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With the completion of species genome sequencing and the generation of a large amounts of biological data, genomic-scale metabolic models (GSMMs) have become an indispensable research tool in systems biology and metabolic engineering. GSMMs are increasingly being used in the field of industrial microbial manufacturing, including the analysis of metabolic network attributes, prediction and analysis of microbial growth phenotypes, model-based omics data interpretation, systematic metabolic engineering, and bacterial infection treatment.
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A genome-scale metabolic model (GSMM) is a specific microbial metabolic network composed of gene-protein (enzyme) – biochemical reaction associations by integrating genomics, transcriptomes, proteomics, and metabolomics data. GSMMs connect the biochemical reaction information obtained from the genome with the metabolic phenotype and provides an efficient platform for the systematic understanding of microbial physiological metabolic function models.
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Current advances in genome sequencing technologies enable a fast and cheap overview into the genetic composition of virtually any organism. Nevertheless, determining the global metabolic profile of a cell or organism is fundamental to provide a comprehensive readout of its functional state, resulting from the interplay between genome, biochemistry, and environment. In this context, genome-scale metabolic models (GSMMs) offer a systemic overview for the investigation of cell metabolic potential because of their key feature of embracing all available knowledge about the biochemical transformations taking place in a given cell or organism. The reconstruction methods of genome-scale metabolic network models are gradually standardized, and the main process of genome scale metabolic network model reconstruction is shown in Figure 1.
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Synbio Technologies’s genome-scale metabolic models, based on gene function annotation, constructs the association between microbial genome and metabolic activity, providing an efficient platform for comprehensive analysis of microbial physiological functions.
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