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NAViFluX: a visualization-centric platform for interactive analysis, refinement and design of genome-scale metabolic networks

Krishnamurthy, M. B.; P S, H.; Subramanian, A.

2026-01-03 systems biology
10.64898/2026.01.03.697453 bioRxiv
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Genome-scale metabolic network (GSMN) models are rigorously curated, cellular-level representations of metabolism that enable flux-based metabolite fate discovery, metabolic engineering, drug target identification and context-specific multi-omics integration. However, the inherent complexity of model architectures, need for programming skills and limited visualization support restrict their broader applicability. Existing tools focus on visualization and analyses separately, necessitating tool-specific format conversions, offer either topology or flux analyses options exclusively, lack intuitive pathway-specific visualizations, database-integrated model refinement, pathway enrichment and large-scale perturbation analyses. Here, we present NAViFluX (metabolic Network Analysis and Visualization of Flux), a visualization-centric, web browser-based environment that unifies GSMN exploration through native pathway / subsystem map generation using various layouts; interactive refinement through addition of reactions from KEGG / BiGG, amending constraints, pathway merging; and modules for flux computations, topology, functional enrichment. Instead of treating visualization as a post hoc step, NAViFluX performs all actions within network views, enabling users to make decisions directly from visually interpretable subnetworks. Using three Escherichia coli case studies, NAViFluX is shown to characterize nutrient-specific metabolic adaptations, improve metabolic gene essentiality predictions, provide mechanistic insight into synthetic lethality and facilitate the design of an optimized carbon-fixing metabolic state, thereby democratizing GSMNs for biologically meaningful applications. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/697453v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@9fff20org.highwire.dtl.DTLVardef@26b947org.highwire.dtl.DTLVardef@4e0f7aorg.highwire.dtl.DTLVardef@da307d_HPS_FORMAT_FIGEXP M_FIG C_FIG

Published in Bioinformatics (predicted rank #4) · training set

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