Please note that Systems Microbiology does not consider descriptive studies that are solely based on amplicon (e.g., 16S rRNA) profiles, unless they are accompanied by a clear hypothesis and experimentation, and provide insight into the microbiological system or process being studied.
Most studies of microorganisms focus on several genes, enzymes, proteins, or certain metabolic pathways, whereas regulation in the cell is governed by a complicated network. In recent years, with the development of omics technology, such as genomics, transcriptomics, fluxomics, and metabolomics, the generation of large amounts of data has been accompanied by the weaving of knowledge on microbial metabolism into a huge network for exploring the physiological characteristics of microorganisms. Consequently, the concept of establishing models to explore microbial metabolic regulatory networks was proposed.
Genome-Scale Metabolic Models (GSMM) are the bridge connecting the genome-derived biochemical information and metabolic phenotypes, providing a solid framework for interpreting whole-cell metabolic activities. Various biochemical reactions representing molecular mechanisms and the associated molecular components (enzymes, substrates, and products) are assembled in GSMM. Each corresponding biochemical transformation of metabolism can be identified either directly by enzymological assays or indirectly from genomic sequences by homology search with proteins whose functions have been previously elucidated. As a result, GSMM provide the ability to computationally and comprehensively connect gene-protein-reaction (G-P-R) as a cellular-level network for entire metabolic genes in an organism, which can be simulated to predict metabolic fluxes for various systems-level metabolic studies.
Currently, GSMM-related technologies are rapidly evolving with valuable metabolic data being generated and modeling techniques being iteratively updated. GSMM is a promising research area for systematically understanding the physiological and metabolic characteristics of microorganisms from a global cellular perspective. And thus, GSMM has great potential for industrial applications in guiding the production of targeted metabolites or deciphering relevant biological mechanisms. What’s more, the process of GSMM reconstruction has elevated biology into a subject of multidisciplinary integration. In order to improve the accuracy of GSSM, related research involving gap filling, model expansion, and teasing out missing reactions is also attractive.
We welcome submissions of Original Research, Review, and Mini-Review articles that cover, but are not limited to, the following subtopics:
• Developing/Reconstruction of high-quality GSMM for particular organisms.
• Community modeling to understand the metabolic interplay of microbiota community or microbe-host interaction.
• GSMM in predicting and analyzing bacterial growth phenotypes, e.g., identification of gene essentialities through in silico gene knockout.
• Application of GSMM in industry, metabolic engineering, e.g., application in enhancing metabolite production yield.
• Reconciling the GSMM prediction and experimental observation to discover new biological pathways/routes.
• GSMM in interpreting experimental data, e.g., metabolic flux measurements, metabolite concentrations, and gene expression.
• GSMM in understanding metabolism, e.g., investigating essential metabolic pathways, exploring the relationships and dependencies between different metabolic reactions, and identifying new metabolic pathways.
• Novel methods/algorithms to incorporate omics data into GSMM for biological discoveries.
• In vivo methods to improve the accuracy of GSMM.
• In silico methods to improve the accuracy of GSMM.
Please note that Systems Microbiology does not consider descriptive studies that are solely based on amplicon (e.g., 16S rRNA) profiles, unless they are accompanied by a clear hypothesis and experimentation, and provide insight into the microbiological system or process being studied.
Most studies of microorganisms focus on several genes, enzymes, proteins, or certain metabolic pathways, whereas regulation in the cell is governed by a complicated network. In recent years, with the development of omics technology, such as genomics, transcriptomics, fluxomics, and metabolomics, the generation of large amounts of data has been accompanied by the weaving of knowledge on microbial metabolism into a huge network for exploring the physiological characteristics of microorganisms. Consequently, the concept of establishing models to explore microbial metabolic regulatory networks was proposed.
Genome-Scale Metabolic Models (GSMM) are the bridge connecting the genome-derived biochemical information and metabolic phenotypes, providing a solid framework for interpreting whole-cell metabolic activities. Various biochemical reactions representing molecular mechanisms and the associated molecular components (enzymes, substrates, and products) are assembled in GSMM. Each corresponding biochemical transformation of metabolism can be identified either directly by enzymological assays or indirectly from genomic sequences by homology search with proteins whose functions have been previously elucidated. As a result, GSMM provide the ability to computationally and comprehensively connect gene-protein-reaction (G-P-R) as a cellular-level network for entire metabolic genes in an organism, which can be simulated to predict metabolic fluxes for various systems-level metabolic studies.
Currently, GSMM-related technologies are rapidly evolving with valuable metabolic data being generated and modeling techniques being iteratively updated. GSMM is a promising research area for systematically understanding the physiological and metabolic characteristics of microorganisms from a global cellular perspective. And thus, GSMM has great potential for industrial applications in guiding the production of targeted metabolites or deciphering relevant biological mechanisms. What’s more, the process of GSMM reconstruction has elevated biology into a subject of multidisciplinary integration. In order to improve the accuracy of GSSM, related research involving gap filling, model expansion, and teasing out missing reactions is also attractive.
We welcome submissions of Original Research, Review, and Mini-Review articles that cover, but are not limited to, the following subtopics:
• Developing/Reconstruction of high-quality GSMM for particular organisms.
• Community modeling to understand the metabolic interplay of microbiota community or microbe-host interaction.
• GSMM in predicting and analyzing bacterial growth phenotypes, e.g., identification of gene essentialities through in silico gene knockout.
• Application of GSMM in industry, metabolic engineering, e.g., application in enhancing metabolite production yield.
• Reconciling the GSMM prediction and experimental observation to discover new biological pathways/routes.
• GSMM in interpreting experimental data, e.g., metabolic flux measurements, metabolite concentrations, and gene expression.
• GSMM in understanding metabolism, e.g., investigating essential metabolic pathways, exploring the relationships and dependencies between different metabolic reactions, and identifying new metabolic pathways.
• Novel methods/algorithms to incorporate omics data into GSMM for biological discoveries.
• In vivo methods to improve the accuracy of GSMM.
• In silico methods to improve the accuracy of GSMM.
