion medicineen
dc.typeArticleen
dc.contributor.department[ 1 ] Univ Calif San Diego, Dept Bioengn, 9500 Gilman Dr, La Jolla, CA 92093 USA Show more [ 2 ] Univ Calif San Diego, Bioinformat & Syst Biol Program, 9500 Gilman Dr, La Jolla, CA 92093 USA [ 3 ] Sinopia Biosci, 600 W Broadway Suite 700, San Diego, CA 92101 USA Show more [ 4 ] Reykjavik Univ, Sch Sci & Engn, Hringbraut 101, IS-101 Reykjavik, Iceland Show more [ 5 ] Landspitali Univ Hosp, Blood Bank, 9500 Gilman Dr, IS-101 Reykjavik, Iceland Show more [ 6 ] Univ Calif San Diego, Dept Pediat, La Jolla, CA 92093 USAen
dc.identifier.journalBMC Systems Biologyen
dc.rights.accessOpen Access - Opinn aðganguren
dc.departmentcodeBAB12
refterms.dateFOA2018-09-12T17:14:00Z
html.description.abstractBlood transfusions are an important part of modern medicine, delivering approximately 85 million blood units to patients annually. Recently, the field of transfusion medicine has started to benefit from the "omic" data revolution and corresponding systems biology analytics. The red blood cell is the simplest human cell, making it an accessible starting point for the application of systems biology approaches.In this review, we discuss how the use of systems biology has led to significant contributions in transfusion medicine, including the identification of three distinct metabolic states that define the baseline decay process of red blood cells during storage. We then describe how a series of perturbations to the standard storage conditions characterized the underlying metabolic phenotypes. Finally, we show how the analysis of high-dimensional data led to the identification of predictive biomarkers.The transfusion medicine community is in the early stages of a paradigm shift, moving away from the measurement of a handful of chosen variables to embracing systems biology and a cell-scale point of view.


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