in silico analysis and comparison of the metabolic capabilities of different organisms by reducing metabolic complexity

Abstract

Understanding how metabolic capabilities diverge across microbial species is fundamental for deciphering community function, ecological interactions, and for guiding synthetic microbiome design. Despite shared core pathways, microbial phenotypes can differ markedly due to evolutionary adaptations and metabolic specialization. Genome-scale metabolic models (GEMs) provide a systems-level framework to explore these differences; however, their complexity poses significant challenges to direct comparison. Here, we introduce NIS, a computational approach that uses the redGEM, lumpGEM, and redGEMX algorithms to systematically reduce GEMs to interpretable modules. NIS enables direct comparison of fueling pathways, biosynthetic routes, and environmental exchange processes across organisms, while preserving key metabolic information. We demonstrate the utility of NIS by analyzing Escherichia coli and Saccharomyces cerevisiae, revealing both conserved and divergent patterns in central metabolism, biomass biosynthesis, and substrate utilization. We further apply NIS to members of the core honeybee gut microbiome, uncovering distinct metabolic traits and complementarity that explain coexistence and interaction potential. Our framework offers a robust and scalable method to dissect microbial metabolic networks and supports the rational design and ecological understanding of microbial communities.