ArticleFrontiers in bioinformatics2026
Metabolite coupling analysis and metabolite-flux coupling analysis of genome-scale metabolic models.
Article in Frontiers in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
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.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.