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

## TL;DR

This paper introduces NIS, a computational tool that simplifies and compares microbial metabolic models to better understand ecological interactions and microbiome design.

## Contribution

NIS is a novel workflow that integrates multiple algorithms to reduce and compare genome-scale metabolic models in a biologically interpretable way.

## Key findings

- NIS revealed conserved and divergent metabolic strategies in Escherichia coli and Saccharomyces cerevisiae.
- Application to the honeybee gut microbiome uncovered functional redundancy and cross-feeding interactions.
- NIS enables scalable and reproducible analysis of microbial metabolic networks beyond gene content or taxonomy.

## Abstract

Understanding how metabolic capabilities diverge across microbial species is essential for deciphering community function, ecological interactions, and the design of synthetic microbiomes. 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 hinders direct comparison.

We introduce NIS (Neidhardt–Ingraham–Schaechter), a computational workflow that integrates the redGEM, lumpGEM, and redGEMX algorithms to systematically reduce genome-scale models into biologically interpretable modules. This approach enables direct, quantitative comparison of fueling pathways, biomass biosynthetic routes, and environmental exchange processes while retaining essential metabolic information. We first demonstrate the utility of NIS by analyzing Escherichia coli and Saccharomyces cerevisiae, which revealed both conserved and divergent strategies in central metabolism, biosynthetic cost, and substrate utilization. We then applied NIS to the core honeybee gut microbiome, uncovering distinct metabolic traits, functional redundancy, and complementarity that help explain auxotrophy, cross-feeding interactions, and microbial coexistence.

NIS provides an automated, scalable, and reproducible framework for dissecting microbial metabolic networks beyond gene content or taxonomy. By linking metabolism to ecological function, NIS offers new opportunities to interpret microbial community dynamics and to support the rational design of microbiomes in health, agriculture, and environmental applications.

Video Abstract

Video Abstract

The online version contains supplementary material available at 10.1186/s40168-025-02299-0.