Why Systems Biology / Quantitative Biology?

Software Infrastructure

Software infrastructure is one of the most crucial components in systems biology research. This project will focus on developing high-performance simulation libraries and image processing algorithms.

Mathematical Modeling

Mathematical modeling is a practical approach to understanding biological phenomena as a system because it can capture the essence of its dynamics. For example, we are currently building mathematical models to understand the early development of C.elegans.

Machine Learning

Machine learning techniques, exemplified by "deep learning," are now known to be powerful tools. We are developing new image processing algorithms based on deep learning.

Data Analysis

Systems identification from given high-throughput data is another interesting topic in systems biology. This project focuses on inferring gene regulatory networks from time-series RNA-seq data.


Live-cell imaging is an essential component of mathematical modeling because it allows acquiring quantitative time-series data. We have developed methods for measuring intracellular temperature differences, quantifying intracellular crowding, and cell tracking methods. We are currently working on its application.


Precise experimental systems play an essential role in the quantitative understanding of biological phenomena. Therefore, we are developing automated experimental systems using microfluidic devices.

Case study

“ Researchers from Keio University, Kindai University, Sanyo-Onoda University, and the University of Tokyo recently collaborated on a project that applied deep learning to a biological use case. Specifically, they applied 3D convolutional neural network segmentation to extract quantitative criteria of the nucleus during mouse embryogenesis. They recently published their findings in Systems Biology and Applications. ”
“ It could one day enable medical workers to make more objective assessments on human egg quality, a factor related to infertility, and could help improve in vitro fertilization pregnancy rates, according to a team of researchers from Keio University, Kindai University and other institutions. ”
“ AI使い受精卵を解析、体外受精の妊娠率向上に期待 ”
“ 不妊治療につながるAI開発に成功、発生過程の細胞核を世界最高精度で評価 ”
“ AI用いて細胞数測定を高精度・効率化 スマホ撮影で可能に 慶大 ”
“ COVID-19攻略に向け分子地図プロジェクトがスタート 治療薬開発の情報基盤構築に世界163人の研究者が参加 ”
“ 理化学研究所、「乾燥しても死なない細胞」の死の回避システムスイッチON!”
“ 細胞が移動する方向を予測するAIを開発、がんの予後診断に期待 ”
“ 学校法人慶應義塾大学、乾燥しても死なない細胞はなぜ死なずに生き返ることができるのか? - Pv11細胞の乾燥耐性および再水和復活メカニズムの示唆 - ”
“ 理研・慶大など、乾燥しても死なない細胞はなぜ死なずに生き返ることができるのか解明 ”
“ 慶応大、SBMLに完全準拠した待望の生化学シミュレータの開発に成功 ”

Try the software tools we have developed.

Open Source

Most of the software we develop is released as open-source software. We encourage you to try them out and give us feedback.

Quality designs

Most of our publicly available software is based on research results published in papers. Our software has been rigorously peer-reviewed to ensure that it is novel and valuable.
Visit our GitHub repository


We harness the power of computation and mathematics to tackle the mysteries of biology.