Genome-scale metabolic modeling has become widespread for analyzing microbial metabolism. Extending this established paradigm to more complex microbial communities is emerging as a promising way to unravel the interactions and biochemical repertoire of these omnipresent systems. While several modeling techniques have been developed for microbial communities, little emphasis has been placed on the need to impose a time-averaged constant growth rate across all members for a community to ensure co-existence and stability.
View Article and Find Full Text PDFA combined metabolomic, biochemical, fluxomic, and metabolic modeling approach was developed using 19 genetically distant maize () lines from Europe and America. Considerable differences were detected between the lines when leaf metabolic profiles and activities of the main enzymes involved in primary metabolism were compared. During grain filling, the leaf metabolic composition appeared to be a reliable marker, allowing a classification matching the genetic diversity of the lines.
View Article and Find Full Text PDFThe gut microbiota modulates obesity and associated metabolic phenotypes in part through intestinal farnesoid X receptor (FXR) signaling. Glycine-β-muricholic acid (Gly-MCA), an intestinal FXR antagonist, has been reported to prevent or reverse high-fat diet (HFD)-induced and genetic obesity, insulin resistance, and fatty liver; however, the mechanism by which these phenotypes are improved is not fully understood. The current study investigated the influence of FXR activity on the gut microbiota community structure and function and its impact on hepatic lipid metabolism.
View Article and Find Full Text PDFIn this review, we will present the latest developments in systems biology with particular emphasis on improving nitrogen-use efficiency (NUE) in crops such as maize and demonstrating the application of metabolic models. The review highlights the importance of improving NUE in crops and provides an overview of the transcriptome, proteome, and metabolome datasets available, focusing on a comprehensive understanding of nitrogen regulation. 'Omics' data are hard to interpret in the absence of metabolic flux information within genome-scale models.
View Article and Find Full Text PDFA genome-scale model (GSM) is an in silico metabolic model comprising hundreds or thousands of chemical reactions that constitute the metabolic inventory of a cell, tissue, or organism. A complete, accurate GSM, in conjunction with a simulation technique such as flux balance analysis (FBA), can be used to comprehensively predict cellular metabolic flux distributions for a given genotype and given environmental conditions. Apart from enabling a user to quantitatively visualize carbon flow through metabolic pathways, these flux predictions also facilitate the hypothesis of new network properties.
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