KOMATSUZAKI, Tamiki

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KOMATSUZAKI, Tamiki
Principal Investigator
Hokkaido University
KOMATSUZAKI, Tamiki Group
Principal Investigator
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    KOMATSUZAKI, Tamiki
Faculty Members
Postdoctoral Fellows
Research Collaborators
Staff
  • ISHIDA, Maiko

About the Research

Research Theme

Mathematical modeling of macromolecule systems and data-driven science

Keyword

Data Driven Mathematical Science, Chemical Reaction Kinetics and Dynamics, Biological Physics, Nonlinear Physics, Statistical Physics

Research Outline

Can we predict, from a static picture, whether a chemical reaction will take place or not? Another way to phrase this question is whether chemical reactions are purely stochastic in nature or whether they are not rather predictable, after taking into account the dynamical aspects of the system in addition to the energy landscape, even if some small deviation is allowed.

This can help solve many unresolved questions where reactions deviate from the purely static standard model. Our theory, rooted in language from dynamical systems theory, on the other hand is not limited to applications in chemistry, and as such has also been used to design the Genesis space probe routes. This indicates that the concept behind our description of chemical reactions is very universal.

A new problem arises if the system becomes so complex that we cannot anymore assign equations of motion, for example when looking at the chemical reactions in a cell. Nevertheless, the system may still follow some predetermined path characteristic of such complex reacting systems. The challenge is how we can learn about the underlying guiding principle based on the only information we have: the experimental data.

The Researcher’s Perspective

“Nature does not know any boundary of science” (by Prof. Kenichi Fukui, the first Japanese Nobel laureate in Chemistry) is the pivotal motto for my research. As soon as we classify our research into distinct fields, our views may become narrower. However, ICReDD is a project where people from different research areas meet and are forced to phrase their work into simpler terms. I expect that this will enable us to gain new insights and approaches into our common topic: designing and discovering chemical reactions.

For details on MANABIYA course topics, please follow this link. To learn more about MANABIYA in general, please click here.

Representative Research Achievements

  • FRET monitoring of a nonribosomal peptide synthetase
    J. Alfermann, X. Sun, F. Mayerthaler, T.E. Morrell, E. Dehling, G. Volkmann, T. Komatsuzaki, H. Yang, H.D. Mootz, Nat. Chem. Bio., 2017, 13, 1009-1015
    DOI : 10.1038/nchembio.2435 
  • ATP Hydrolysis Assists Phosphate Release and Promotes Reaction Ordering in F1-ATPase
    C.-B. Li, H. Ueno, R. Watanabe, H. Noji, T. Komatsuzaki, Nat. Commun., 2015, 6, 10223 (9 pages)
    DOI : 10.1038/ncomms10223
  • Dynamical Switching of a Reaction Coordinate to Carry the System Through to a Different Product State at High Energies
    H. Teramoto, M. Toda, T. Komatsuzaki, Phys. Rev. Lett., 2011, 106, 054101
    DOI : 10.1103/PhysRevLett.106.054101
  • Robust Existence of a Reaction Boundary to Separate the Fate of a Chemical Reaction
    S. Kawai, T. Komatsuzaki, Phys. Rev. Lett., 2010, 105, 048304 (4 pages)
    DOI : 10.1103/PhysRevLett.105.048304
  • Multiscale Complex Network of Protein Conformational Fluctuations in Single Molecule Time Series
    C.-B. Li, H. Yang, T. Komatsuzaki, Proc. Natl. Acad. Sci. U.S.A., 2008, 105, 536-541
    DOI : 10.1073/pnas.0707378105

Publications

2020

  • A Bad Arm Existence Checking Problem: How to Utilize Asymmetric Problem Structure?
    K. Tabata, A. Nakamura, J. Honda, T. Komatsuzaki, Machine Learning, 2020, 109, 327-372
    DOI: 10.1007/s10994-019-05854-7

2019

  • Raman Spectroscopic Histology Using Machine Learning for Nonalcoholic Fatty Liver Disease
    KM. Helal, JN. Taylor, H. Cahyadi, A. Okajima, K. Tabata, Y. Itoh, H. Tanaka, K. Fujita, Y. Harada, T. Komatsuzaki, Febs Letters, 2019, 593, 2535-2544
    DOI: 10.1002/1873-3468.13520
  • High-Resolution Raman Microscopic Detection of Follicular Thyroid Cancer Cells with Unsupervised Machine Learning
    JN. Taylor, K. Mochizuki, K. Hashimoto, Y. Kumamoto, Y. Harada, K. Fujita, T. Komatsuzaki, J. Phys. Chem. B, 2019, 123, 4358-4372
    DOI: 10.1021/acs.jpcb.9b01159