Laboratory of Systems Biology and Medicine

daehee hwang

Prof. Hwang, Daehee

Professor, Group Leader, Institute for Basic Science;


Phone: 82-53-785-1840,


Hwang Lab HomePage



Systems biology is the study of network operations that give rise to functions of a system via the interactions among molecular components in the system. For example, the network below shows transcriptional regulation (bottom), metabolic regulation (top left), and protein/signaling regulation (top left) after treatment of galactose in yeast (Hwang et al., 2005, PNAS, 102, 17302-17307).


Systems biology approaches involve the following three cardinal features:

1. Global profiling using high-throughput technologies: Global data that represent cellular states relevant to given problems are first produced. These data include DNA sequences (SNVs, CGHs, and fusion genes), epigenetic data (histone modifications and DNA methylation), small-RNA, mRNA, protein (abundances, posttranslational modifications, protein interactions, protein localization, etc.), and/or metabolite data.

2. Network modeling: Biological networks (protein and gene regulatory networks) describing key cellular events relevant to given problems are constructed by integrating global datasets available.

3. Identification of key regulatory pathways from the network models: Network-driven hypotheses for mechanisms for key cellular processes are then generated by analyzing the network models, and the hypotheses are experimentally verified.

The success of these approaches are highly dependent on the efficacy of interactions among the three components (biology/medicine, technology, and computation) of the tripod (called the tripod of systems biology): ‘biology/medicine’ provides questions to be answered, ‘technology’ provides diverse types of global data, ‘computation’ generates network models and network-driven hypotheses, and ‘biology/medicine’ experimentally validates the hypotheses. The successful cycle through the three components in the tripod can lead to novel knowledge about the biological or medicinal questions.


Further detailed concepts of systems biology can be found at the institute for systems biology.

Research Summary

1. Multilayered spatiotemporal networks: Complex biological events are commonly regulated by operations of multilayered networks each of which is formed by a different type of regulatory molecules (signaling molecules, transcription factors, micro-RNAs, epigenetic regulators, etc.). For example, the network below shows multiple types of regulators defining multilayered networks for leaf senescence in Arabidopsis (Woo et al., 2013, Journal of Cell Science, 126, 4823-4833). A main research interest in our lab is to develop an experimental and analytical framework to decode multilayered spatiotemporal networks.


A. Time-evolving networks: Through time-course profiling of global data, time-evolving networks can be generated, which show temporal transitions of molecules (nodes) in abundances or activities. For example, the time-evolving networks below show temporal transitions of the genes involved in prion accumulation during the course of prion infection (Hwang et al., 2009, Molecular Systems Biology, 5:252, doi:10.1038/msb.2009.10). Furthermore, the time-evolving networks showing temporal transitions of both nodes and edges are being developed.


B. Spatial networks: Proteins involve spatial transitions during cellular events. Moreover, the same proteins can be localized in multiple organelles, forming different networks by interacting different sets of molecules in distinct organelles. Combining quantitative and organelle proteomics approaches, spatial networks can be identified. For example, the networks below show a spatial network encoded by nuclear and cytosolic GIGENTEA (GI), regulates circadian rhythm in Arabidopsis (Kim et al., 2013, Developmental Cell, 26, 1−13).


By combining genomic (NGS-sequencing), proteomic (mass spectrometry-based proteomics), computational approaches (network-based data integration), we aim to develop an experimental and analytical framework to decode multilayered spatiotemporal networks for leaf aging in Arabidopsis and Rice.

2. Regulatory motifs or pathways: In complex network models, there are key motifs or pathways that can govern activities of the network models. These motifs or pathways can be identified using various network analysis tools based on diverse centrality measures (e.g. degree and betweeness centralities, as well as random walk-based methods). For example, a transcriptional regulatory pathway to govern down-regulation of the genes involved in oxidative phosphorylation in the presence of a point mutation (A3243G) in the mitochondrial genome (Chae et al., 2013, Science Signaling, 6, 264, doi: 10.1126/scisignal.2003266).


3. Metabolite-protein networks: Metabolites regulate cellular signaling and regulatory networks through their interactions with proteins. Experimental methods involving the conjugation of metabolites to Au-coated magnetic beads coupled with mass spectrometry analysis are being developed to profile the interactors of metabolites involved in a particular metabolic pathway (e.g. ceramide metabolic pathway described in Hannun and Obeid, Nature Reviews Molecular Cell Biology, 9, 139-150).


4. Network stochasticity: Various cellular processes have been shown to be stochastic due to the stochastic nature of molecular interactions, leading to stochasticity in biological networks defining the cellular processes. For example, the expression levels of a GFP reporter coupled to an hsp16 promoter in C. elegans varied widely, even among worms that are genetically identical and grown under the same environmental conditions. The lifespan of worms were predicted by fluctuating GFP expression levels, which suggests that gene expression noise plays an important role in determining the longevity (Rea et al., 2005, Nature Genetics, 37, 8, 894−898). After developing multilayered networks for leaf aging in Arabidopsis, we aim to decode the stochasticity of key regulatory motifs or pathways in the multilayered networks using single cell genomic approaches.

5. Proteomics and informatics: We aim to develop diverse proteomics approaches to monitor posttranslational modifications, localization, and interactomes under different conditions or during cellular events and then to apply them to many biological systems. Also, we have been developing various bioinformatics tools for 1) analyses of genomic, transcriptomic, proteomic, and metabolomic data, 2) integration of multiple types of global datasets (e.g. transcriptomic- proteomic datasets or epigenomic-transcriptomic datasets), 3) construction of static or dynamic networks describing cellular processes differentially regulated during cellular processes, and 4) identification of key regulators that may govern activities of the network models. For example, a framework was developed to decode a network model (see below) for cancer association of tRNA synthetases (ARS) whose functions in cancer are unknown.


Also, a bioinformatics framework was developed to decode subnetworks each of which describes cellular processes showing principal differential expression patterns and also to construct time-evolving networks when it was applied to time-course gene expression data.


Selected Publications

  • Hwang, D*., I.Y. Lee*, H. Yoo*, N. Gehlenborg, J.H. Cho, B. Petritis, D. Baxter, R. Pitstick, R. Young, D. Spicer, J. Hohmann, S.J. DeArmond, G.A. Carlson, L.E. Hood. A Systems Approach to Prion Disease. Molecular Systems Biology, 5, 252 (2009).
  • H. Yoon, S. You, S. Yoo, N. Kim, S. Choi, H.M. Kwon, C. Cho, D. Hwang*, and W. Kim*. NFAT5 is a critical regulator of inflammatory arthritis. Arthritis & Rheumatism, 63, 7, 1843–1852 (2011).
  • S. Hyung#, M.Y. Lee#, J. Yu#, B. Shin, H. Jung, J. Park, W. Han, H. Zhang, R. Aebersold, D. Hwang*, S. Lee*, M. Yu*, and D. Noh*. A serum protein profile predictive of the resistance to neoadjuvant chemotherapy in advanced breast cancers. Molecular and Cellular Proteomics, 10, 10, M111.011023 (2011).
  • S. Chae*, B.Y. Ahn*, K. Byun*, Y.M. Cho, M. Yu, B. Lee†, D. Hwang†, and K.S. Park†. A systems approach for effective decoding of mitochondrial retrograde signaling pathways. Science Signaling 6, 264 (2013).
  • Y. Kim†, S. Han2†, M. Yeom, H. Kim, J. Lim, J. Cha, W. Kim, D.E. Somers, J. Putterill, H.G. Nam*, and D. Hwang*. Balanced Nucleocytosolic Partitioning Defines a Spatial Network for Coordination of Circadian Physiology in Plants. Developmental Cell, 26, 1, 73-85 (2013).
  • S. You, S. Yoo, S. Choi, J. Kim, S. Park, J.D. Jic, T. Kim, K. Kim, C. Cho, D. Hwang*, W. Kim. Identification of key regulators for the migration and invasion of rheumatoid synoviocytes through a systems approach. Proceedings of the National Academy of Sciences of the United States of America 111, 1, 550-555 (2014).
  • S. Kim*, S. Chae*, H. Kim, D. Mun, S. Back, H.Y. Cho, K.S. Park, D. Hwang#, S.H. Choi#, S. Lee#. A protein profile of visceral adipose tissues linked to early pathogenesis of type 2 diabetes mellitus. Molecular & Cellular Proteomics (accepted for publication) (2014).
  • B.K. Koo*, S. Chae*, K. Kim*, M.J. Kang*, E.G. Kim, S. Kwak, H. Jung, Y.M. Cho, S.H. Choi, Y.J. Park, C.H. Shin, H.C. Jang, C.S. Shin, D. Hwang†, E.C. Yi†, K. Park†. Identification of Novel Auto-antibodies in Type 1 Diabetic Patients using a High-density Protein Microarray. Diabetes (accepted for publication) (2014).
  • S-Y. Kook, H. Jeong, M.J. Kang, R. Park, H.J. Shin, S. Han, S.M. Son, H. Song, S.H. Baik, M. Moon, E.C. Yi, D. Hwang*, and I. Mook-Jung*. Crucial role of calbindin-D28k in the pathogenesis of Alzheimer’s disease mouse model. Cell Death and Differentiation (accepted for publication) (2014).
  • Y. Kim, J. Jang, S. Choi* and D. Hwang*. TEMPI: Probabilistic modeling Time-Evolving differential PPI net-works with MultiPle Information. Bioinformatics, ECCB spatial edition (accepted for publication) (2014).
  • B. Bin, S. Hojyo, T. Hosaka, J. Bhin, H. Kano, T. Miyai, M. Ikeda, T. Kimura‐Someya, M. Shirouzu, E. Cho, K. Fukue, T. Kambe, W. Ohashi, K. Kim, J. Seo, D. Choi, Y. Nam, D. Hwang, A. Fukunaka, Y. Fujitani, S. Yokoyama, A. Superti‐Furga, S. Ikegawa, T.R. Lee, T. Fukada. Molecular pathogenesis of Spondylocheirodysplastic Ehlers‐Danlos syndrome caused by mutant ZIP13 proteins. EMBO Molecular Medicine 6, 8, 1028-1042 (2014).
  • Kim H.P., Han S.W., Song S.H., Jeong E.G., Lee M.Y., Hwang D., Im S.A., Bang Y.J., Kim T.Y. Testican-1-mediated epithelial-mesenchymal transition signaling confers acquired resistance to lapatinib in HER2-positive gastric cancer. Oncogene 33, 25, 3334-41 (2014).
  • M.J. Kang#, Y. Park#, S. You#, S. Yoo, S. Choi, D. Kim, C. Cho, E.C. Yi*, D. Hwang*, and W. Kim*. A urinary proteome profile predictive of disease activity in rheumatoid arthritis. Journal of Proteome Research 13, 11, 5206-5217 (2014).
  • H. Park, J. Bae, H. Kim, S. Kim, H. Kim, D. Mun, Y. Joh, W. Lee, S. Chae, S. Lee, H.K. Kim, D. Hwang*, S. Lee*, and E. Paek*. A compact variant-rich customized sequence database and a fast and sensitive database search for efficient proteogenomic analyses. Proteomics 14, 2742-2749 (2014).
  • S.H. Choi, D.Y. Hyeon, I.H. Lee, S.J. Park, S. Han, I.C. Lee, D. Hwang*, and H.G. Nam*. Gene duplication of type-B ARR transcription factors systematically extends transcriptional regulatory structures in Arabidopsis. Scientific Report 4, 7197 (2014).
  • N. Lee, S.Y. You, W. Lee, M.S. Shin, K.S. Kang, S.H. Kim, A.C. Shaw, G.L. Chupp, P. Lee, D. Hwang and I. Kang. IL-6 receptor α defines effector memory CD8+ T cells producing Th2 cytokines and expanding in asthma. American Journal of Respiratory and Critical Care Medicine 190, 12, 1383-1394 (2014).
  • K. Lee, B. Kim, J. Bhin, D. Kim, H. You, E. Kim, S. Kim, D. Hwang, W. Lee*. Bacterial uracil modulates Drosophila DUOX-dependent gut immunity via Hedgehog-induced signaling endosomes. Cell Host & Microbe 17, 2, 191-204 (2014).
  • Yoo S., Park J., Hwang S., Oh S., Lee S., Cicatiello V., Rho S., De Falco S., Hwang D., Cho C., Kim W. PlGF-1 and -2 induce hyperplasia and invasiveness of primary rheumatoid synoviocytes. Journal of Immunology (accepted for publication/doi:10.4049/jimmunol.1402900) (2015).
  • Y. Kim, S. Lee, J. Park, M. Kim, B. Lee, D. Hwang, and S. Chang. ARF6 bi-directionally regulates dendritic spine formation depending on neuronal maturation and activity. Journal of Biological Chemistry (accepted for publication/doi: 10.1074/jbc.M114.634527) (2015).
  • J.H. Yoon, D. Kim, J. Jang, J. Ghim, S. Park, P. Song, Y. Kwon, D. Hwang, Y. Bae, P. Suh, P. Berggren, and S.H. Ryu*. Proteomic analysis of the palmitate- induced myotube secretome reveals involvement of the annexin A1-FPR2 pathway in insulin resistance. Molecular & Cellular Proteomics (accepted for publication) (2015).
  • K. Boo, J. Bhin, Y. Jeon, J. Kim, H.R. Shin, J. Park, K. Kim, C.Rok. Kim, H. Jang, I. Kim, V.N. Kim, D. Hwang*, H. Lee*, and S.H. Baek*. Pontin functions as an essential coactivator for Oct4-dependent lincRNA expression in mouse embryonic stem cells. Nature Communications (accepted for publication) (2015).
  • J. Lee*, S. Yoo*, D.Y. Hyeon, B. Kang, H. Kim, K.M. Park, B. Han, D. Hwang#, and S. Kim#. Comprehensive data resources and analytical tools for pathological association of aminoacyl tRNA synthetases with cancer. Database (accepted for publication) (2015).
  • Kim E., Park S., Choi N., Lee J., Yoe J., Kim S., Jung H.Y., Kim K.T., Kang H., Fryer J.D., Zoghbi H.Y., Hwang D., and Lee Y. Deficiency of Capicua disrupts bile acid homeostasis. Scientific Reports, 5, 8272 (2015).
  • Koo Y.D., Choi J.W., Kim M., Chae S., Ahn B.Y., Kim M., Oh B.C., Hwang D., Seol J.H., Kim Y., Park Y.J., Chung S.S., and Park K.S. SUMO-specific protease 2 (SENP2) is an important regulator of fatty acid metabolism in skeletal muscle. Diabetes (accepted for publication) (2015).
  • BMP9 induces cord blood derived endothelial progenitor cell differentiation and ischemic neovascularization via ALK1 (submitted) (2015).