Visualizing metabolic network dynamics through time-series metabolomic data
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....
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-04-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-020-3415-z |
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| author | Lea F. Buchweitz James T. Yurkovich Christoph Blessing Veronika Kohler Fabian Schwarzkopf Zachary A. King Laurence Yang Freyr Jóhannsson Ólafur E. Sigurjónsson Óttar Rolfsson Julian Heinrich Andreas Dräger |
| author_facet | Lea F. Buchweitz James T. Yurkovich Christoph Blessing Veronika Kohler Fabian Schwarzkopf Zachary A. King Laurence Yang Freyr Jóhannsson Ólafur E. Sigurjónsson Óttar Rolfsson Julian Heinrich Andreas Dräger |
| author_sort | Lea F. Buchweitz |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-22T20:20:53Z |
| format | Article |
| id | doaj.art-d6b35fc262944f6188fb91f69512f3df |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-22T20:20:53Z |
| publishDate | 2020-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-d6b35fc262944f6188fb91f69512f3df2022-12-21T18:13:51ZengBMCBMC Bioinformatics1471-21052020-04-0121111010.1186/s12859-020-3415-zVisualizing metabolic network dynamics through time-series metabolomic dataLea F. Buchweitz0James T. Yurkovich1Christoph Blessing2Veronika Kohler3Fabian Schwarzkopf4Zachary A. King5Laurence Yang6Freyr Jóhannsson7Ólafur E. Sigurjónsson8Óttar Rolfsson9Julian Heinrich10Andreas Dräger11Computational Systems Biology of Infection and Antimicrobial-Resistant Pathogens, Institute for Biomedical Informatics (IBMI)Institute for Systems BiologyComputational Systems Biology of Infection and Antimicrobial-Resistant Pathogens, Institute for Biomedical Informatics (IBMI)Computational Systems Biology of Infection and Antimicrobial-Resistant Pathogens, Institute for Biomedical Informatics (IBMI)yWorks GmbHSystems Biology Research Group, Department of Bioengineering, University of California, San DiegoDepartment of Chemical Engineering, Queen’s UniversityCenter for Systems Biology, University of IcelandThe Blood Bank, Landspítali-University HospitalCenter for Systems Biology, University of IcelandDepartment of Computer Science, University of TübingenComputational Systems Biology of Infection and Antimicrobial-Resistant Pathogens, Institute for Biomedical Informatics (IBMI)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.http://link.springer.com/article/10.1186/s12859-020-3415-zData visualizationMetabolismMetabolomicsPlateletRed blood cell |
| spellingShingle | Lea F. Buchweitz James T. Yurkovich Christoph Blessing Veronika Kohler Fabian Schwarzkopf Zachary A. King Laurence Yang Freyr Jóhannsson Ólafur E. Sigurjónsson Óttar Rolfsson Julian Heinrich Andreas Dräger Visualizing metabolic network dynamics through time-series metabolomic data BMC Bioinformatics Data visualization Metabolism Metabolomics Platelet Red blood cell |
| title | Visualizing metabolic network dynamics through time-series metabolomic data |
| title_full | Visualizing metabolic network dynamics through time-series metabolomic data |
| title_fullStr | Visualizing metabolic network dynamics through time-series metabolomic data |
| title_full_unstemmed | Visualizing metabolic network dynamics through time-series metabolomic data |
| title_short | Visualizing metabolic network dynamics through time-series metabolomic data |
| title_sort | visualizing metabolic network dynamics through time series metabolomic data |
| topic | Data visualization Metabolism Metabolomics Platelet Red blood cell |
| url | http://link.springer.com/article/10.1186/s12859-020-3415-z |
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