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Synthetic gut microbiome: Advances and challenges

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dc.contributor.author A.Mabwi, Humphrey
dc.contributor.author Kim, Eunjung
dc.contributor.author Song, Dae-Geun
dc.contributor.author ShinYoon, Hyo
dc.contributor.author Pan, Cheol-Ho
dc.contributor.author Komba, Erick.V.G.
dc.contributor.author Ko, GwangPyo
dc.contributor.author HyunCha, Kwang
dc.date.accessioned 2021-07-08T04:42:11Z
dc.date.available 2021-07-08T04:42:11Z
dc.date.issued 2020-12-24
dc.identifier.uri https://repository.rsif-paset.org/xmlui/handle/123456789/93
dc.description Journal Article en_US
dc.description.abstract An exponential rise in studies regarding the association among human gut microbial communities, human health, and diseases is currently attracting the attention of researchers to focus on human gut microbiome research. However, even with the ever-growing number of studies on the human gut microbiome, translation into improved health is progressing slowly. This hampering is due to the complexities of the human gut microbiome, which is composed of >1,000 species of microorganisms, such as bacteria, archaea, viruses, and fungi. To overcome this complexity, it is necessary to reduce the gut microbiome, which can help simplify experimental variables to an extent, such that they can be deliberately manipulated and controlled. Reconstruction of synthetic or established gut microbial communities would make it easier to understand the structure, stability, and functional activities of the complex microbial community of the human gut. Here, we provide an overview of the developments and challenges of the synthetic human gut microbiome, and propose the incorporation of multi-omics and mathematical methods in a better synthetic gut ecosystem design, for easy translation of microbiome information to therapies. en_US
dc.publisher Computational and Structural Biotechnology Journal en_US
dc.relation.ispartofseries 19;
dc.subject Synthetic microbiota Gut ecosystem Omics Mathematical modelling en_US
dc.title Synthetic gut microbiome: Advances and challenges en_US
dc.type Article en_US


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