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Visualizing metabolic network dynamics through time-series metabolomic data

Visualizing metabolic network dynamics through time-series metabolomic data

Title: Visualizing metabolic network dynamics through time-series metabolomic data
Author: Buchweitz, Lea F.
Yurkovich, James T.
Blessing, Christoph
Kohler, Veronika
Schwarzkopf, Fabian
King, Zachary A.
Yang, Laurence
Jóhannsson, Freyr   orcid.org/0000-0001-6460-2463
Sigurjónsson, Ólafur E.
Rolfsson, Óttar   orcid.org/0000-0003-4258-6057
... 2 more authors Show all authors
Date: 2020-04-03
Language: English
Scope: 130
University/Institute: Háskóli Íslands
University of Iceland
Háskólinn í Reykjavík
Reykjavik University
School: Heilbrigðisvísindasvið (HÍ)
School of Health Sciences (UI)
Tæknisvið (HR)
School of Technology (RU)
Department: Rannsóknarsetur í kerfislíffræði (HÍ)
Center for Systems Biology (UI)
Series: BMC Bioinformatics;21(1)
ISSN: 1471-2105
DOI: 10.1186/s12859-020-3415-z
Subject: Data visualization; Metabolism; Metabolomics; Platelet; Red blood cell; Blóðkorn; Efnaskipti; Gagnagreining
URI: https://hdl.handle.net/20.500.11815/2385

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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


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.


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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.

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