2005 B.S., Biotechnology, Yonsei University, Seoul, Korea
2007 M.S., Chemical & Biomolecular Engineering, KAIST, Daejeon, Korea
2011 Ph.D., Chemical & Biomolecular Engineering, KAIST, Daejeon, Korea
Employment and Professional Experience
2014 - 2016 Visiting Senior Researcher, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
2013 - 2018 Research Assistant Professor / Research Fellow, BioInformatics Research Center, KAIST, Daejeon, Korea
2011 - 2013 Postdoctoral Researcher, BioInformatics Research Center, KAIST, Daejeon, Korea
2005 - 2011 Graduate Research Assistant, Metabolic and Biomolecular Engineering Laboratory (Prof. Sang Yup Lee), Dept. Chemical & Biomolecular Eng., KAIST, Daejeon, Korea
2004 Undergraduate Research Assistant, Biochemical Engineering Laboratory (Prof. Dewey Ryu), University of California, Davis, CA, USA
2003 - 2004 Undergraduate Research Assistant, Center for Neuroscience (Prof. Noelle L’Etoile), University of California, Davis, CA, USA
Awards and Honors
2016 The Best National R&D Achievement in 2016, Ministry of Science, ICT and Future Planning, Republic of Korea
2010 Best Paper Award, 2010 Korean Society for Biotechnology and Bioengineering (KSBB) Spring Meeting
2010 Young Pasteurian Award, Institut Pasteur Korea (Sponsored by The French Embassy and Ministry of Education, Science and Technology)
2007 Best Poster Award, 2007 Korean Society for Biotechnology and Bioengineering (KSBB) Fall Meeting
2007 Best Paper Award, 2007 Korean Society for Biotechnology and Bioengineering (KSBB) Spring Meeting
2003 High Honor Student, Yonsei University
2002 Honor Student, Yonsei University
Editorial Board Member, BMC Biomedical Engineering
Selected Journal Publications
1 Equal contribution; * Co-correspondence
46. Ryu JY1, Kim HU1 & Lee SY. Deep learning improves prediction of drug-drug and drug-food interactions. Proceedings of the National Academy of Sciences U S A (PNAS) 115, E4304-E4311 (April, 2018)
43. Ryu JY1, Kim HU1 & Lee SY. Framework and resource for more than 11,000 gene-transcript-protein-reaction associations in human metabolism. Proceedings of the National Academy of Sciences U S A (PNAS) 114, E9740-E9749 (November, 2017)
36. Choi S1, Kim HU1, Kim TY & Lee SY. Systematic engineering of TCA cycle for optimal production of a four-carbon platform chemical 4-hydroxybutyric acid in Escherichia coli. Metabolic Engineering 38, 264-273 (November 2016)
35. Kim HU*, Charusanti P, Lee SY & Weber T*. Metabolic engineering with systems biology tools to optimize production of prokaryotic secondary metabolites. Natural Product Reports 33, 933-941 (July 2016)
33. Lee SY & Kim HU. Systems strategies for developing industrial microbial strains. Nature Biotechnology 33, 1061-1072 (October 2015)
28. Kim HU, Ryu JY, Lee JO & Lee SY. A systems approach to traditional oriental medicine. Nature Biotechnology 33, 264-268 (March 2015)
27. Kim TY1, Park JM1, Kim HU1, Cho KM* & Lee SY*. Design of homo-organic acid producing strains using multi-objective optimization. Metabolic Engineering 28, 63-73 (March 2015)
16. Kim HU1, Kim TY1* & Lee SY*. Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network. BMC Systems Biology 5(Suppl 2):S14 (December 2011)
14. Kim HU1, Kim SY1, Jeong H, Kim TY, Kim JJ, Choy HE, Yi KY, Rhee JH* & Lee SY*. Integrative genome-scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery. Molecular Systems Biology 7, 460 (January 2011)
13. Kim TY1, Kim HU1 & Lee SY. Metabolite-centric approaches for the discovery of antibacterials using genome-scale metabolic networks. Metabolic Engineering 12(2), 105-111 (March 2010)
11. Kim HU, Kim TY & Lee SY. Genome-scale metabolic network analysis and drug targeting of multi-drug resistant pathogen Acinetobacter baumannii AYE. Molecular BioSystems 6(2), 339-348 (February 2010)
6. Kim HU, Kim TY & Lee SY. Metabolic flux analysis and metabolic engineering of microorganisms. Molecular BioSystems 4, 113-120 (February 2008)
Systems Biology and Medicine Laboratory (SBML)
Research at SBML is mainly concerned with facilitating drug discovery/development and improving individual’s health using systems biology approaches. Representative studies include:
- Constructing biological network models (metabolism, signaling and transcriptional regulations) of medically important biological systems for better understanding and engineering. Relevant target systems include (both normal and diseased) human cells, microbial pathogens and natural product-producing microorganisms.
- Predicting effective drug targets for both infectious and chronic diseases using biological network models.
- Developing data-driven platform technologies that help facilitate drug discovery/development and improve individual’s health.
To better understand and engineer a biological network, we use a wide range of systems biology approaches, including, but not limited to, genome-scale metabolic modeling, machine learning, bioinformatics, cheminformatics and software development technologies. Large-scale biological datasets serve as important inputs to our strategies. We also have an extensive research collaboration network to achieve our research objective.
This research field corresponds to biotechnology, systems biology, systems medicine and systems healthcare.