Front Bioinform. 2026 Jul 1;6:1859473. doi: 10.3389/fbinf.2026.1859473. eCollection 2026.
ABSTRACT
BACKGROUND: Genome-scale metabolic models (GEMs) provide detailed representations of metabolic networks. Flux Coupling Analysis (FCA) is widely used for analyzing dependencies between reaction fluxes in GEMs.
RESULTS: We introduce Metabolite Coupling Analysis (MCA) and Metabolite-flux Coupling Analysis (MetFCA), two methods that extend FCA concepts from reactions to metabolites and metabolite-reaction pairs, enabling the identification of condition-specific modules for omics (e.g., transcriptomics, proteomics, and metabolomics) data analysis.
CONCLUSION: MCA and MetFCA, together with FCA, provide a unified framework for generating condition-specific modules in GEMs. These modules exhibit clearer biological functions than those generated by statistical, data-driven approaches. A case study demonstrates the use of gene modules to analyze transcriptomics data in the influenza-infected Calu-3 cell line.
PMID:42460396 | PMC:PMC13370160 | DOI:10.3389/fbinf.2026.1859473
