Laboratory of Mathematical Biology
- Research Theme
- mathematical modeling and data analysis in life science
- Research Keywords
mathematical biology, systems biology, bioinformatics, microbial ecology, precision medicine, multi-omics, mathematical modeling, machine learning, big data analysis
Overview of Research and Education
In our laboratory, mathematical and statistical models are employed to work with experimental or field biologists in order to understand biological phenomena of interest, or quantitatively characterize data. Primary research target of our laboratory is to develop ecomics and ecomimetics. The former represents a type of research to investigate a given microbial community by integrating omics data on the basis of community ecology theories and methodologies. While the latter is motivated by biomimetics to represent a type of study to extract the essence of community assembly rules from existing microbial ecosystem to construct a new artificial microbial community. Other related topics in our laboratory include omics data analysis in precision medicine or the development of novel mathematical tools for data analysis in life sciences.
Laboratory of Mathematical Biology (nakaoka2018)
- Charge (US):
School of Science, Biological Science course (Macromolecular Functions), Core Laboratories
- Charge (GS):
Graduate School of Life Science, Division of Life Science, Transdisciplinary Life Science Course, Bioinformation and Molecular Sciences
7th floor, Science Building No 2, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, JAPAN
- +81 11-706-2774 (ext. 2774)
- snakaoka*sci.hokudai.ac.jp (Please replace ＊ with @ when sending e-mail.)
Shinji Nakaoka, Data-driven mathematical modeling of microbial community dynamics, Handbook of Statistics Vol.39 Part A, pp.93-130 (2018).
Yamaguchi, R., Yamanaka, T. and Liebhold, A. M. 2019. Consequences of hybridization during invasion on establishment success. Theoretical Ecology Springer. 12: 197-205.
Akane Hara, Shoya Iwanami, Yusuke Ito, Tomoyuki Miura, Shinji Nakaoka, Shingo Iwami, Revealing uninfected and infected target cell dynamics from peripheral blood data in highly and less pathogenic simian/human immunodeficiency virus infected Rhesus macaque, Journal of Theoretical Biology, 479, 29-36 (2019).
Shinji Nakaoka and Keita Matsuyama, Information and statistical analysis pipeline for high-throughput RNA sequencing data, Springer protocol Epidermis, pp.1-10 (2019).
Shinji Nakaoka and Keisuke H. Ota, An information and statistical analysis pipeline for microbial metagenomic sequencing data, Handbook of Statistics Vol.43(2020).
Refer to HOKKAIDO UNIVERSITY RESEARCHERS DIRECTORY