Visualizing metabolic network dynamics through time-series metabolomic data.
Average rating
Cast your vote
You can rate an item by clicking the amount of stars they wish to award to this item.
When enough users have cast their vote on this item, the average rating will also be shown.
Star rating
Your vote was cast
Thank you for your feedback
Thank you for your feedback
Authors
Buchweitz, Lea FYurkovich, James T
Blessing, Christoph
Kohler, Veronika
Schwarzkopf, Fabian
King, Zachary A
Yang, Laurence
Jóhannsson, Freyr
Sigurjónsson, Ólafur E
Rolfsson, Óttar
Heinrich, Julian
Dräger, Andreas
Issue Date
2020-04-03
Metadata
Show full item recordCitation
Buchweitz LF, Yurkovich JT, Blessing C, et al. Visualizing metabolic network dynamics through time-series metabolomic data. BMC Bioinformatics. 2020;21(1):130. Published 2020 Apr 3. doi:10.1186/s12859-020-3415-zAbstract
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.Description
To 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 DownloadAdditional Links
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3415-zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7119163/
ae974a485f413a2113503eed53cd6c53
10.1186/s12859-020-3415-z
Scopus Count
Collections
Related articles
- Translational Metabolomics of Head Injury: Exploring Dysfunctional Cerebral Metabolism with Ex Vivo NMR Spectroscopy-Based Metabolite Quantification
- Authors: Wolahan SM, Hirt D, Glenn TC, Kobeissy FH
- Issue date: 2015
- Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics.
- Authors: Bordbar A, Yurkovich JT, Paglia G, Rolfsson O, Sigurjónsson ÓE, Palsson BO
- Issue date: 2017 Apr 7
- Genome-Scale <sup>13</sup>C Fluxomics Modeling for Metabolic Engineering of Saccharomyces cerevisiae.
- Authors: Ando D, García Martín H
- Issue date: 2019
- Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis.
- Authors: Chong J, Wishart DS, Xia J
- Issue date: 2019 Dec
- MetaMapp: mapping and visualizing metabolomic data by integrating information from biochemical pathways and chemical and mass spectral similarity.
- Authors: Barupal DK, Haldiya PK, Wohlgemuth G, Kind T, Kothari SL, Pinkerton KE, Fiehn O
- Issue date: 2012 May 16