Abstract
Genome-scale metabolic models (GEMs) have become a popular tool for systems biology, and they have been used in many fields such as industrial biotechnology and systems medicine. Since more and more studies are being conducted using GEMs, they have recently received considerable attention. In this review, we introduce the basic concept of GEMs and provide an overview of their applications in biotechnology, systems medicine, and some other fields. In addition, we describe the general principle of the applications and analyses built on GEMs. The purpose of this review is to introduce the application of GEMs in biological analysis and to promote its wider use by biologists.
Highlights
Genome-scale metabolic models (GEMs) are reconstructions of the metabolic networks of many kinds of cells, including those of microorganisms, plants, and mammals
With the development of systems biology, GEMs were used as scaffolds for systematic integration of omics data because GEMs could be used to reconstruct the relationship among genes, enzymes, and metabolism
It’s worth mentioning that, after a certain period of adaptive evolution, a false-positive knockout could become nonessential in vivo again (Patil et al, 2005).essentiality analysis (EA) and synthetic lethality analysis (SLA) have mainly been used to validate newly constructed GEMs and in recent years, EA and SLA were applied to study of systems medicine
Summary
Genome-scale metabolic models (GEMs) are reconstructions of the metabolic networks of many kinds of cells, including those of microorganisms, plants, and mammals. GEMs could represent the whole tissue or body of a multicellular organism In these metabolic networks, the gene-protein-reaction (GPR) relationships are annotated. Since GPR relationships are included in GEMs, other omics data such as transcriptomic and proteomic data could be systematically integrated into GEMs. GEM-based multi-omic analyses are more informative with stoichiometric balance and could possibly provide deeper biological insights. Researchers tried to use GEM-based in silico simulations to guide the rational design of industrial microorganisms (hereafter referred to as in silico metabolic engineering). With the development of systems biology, GEMs were used as scaffolds for systematic integration of omics data because GEMs could be used to reconstruct the relationship among genes, enzymes, and metabolism. The information presented here is expected to promote the spread of GEM usage by biologists
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