Laboratory

Laboratory of Mathematical Biology

Understanding living systems through mathematics
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

Staff

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

Contact

Address

7th floor, Science Building No 2, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, JAPAN

Phone
+81 11-706-2774 (ext. 2774)
E-mail
snakaoka*sci.hokudai.ac.jp (Please replace * with @ when sending e-mail.)

Representative Publications

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
https://researchers.general.hokudai.ac.jp/profile/en.f2db12884fc8303c520e17560c007669.html