@article{Buchweitz2020,
  author     = {Buchweitz, Lea F. and Yurkovich, James T. and Blessing, Christoph and Kohler,
               Veronika and Schwarzkopf, Fabian and King, Zachary A. and Yang, Laurence and
               J\'ohannsson, Freyr and Sigurj\'onsson, \'Olafur E. and Rolfsson, \'Ottar and
               Heinrich, Julian and Dr\"ager, Andreas},
  journal    = {BMC Bioinformatics},
  title      = {Visualizing metabolic network dynamics through time-series metabolomic data},
  year       = {2020},
  issn       = {1471-2105},
  month      = apr,
  number     = {1},
  pages      = {130},
  volume     = {21},
  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.},
  doi        = {10.1186/s12859-020-3415-z},
  keywords   = {Data visualization; Metabolism; Metabolomics; Platelet; Red blood cell},
  pdf        = {https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-020-3415-z},
  url        = {https://doi.org/10.1186/s12859-020-3415-z},
}