Human gut metagenomics study offers new insight for understanding the microbial communities residing in the gastrointestinal tract. Genome-Scale Metabolic Models (GEMs) are one of the potent tools for predicting the metabolic reactions among microbial communities. Developed from the traditional GEMs, enzyme constraints GEMs (ecGEMs) consider enzyme usage in the models as enzymes catalyze ability also plays important roles in metabolites reactions. Although the ecGEMs have alreadv been used in several studies for better dissecting the microbial metabolites pathways, still lack of the automatic pipeline to directly generate and analyse ecGEMs from the metagenomics raw data. In this project, we are going to develop a pipeline for automatically assemble and binning the raw metagenomics data and generating the enzyme constraint Genome-Scale Metabolic Models (ecGEMs). The simulation of the ecGEMs will also be performed in this project. The published metagenomics data from human gut, oral, and plants will be collected from the online databases and used to test the pipeline. Thereafter, the raw metagenomics data from the human gut of liver cancer patients will be analyzed by the pipeline. EcGEMs for each microbe in the samples will be generated and simulated for a better understanding of the relationship between liver cancer and the gut microbial community.