Genome scale metabolic models (GEM) are powerful systems biology tools to gain species-level and community-level functional insights into microbiome data. A large number of high-quality GEMs are now available such as BiGG, Human Metabolic Atlas, Virtual Metabolic Human, BioModels, etc. However, there is a lack of proficient resource that supports efficient search, visualize and extract knowledge from these large networks comprising thousands of reactions and metabolites that are characteristic of microbiome studies. To address this need , we have developed MicrobiomeNet. It main features include

• Search, browse and visualize pathways and GEMs at species level;
• Create context-specific or personalized community-scale GEMs by integrating multi-omics data
• Interrogate microbe-microbe or microbe-host interactions