ArticleBMC bioinformatics2020
Visualizing metabolic network dynamics through time-series metabolomic data.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 23 citations in OpenAlex.
- A metabolomic profile of biological aging in 250,341 individuals from the UK Biobank.Nature communications · 2024Article
- Genome-scale metabolic models consistently predictFrontiers in bioinformatics · 2023Article
- Microbiome and Metabolomics in Liver Cancer: Scientific Technology.International journal of molecular sciences · 2022Review
- Molecular Regulatory Mechanisms Drive Emergent Pathogenetic Properties ofMicroorganisms · 2022Review
- Inter-study and time-dependent variability of metabolite abundance in cultured red blood cells.Malaria journal · 2021Article
- Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions.Microbial cell factories · 2021Article
- Network analyses in microbiome based on high-throughput multi-omics data.Briefings in bioinformatics · 2021Review
- Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics-Standardization, Coverage, and Throughput.Analytical chemistry · 2021Review
- Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks.Computational and structural biotechnology journal · 2021Review
Corrections and comments
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Authors and funding
12 authors at 7 institutions in 5 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.