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Ohno Lab
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A lab that uses AI and mathematics to decipher mechanisms from large-scale biological data.
AboutAbout the lab
Decoding mechanisms from large-scale biological data
The Life Systems Science Lab (Ohno Lab) aims to create new mathematical frameworks for understanding the essence of living systems from large-scale biological data.
By combining the strong descriptive power of artificial intelligence (AI) with the logical rigor of systems biology, the lab is developing broadly applicable analysis technologies.
The lab works on projects such as single-cell metabolic modeling, integrated multi-omics analysis, understanding of disease states, and diagnostic and predictive AI using medical data.
The Ohno Lab is a new lab launched in January 2026, and it is recruiting undergraduate and graduate students from diverse backgrounds, including biology, medicine, information science, and the mathematical and physical sciences.
Key terms
- Single-cell
- Measuring and analyzing cells one by one, distinguishing each individual cell. It makes it possible to study differences among individual cells within a population
- Metabolic flux
- The rate at which metabolic reactions proceed inside a cell (reaction rate). It is an indicator of the metabolic state
- Multi-omics
- Combining comprehensive molecular datasets of different types, such as genes, proteins, and metabolites
NewsLab news
A paper revealing the robustness of liver metabolism has been published in iScience (joint research with the University of Tokyo)
Associate Professor Ohno received the Outstanding Poster Presentation Award at IIBMP2026
A paper on estimating metabolic flux by cell type has been submitted to bioRxiv
Associate Professor Satoshi Ohno took up his post, and the lab was launched
ResearchResearch themes
Inside cells, many kinds of molecules, such as DNA, RNA, proteins, and small compounds, interact with one another to form a huge and precise system. Aiming to create new mathematical frameworks for understanding the essence of living systems from large-scale data, the lab develops analysis technologies that combine AI and systems biology.
*Illustrative imageAI that predicts metabolism in each individual cell
The lab is developing a deep learning model that predicts intracellular metabolic flux from single-cell gene expression data. A distinctive feature is that the laws of physical chemistry and known biological relationships are built directly into the structure of the model.
*Illustrative imageMechanism-based multi-omics analysis
Using mathematical models such as differential equations, the lab aims to understand molecular regulation at a quantitative resolution, such as "by what percentage molecule A acts on molecule B." In diabetic model mice, it has found that the mode of regulation differs from one biological reaction to another.
*Illustrative imageUnderstanding disease as a spatiotemporal system
The lab analyzes data from a wide range of diseases, including diabetes, cardiomyopathy, inflammatory bowel disease, leukemia, and breast cancer. Using systems biology methods, it aims to clarify how networks of tissues and cells change over time and space as a disease progresses.
*Illustrative imageDiagnostic and predictive AI using medical data
The lab also develops AI using real medical data. As one example, it developed an AI that predicts future diabetes risk (prediabetes) from electrocardiogram data, which place little burden on the body.
MembersFaculty
The members of the Life Systems Science Lab consist of faculty members and technical assistants.As of 2026
- Associate ProfessorSatoshi Ohno
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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: “Music Computer Lab” Mbrickn / CC BY-SA 4.0 (cropped)
- AI that predicts metabolism in each individual cell: “Flow Cytometer (NIH BioArt 160)” Courtesy of NIAID Ryan Kissinger / Public domain (cropped)
- Mechanism-based multi-omics analysis: “DNBSEQ-G400” RPSkokie / CC BY-SA 4.0 (cropped)
- Understanding disease as a spatiotemporal system: “Part of whole slide image of a Wilms' tumor of the kidney” Mikael Häggström, M.D. Author info - Reusing images- Conflicts of interest: None Mikael Häggström, M.D.Consent note: Consent from the patient or patient's relatives is regarded as redundant, because of absence of identifiable features (List of HIPAA identifiers) in the media and case information (See also HIPAA case reports guidance). / Public domain (cropped)
- Diagnostic and predictive AI using medical data: “(20250417) Blood glucose meter 09” Roy Zuo / CC BY-SA 4.0 (cropped)
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