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김동혁

Kim, Donghyuk
Systems Biology and Machine Learning Lab.
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dc.citation.conferencePlace KO -
dc.citation.conferencePlace 대전 -
dc.citation.title mBiome 학술대회 -
dc.contributor.author Kim, Donghyuk -
dc.date.accessioned 2024-01-31T23:08:59Z -
dc.date.available 2024-01-31T23:08:59Z -
dc.date.created 2020-03-19 -
dc.date.issued 2019-12-10 -
dc.description.abstract The complicated and dynamic nature of microbiome investigation can benefit from systems-biology approaches including in silico genome-wide computation and in vivo genome-scale experimental measurement. In this talk, three systems toolsets will be introduced, which can be applied to study individual bacterium or microbiome: pan-genome analysis, analysis of transcriptional regulatory network, and genome-scale metabolic modeling. Pan-genome analysis was used to study genomic features of newly isolated M. avium subsp. Paratuberculosis strains, which can cause Johne’s disease in cows. Integration of machine-learning based transcription factor prediction and experimental validation with ChIP-exo enabled investigation of less-studied fraction of transcriptional regulation in bacteria. Lastly, genome-scale metabolic modeling provided explanatory and predictive capabilities for understanding bacterial metabolic at the genome scale, which can be further used in metabolic interactions in bacterial community. -
dc.identifier.bibliographicCitation mBiome 학술대회 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/78683 -
dc.publisher mBiome -
dc.title Systems Toolsets to InvestigateMicrobiome -
dc.type Conference Paper -
dc.date.conferenceDate 2019-12-10 -

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