Session Details

[SS16]Next-Generation Mathematical Biology: Where It Meets Bioinformatics

Fri. Jul 11, 2025 3:00 PM - 4:40 PM JST
Fri. Jul 11, 2025 6:00 AM - 7:40 AM UTC
Room 02
Chair:Shingo Iwami(Nagoya University, Japan), Eiryo Kawakami(Chiba University, Japan)
Many biological phenomena are described within the framework of population dynamics, through a combination of elements such as proliferation, differentiation, infection, mutation, evolution, and adaptation, along with temporal evolution. Classical mathematical biology has long used mathematical models and computer simulations to explain and understand these dynamics. However, advancements in technology, such as next-generation flow cytometers and sequencers, now allow for comprehensive measurement of diverse cellular and molecular states. Furthermore, the integration of statistical science and machine learning methods with mathematical models, facilitated by improvements in computing power, has furthered quantitative understanding of population dynamics. The revival of "on-the-bench mathematical models," previously developed and debated, now as "practical mathematical models," alongside advances in measurement technologies and information statistics, marks the beginning of a new era in mathematical biology. In this symposium, we aim to share cutting-edge research results as concrete examples, with a focus on the integrated approach of mathematical models, computer simulations, bioinformatics, and artificial intelligence, to discuss the future of next-generation mathematical biology.

[SS16-01]Simulating Embryonic Development: A Collective Cellular Society

*Nika Shakiba1,2 (1. University of British Columbia (Canada), 2. University of Osaka (Japan))

[SS16-02]RNA landscape in single cells

*Yusuke Imoto1 (1. Kyoto University (Japan))

[SS16-03]Structural robustness and temporal vulnerability of the starvation-responsive metabolic network in liver of healthy and obese mice

*Keigo Morita1, Atsushi Hatano1,2,3, Toshiya Kokaji1,4, Hikaru Sugimoto1, Takaho Tsuchiya5, Haruka Ozaki5, Riku Egami1, Dongzi Li1, Akira Terakawa1, Satoshi Ohno1,6, Hiroshi Inoue7, Yuka Inaba7, Yutaka Suzuki1, Masaki Matsumoto2, Masatomo Takahashi8, Yoshihiro Izumi8, Takeshi Bamba8, Akiyoshi Hirayama9, Tomoyoshi Soga9, Shinya Kuroda1 (1. University of Tokyo (Japan), 2. Niigata University (Japan), 3. RIKEN Center for Integrative Medical Sciences (Japan), 4. Nara Institute of Science and Technology (Japan), 5. University of Tsukuba (Japan), 6. Science Tokyo (Japan), 7. Kanazawa University (Japan), 8. Kyushu University (Japan), 9. Keio Univesity (Japan))

[SS16-04]Deep distributed computing for clustering extremely large datasets

*Nozomu Yachie1,2 (1. The University of British Columbia SBME (Canada), 2. Osaka University PRIMe (Japan))