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Visualizing metabolic network dynamics through time-series metabolomic data

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dc.contributor Háskóli Íslands
dc.contributor University of Iceland
dc.contributor Háskólinn í Reykjavík
dc.contributor Reykjavik University
dc.contributor.author Buchweitz, Lea F.
dc.contributor.author Yurkovich, James T.
dc.contributor.author Blessing, Christoph
dc.contributor.author Kohler, Veronika
dc.contributor.author Schwarzkopf, Fabian
dc.contributor.author King, Zachary A.
dc.contributor.author Yang, Laurence
dc.contributor.author Jóhannsson, Freyr
dc.contributor.author Sigurjónsson, Ólafur E.
dc.contributor.author Rolfsson, Óttar
dc.contributor.author Heinrich, Julian
dc.contributor.author Dräger, Andreas
dc.date.accessioned 2021-01-15T15:34:07Z
dc.date.available 2021-01-15T15:34:07Z
dc.date.issued 2020-04-03
dc.identifier.citation Buchweitz, L.F., Yurkovich, J.T., Blessing, C. et al. Visualizing metabolic network dynamics through time-series metabolomic data. BMC Bioinformatics 21, 130 (2020). https://doi.org/10.1186/s12859-020-3415-z
dc.identifier.issn 1471-2105
dc.identifier.uri https://hdl.handle.net/20.500.11815/2385
dc.description Publisher's version (útgefin grein)
dc.description.abstract Background: New technologies have given rise to an abundance of-omics data, particularly metabolomic data. The scale of these data introduces new challenges for the interpretation and extraction of knowledge, requiring the development of innovative computational visualization methodologies. Here, we present GEM-Vis, an original method for the visualization of time-course metabolomic data within the context of metabolic network maps. We demonstrate the utility of the GEM-Vis method by examining previously published data for two cellular systems-the human platelet and erythrocyte under cold storage for use in transfusion medicine. Results: The results comprise two animated videos that allow for new insights into the metabolic state of both cell types. In the case study of the platelet metabolome during storage, the new visualization technique elucidates a nicotinamide accumulation that mirrors that of hypoxanthine and might, therefore, reflect similar pathway usage. This visual analysis provides a possible explanation for why the salvage reactions in purine metabolism exhibit lower activity during the first few days of the storage period. The second case study displays drastic changes in specific erythrocyte metabolite pools at different times during storage at different temperatures. Conclusions: The new visualization technique GEM-Vis introduced in this article constitutes a well-suitable approach for large-scale network exploration and advances hypothesis generation. This method can be applied to any system with data and a metabolic map to promote visualization and understand physiology at the network level. More broadly, we hope that our approach will provide the blueprints for new visualizations of other longitudinal-omics data types. The supplement includes a comprehensive user's guide and links to a series of tutorial videos that explain how to prepare model and data files, and how to use the software SBMLsimulator in combination with further tools to create similar animations as highlighted in the case studies.
dc.description.sponsorship This work was funded by the National Institutes of Health (NIH, US, grants 2R01GM070923-13 to AD and U01-GM102098 to LY), the Institute for Systems Biology’s Translational Research Fellowship (JTY), the Landspítali University Hospital Research Fund, the University of Iceland Research Fund, and the Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (grant NNF10CC1016517). AD was supported by infrastructural funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections and by the DZIF (German Center for Infection Research). We acknowledge support by Open Access Publishing Fund of University of Tübingen (https://uni-tuebingen.de/de/58988). This work was made possible by the friendly support of yWorks GmbH (https://www.yworks.com) who provided their diagram visualization library yFiles for Java (https://www.yworks.com/products/yfiles-for-java) and assistance during the implementation phase. GEMA-free music licenses for the tutorial videos on SBMLsimulator were provided by Frametraxx UG (https://www.frametraxx.de). We also thank the Google Summer of Code program (https://summerofcode. withgoogle.com) for supporting open-source software development for this project.
dc.format.extent 130
dc.language.iso en
dc.publisher Springer Science and Business Media LLC
dc.relation.ispartofseries BMC Bioinformatics;21(1)
dc.rights info:eu-repo/semantics/openAccess
dc.subject Data visualization
dc.subject Metabolism
dc.subject Metabolomics
dc.subject Platelet
dc.subject Red blood cell
dc.subject Blóðkorn
dc.subject Efnaskipti
dc.subject Gagnagreining
dc.title Visualizing metabolic network dynamics through time-series metabolomic data
dc.type info:eu-repo/semantics/article
dcterms.license Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
dc.description.version Peer Reviewed
dc.identifier.journal BMC Bioinformatics
dc.identifier.doi 10.1186/s12859-020-3415-z
dc.relation.url https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3415-z
dc.contributor.department Rannsóknarsetur í kerfislíffræði (HÍ)
dc.contributor.department Center for Systems Biology (UI)
dc.contributor.school Heilbrigðisvísindasvið (HÍ)
dc.contributor.school School of Health Sciences (UI)
dc.contributor.school Tæknisvið (HR)
dc.contributor.school School of Technology (RU)

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