Abstract

BackgroundNew 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.ResultsThe 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.ConclusionsThe 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.

Highlights

  • New technologies have given rise to an abundance of -omics data, metabolomic data

  • We present Genome-scale metabolic model (GEM)-Vis as a new approach for the visualization of time-course metabolomic data in the context of large-scale metabolic network maps

  • The idea of GEM-Vis is that time series can be adequately observed in the form of an animated sequence of a dynamically changing network map when using an appropriate representation of metabolic quantities

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Summary

Introduction

New technologies have given rise to an abundance of -omics data, 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. We present GEM-Vis, an original method for the visualization of time-course metabolomic data within the context of metabolic network maps. Over the last few decades, new technological developments have enabled the generation of vast amounts of “-omics” data [1] These various -omic data types have helped bring new insights to a vast array of biological questions [2,3,4]. We present GEM-Vis as a new approach for the visualization of time-course metabolomic data in the context of large-scale metabolic network maps

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