AI Article Synopsis
- * Genome-scale metabolic models (GEMs) serve as extensive databases that integrate information about genes, proteins, and metabolites, and are used to analyze complex metabolic networks and predict their behavior.
- * Enhanced GEMs now incorporate multi-omics data from cells, tissues, and microbiomes, leading to improved treatment designs and diagnostic tools for metabolic diseases, as well as more effective identification of mechanisms and drug targets.
Article Abstract
Metabologenomics integrates metabolomics with other omics data types to comprehensively study the genetic and environmental factors that influence metabolism. These multi-omics data can be incorporated into genome-scale metabolic models (GEMs), which are highly curated knowledge bases that explicitly account for genes, transcripts, proteins and metabolites. By including all known biochemical reactions catalysed by enzymes and transporters encoded in the human genome, GEMs analyse and predict the behaviour of complex metabolic networks. Continued advancements to the scale and scope of GEMs - from cells and tissues to microbiomes and the whole body - have helped to design effective treatments and develop better diagnostic tools for metabolic diseases. Furthermore, increasing amounts of multi-omics data are incorporated into GEMs to better identify the underlying mechanisms, biomarkers and potential drug targets of metabolic diseases.
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| http://dx.doi.org/10.1038/s41576-024-00768-0 | DOI Listing |
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