Visualizing metabolic network dynamics through time-series metabolomic data

Area of Science:

• Computational Biology
• Systems Biology
• Metabolomics

Background:

• Advancements in -omics technologies generate vast metabolomic datasets.
• Interpreting large-scale metabolomic data requires innovative computational visualization methods.
• GEM-Vis is introduced for visualizing time-course metabolomic data within metabolic networks.

Purpose of the Study:

• To present GEM-Vis, a novel method for visualizing time-course metabolomic data.
• To demonstrate GEM-Vis utility using human platelet and erythrocyte data under cold storage.
• To provide insights into cellular metabolic states during storage conditions.

Main Methods:

• Development of the GEM-Vis computational visualization method.
• Application of GEM-Vis to time-course metabolomic data from human platelets and erythrocytes.
• Generation of animated videos to illustrate metabolic changes.

Main Results:

• Animated visualizations revealed metabolic state changes in platelets and erythrocytes during storage.
• Identified nicotinamide accumulation in platelets, potentially linked to purine metabolism pathways.
• Observed significant alterations in erythrocyte metabolite pools under varying storage temperatures and times.

Conclusions:

• GEM-Vis is effective for large-scale network exploration and hypothesis generation.
• The method facilitates understanding of cellular physiology at the network level.
• GEM-Vis can be adapted for visualizing other longitudinal -omics data types.