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Saito Lab (Molecular Functional Informatics Division, AIST)
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The lab uses machine learning to interpret biological data and applies the findings to the design of proteins and other biomolecules.
AboutAbout the lab
Bioinformatics for Bioengineering
Saito Lab studies life by analyzing large-scale data with methods from information science, such as machine learning and bioinformatics.
The lab aims to apply the resulting insights to the design and control of biomolecules such as proteins.
Recently, the lab has also been working on automating biological experiments using laboratory robots.
The lab spans Kitasato University, the National Institute of Advanced Industrial Science and Technology (AIST), and the University of Tokyo.
Key terms
- Bioinformatics
- The field of analyzing large-scale data on organisms, such as DNA and proteins, with computers
- Bayesian optimization
- A machine learning method that uses probability to estimate which candidate to try next based on past results, and finds the best conditions with few trials
- Omics
- Data obtained by comprehensively measuring molecular information inside cells, such as the genome and DNA methylation, and the analysis of such data. In Japanese, the term is also written with a short first vowel.
NewsLab news
Paper selected for the Excellent Paper Award of the Journal of Bioscience and Bioengineering
A student received the Excellent Research Award from the Department of Computational Biology and Medical Sciences
A student's first-author paper accepted by Computational and Structural Biotechnology Journal
A student's first-author paper accepted by Epigenetics & Chromatin
A student's first-author paper accepted by Briefings in Bioinformatics
ResearchResearch themes
The lab aims to understand life from large-scale data using methods from information science and to apply that knowledge to the design and control of biomolecules. It works on designing biomolecules with machine learning and on developing techniques for analyzing omics data.
*Illustrative imageDesigning biomolecules with machine learning
The lab developed a method that uses Bayesian optimization to predict mutations that enhance protein function, and, in collaboration with experimental labs, discovered a new yellow fluorescent protein. It also works on designing mRNA and promoter sequences.
*Illustrative imageTechniques for analyzing omics data
The lab develops methods for discovering knowledge from data obtained with high-throughput sequencers, such as DNA methylation and the three-dimensional structure of the genome, and validates them in collaboration with experimental labs. It also analyzes methylation in bacteria and fungi.
MembersFaculty
The members of Saito Lab consist of faculty, Japan Society for the Promotion of Science (JSPS) Research Fellows, and doctoral and master's students.As of 2026
- Visiting Associate ProfessorYutaka Saito
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Automated pipetting system using manual pipettes” Pzucchel / CC BY-SA 3.0 (cropped)
- Designing biomolecules with machine learning: “E.coli plate dark background” LadaSy / CC BY 4.0 (cropped)
- Techniques for analyzing omics data: “Four 3-day old Aspergillus colonies on a Petri dish” Adrian J. Hunter / CC BY-SA 3.0 (cropped)
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