Recent research has revealed significant taxonomic richness and diversity within the microbiome across various ecosystems, such as the human gut, skin, and soil, using different meta-omics data. However, the mechanisms underlying the interactions between diet, gut microbiome, and host metabolism remain poorly understood. Microbial metabolism is a major determinant of host-microbial homeostasis. In recent years, metabolic network analysis in the gut microbiome has improved due to advances in high-throughput sequencing technologies, computational methods, and data integration approaches. However, improvements are still needed, such as accurate genome annotation and multi-omics data integration into the community and species-level networks or models. Further research is needed to improve the accuracy and comprehensiveness of microbiome metabolism and to develop new computational tools and data integration approaches.
Genome-scale metabolic modelling (GSMM) is one of the approaches that could infer interactions between diet-microbiome, microbe-microbe, and host-microbe under physiological conditions. GSMM and metabolic network analysis could help us better understand the role of microbial metabolism in health and disease, and to develop new therapies and interventions for microbiome-related disorders.
This research topic aims to publish recent advances in the gut microbiome from meta-omics datasets to microbiome modelling and tools and resources for integrating the datasets into metabolic networks or models. We welcome the submission of original research, reviews and minireviews, methods, resources, and perspectives on future directions and challenges. Potential topics may include, but are not limited to:
• Developing improved computational methods for the analysis of microbial metabolism to identify key metabolic pathways and interactions.
• Integrating genome-scale metabolic models (GEMs) of the gut microbiome with models of host metabolism to understand the interactions between the gut microbiome and the host comprehensively.
• Better experimental validation to ensure an accurate representation of the metabolic capabilities of the gut microbiome.
• Investigating dysbiosis of disease-associated microbes in the gut across various diseases and interventions, and the potential impact of treatments on the microbiome.
• Exploring the potential of microbial metabolism in personalized medicine, enabling the identification of personalized dietary interventions, and predicting the efficacy of probiotics and prebiotics.
• Highlighting the importance of GSMM in the gut microbiome and its potential applications in various fields, such as personalized medicine and the development of probiotics and prebiotics.
• Improving metagenomics annotation and integrating meta-omics for studying the metabolic activity of gut microbiomes
Recent research has revealed significant taxonomic richness and diversity within the microbiome across various ecosystems, such as the human gut, skin, and soil, using different meta-omics data. However, the mechanisms underlying the interactions between diet, gut microbiome, and host metabolism remain poorly understood. Microbial metabolism is a major determinant of host-microbial homeostasis. In recent years, metabolic network analysis in the gut microbiome has improved due to advances in high-throughput sequencing technologies, computational methods, and data integration approaches. However, improvements are still needed, such as accurate genome annotation and multi-omics data integration into the community and species-level networks or models. Further research is needed to improve the accuracy and comprehensiveness of microbiome metabolism and to develop new computational tools and data integration approaches.
Genome-scale metabolic modelling (GSMM) is one of the approaches that could infer interactions between diet-microbiome, microbe-microbe, and host-microbe under physiological conditions. GSMM and metabolic network analysis could help us better understand the role of microbial metabolism in health and disease, and to develop new therapies and interventions for microbiome-related disorders.
This research topic aims to publish recent advances in the gut microbiome from meta-omics datasets to microbiome modelling and tools and resources for integrating the datasets into metabolic networks or models. We welcome the submission of original research, reviews and minireviews, methods, resources, and perspectives on future directions and challenges. Potential topics may include, but are not limited to:
• Developing improved computational methods for the analysis of microbial metabolism to identify key metabolic pathways and interactions.
• Integrating genome-scale metabolic models (GEMs) of the gut microbiome with models of host metabolism to understand the interactions between the gut microbiome and the host comprehensively.
• Better experimental validation to ensure an accurate representation of the metabolic capabilities of the gut microbiome.
• Investigating dysbiosis of disease-associated microbes in the gut across various diseases and interventions, and the potential impact of treatments on the microbiome.
• Exploring the potential of microbial metabolism in personalized medicine, enabling the identification of personalized dietary interventions, and predicting the efficacy of probiotics and prebiotics.
• Highlighting the importance of GSMM in the gut microbiome and its potential applications in various fields, such as personalized medicine and the development of probiotics and prebiotics.
• Improving metagenomics annotation and integrating meta-omics for studying the metabolic activity of gut microbiomes
