MIGRENE

MIGRENE constructs personalized genome-scale metabolic models (GEMs) from metagenomic and microbiome data using constraint-based modeling to analyze microbial metabolism and host–microbiome interactions.

Key Features:

• Personalized Microbiome Metabolism Framework: MIGRENE integrates microbiome composition, metagenomic species profiles, and microbial gene catalogues to construct personalized GEMs.
• Generation of Microbial-Level GEMs: The tool facilitates creation of genome-scale metabolic models at the microbial level for detailed analysis of microbial metabolism within a host environment.
• Reactobiome and Community Network Modeling: MIGRENE performs reactobiome modeling to assess reaction richness and abundance and supports community network modeling to probe interactions within microbial communities.
• Individualized Reaction Set Enrichment (iRSE): The framework enables enrichment analysis of individualized reaction sets to identify pathway-level differences per sample.
• Reduction in Reaction and GEM Richness Analysis: MIGRENE identifies significant reductions in reaction richness and GEM richness associated with altered microbial metabolism in disease states such as liver cirrhosis.

Scientific Applications:

• Gut microbiota–disease association analysis: Study links between gut microbiota composition and diseases such as liver cirrhosis by comparing personalized metabolic models.
• Metabolic pathway alteration detection: Detect and quantify changes in pathways including lipopolysaccharides (LPS), ammonia production, and tyrosine metabolism across samples.
• Community interaction inference: Use reactobiome and community network outputs to infer reaction-level richness, abundance, and interspecies metabolic interactions.

Methodology:

Integration of metagenomic data, microbiome composition, metagenomic species profiles, and microbial gene catalogues followed by construction of personalized GEMs using constraint-based genome-scale metabolic modeling, together with reactobiome modeling, community network modeling, individualized reaction set enrichment (iRSE), and analysis of reaction and GEM richness.