Laboratory for Disease Systems Modeling

Disease Systems Modeling Group is responsible for systems biology research and software platform development within Integrative Medical Sciences Center of RIKEN. The group will investigate systems-level principles behind diseases, develop multi-layer physiological models and detailed cellular and molecular interaction models that will be verified through experiments. These basic studies will be quickly applied for drug discovery and personalized medicine. In addition, a series of research will be carried out for novel computational analysis of models as well as efficient model development. Such approaches include, but limited to, text-mining, qualitative reasoning, machine learning, and other AI-related technique.

The group maintains "wet" laboratory capabilities including gTOW-6000 budding yeast quantitative over-expression assay system and a range of mammalian cell-based experimental facilities. Computational models can be verified through experiments. Furthermore, we are planning to start a novel approach to cellular-reprograming targeting iPS cell, direct reprograming, and potential other approaches.

Our goal is to accomplished scientific breakthrough and technology innovations and quickly turn them into practice and business in the real world, particularly in the field of drug discovery, personalized medicine, and a broad context of regenerative medicine leveraging our strength in systems-oriented approach.

Research Fields

Engineering / Biology & Biochemistry / Molecular Biology & Genetics / Immunology / Pharmacology & Toxicology / Microbiology / Computer Science / Mathematics / Multidisciplinary

Research Subjects

• Systems biology of auto-immune diseases and atopic dermatitis
• Systems drug discovery and related software platform development
• Systems biology of cellular reprograming

Publications

1. Kitano, H.
   "Systems Biology: A Brief Overview"
   Science 295, 1662-1664 (2002).
2. Kitano, H.
   "Computational systems biology"
   Nature 420, 206-210, (2002).
3. Kitano, H.
   "Biological Robustness"
   Nature Reviews Genetics 5, 826-837, (2004).
4. Kitano, H.
   "Cancer as a robust system: implications for anticancer therapy"
   Nature Reviews Cancer 4, 227-235, (2004).
5. Kitano, H.
   "A robustness-based approach to systems-oriented drug design"
   Nature Reviews Drug Discovery, 6, 202-210, (2007).
6. Kitano, H., Ghosh, S., Matsuoka, Y.
   "Social engineering for virtual 'big science' in systems biology"
   Nature Chemical Biology 7, 323-326, (2011).
7. Ghosh, S., Matsuoka, Y., Asai, Y., Hsin, K-Y., Kitano, H.
   "Software for systems biology: from tools to integrated platforms"
   Nature Reviews Genetics 12, 821-832, (2011).
8. Kitano, H., Funahashi, A., Matsuoka, Y., Oda, K.
   "Using process diagrams for the graphical representation of biological networks"
   Nature Biotechnology 23, 961-966, (2005)
9. Le Novere, N., Hucka, M., Mi, H., Moodie, S., Schreiber, F., Sorokin, A., Demir, E., Wegner, K, Aladjem, I.M., Wimalaratne, S. M., Bergman, F. T., Gauges, R., Ghazal, P., Kawaji, H., Li, L., Matsuoka, Y., Villéger, A., Boyd, S. E., Calzone, L., Courtot, M., Dogrusoz, U., Freeman, T. C., Funahashi, A., Ghosh, S., Jouraku, A., Kim, S., Kolpakov, F., Luna, A., Sahle, S., Schmidt, E., Watterson, S., Wu, G., "Goryanin, I., Kell, D. B., Sander, C., Sauro, H., Snoep, J. L., Kohn, K., Kitano, H.
   The Systems Biology Graphical Notation"
   Nature Biotechnology 27, 735-741, (2009)
10. Moriya, H., Yoshida, Y., Kitano, H.
    "In Vivo Robustness Analysis of Cell Division Cycle Genes in Saccharomyces cerevisiae"
    PLoS Genetics 2, (2006).