Mathematical Biology Laboratory Department of Biology, Kyushu University

MEMBER

Shingo Iwami

NAME

Shingo Iwami

Email: siwami[at]kyushu-u.org
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CURRENT POSITION

Associate Professor, Department of Biology, Kyushu University

EDUCATION

  • B.S. Osaka Prefecture University, Japan, 2005 (Mathematics and Physics)
  • M.S. Osaka Prefecture University, Japan, 2007 (Mathematical Biology)
  • Ph.D. Shizuoka University, Japan, 2009 (Mathematical Biology)

PROFESSIONAL EXPERIENCE

  • Japan Society for the Promotion of Science for Young Scientists, DC1 (2007.4-2009.3)
  • Japan Society for the Promotion of Science for Young Scientists, PD (2009.4-2009.9)
  • Japan Science and Technology Agency, PRESTO Researcher (2009.10-2013.3)
  • Japan Science and Technology Agency, PRESTO Researcher (2014.10-2018.3)
  • INSERM, U941, Paris, France, Visiting Professor (2015.10-2016.3)
  • Kyushu University, Associate Professor (2011.11-Current)
  • Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Collaborative researcher (2020.4-Current)
  • NEXT-Ganken Program, Japanese Foundation for Cancer Research (JFCR), Visiting Researcher (2020.4-Current)

RESEARCH INTERESTS

Quantitative Population Dynamics during course of life - from birth to death

Through the course of life, from the moment of birth till death, an organism will achieve various states of equilibrium or ‘homeostasis’ which will inevitably encounter perturbations. The processes of cell growth, differentiation, infection, mutation, evolution and adaptation work together as a coordinated ‘system’, described by mathematical models for population dynamics, to maintain a healthy state. Any disruptions to this system leads to disease including infection, allergy, cancer, and aging. I am conducting interdisciplinary research to elucidate “Quantitative Population Dynamics” through the course of life with original mathematical theory and computational simulation, which are both our CORE approach. Our mathematical model-based approach has quantitatively improved a current gold-standard approach essentially relying on the statistical analysis of “snapshot data” during dynamic interaction processes in life sciences research. Integrating current high-throughput technics including next-generation sequencer and mathematical sciences, we would like to make a paradigm shift in future life sciences research. Our developing approach could be applied to population dynamics of virus infections, immune system (e.g. differentiation process from hematopoietic stem cell or other specific immune cell) and to other aspects of cancer progression in terms of quantitative understandings for complex life phenomena including different time-scales and multi-layer data.

SELECTED PUBLICATIONS

  • M. Mahgoub, J. Yasunaga, S. Iwami, S. Nakaoka, Y. Koizumi, K. Shimura, and M. Matsuoka. Sporadic on/off switching of HTLV-1 Tax expression is crucial to maintain the whole population of virus-induced leukemic cells, Proceedings of the National Academy of Sciences of the United States of America, 115(6):E1269-E1278 (2018).
  • Y. Koizumi, H. Ohashi, S. Nakajima, Y. Tanaka, T. Wakita, AS. Perelson, S. Iwami†, and K. Watashi†. Quantifying antiviral activity optimizes drug combinations against hepatitis C virus infection, Proceedings of the National Academy of Sciences of the United States of America. 114:1922-1927 (2017). (†Equal contribution)
  • A. Martyushev, S. Nakanoka, K. Sato, T. Noda†, and S. Iwami†. Modelling Ebola virus dynamics: Implications for therapy, Antiviral Research. 135:62-73 (2016). (†Equal contribution)
  • S. Iwami†, JS Takeuchi†, S Nakaoka, F Mammano, F Clavel, H Inaba, T Kobayashi, N Misawa, K Aihara, Y Koyanagi, K Sato. Cell-to-cell infection by HIV contributes over half of virus infection, Elife. 4, (2015). (†Equal contribution)
  • S. Iwami†, BP. Holder, CA. Beauchemin, S. Morita, T. Tada, K. Sato, T. Igarashi, and T. Miura. Quantification system for the viral dynamics of a highly pathogenic simian/human immunodeficiency virus based on an in vitro experiment and a mathematical model, Retrovirology. 9: 18 (2012).

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