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Life Systems Science Lab
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The lab uses AI and mathematical models to explore the mechanisms of metabolism and disease from biological data.
AboutAbout the lab
Decoding mechanisms from large-scale biological data
The Life Systems Science Lab studies the essence of living systems using mathematical analysis techniques that combine artificial intelligence (AI) and systems biology.
The lab views multicellular organisms such as humans as maintaining homeostasis through networks of cells, tissues, and organs, and regards the loss of this balance as the essence of disease.
The lab uses mathematical models and deep learning to predict metabolism and to analyze data from a variety of diseases.
The lab is new, having been established in January 2026.
Key terms
- Single-cell
- Examining cells one at a time. This makes it possible to distinguish differences among individual cells within a cell population.
- Metabolic flux
- The speed at which metabolic chemical reactions proceed inside a cell (the reaction rate).
- Omics data
- Large-scale data that comprehensively measure molecules, such as the genome and proteome.
- Homeostasis
- The property of keeping the state of the body constant through networks of cells, tissues, and organs.
NewsLab news
Paper from a joint study with the University of Tokyo published in iScience
Associate Professor Ohno received the Best Poster Presentation Award at IIBMP2026
Paper submitted to bioRxiv
Associate Professor Satoshi Ohno joined, and the lab was launched
ResearchResearch themes
The lab aims to create a new mathematical framework for understanding the essence of living systems from large-scale biological data. By combining the expressive power of artificial intelligence (AI) with the logical rigor of systems biology, it develops highly versatile analysis techniques.
*Illustrative imagePredicting metabolism from single-cell gene expression
The lab develops deep learning models that predict metabolic reaction rates (metabolic flux) from single-cell gene expression data. By building the laws of physical chemistry and known biological relationships into the models, it aims to identify cells with disease-specific metabolic states.
*Illustrative imageMechanism-based multi-omics analysis
The lab analyzes omics data such as the transcriptome and proteome using mathematical models, including differential equations. It aims for mechanism-based understanding at a quantitative resolution, such as "molecule A acts on molecule B by X%."
*Illustrative imageCapturing disease progression in time and space
The lab analyzes data from many diseases, including diabetes, cardiomyopathy, inflammatory bowel disease, leukemia, breast cancer, Parkinson's disease, and dementia. Using systems biology methods, it investigates how networks of tissues and cells change and break down over time and in space.
*Illustrative imageAI that predicts diabetes risk from electrocardiograms
To connect basic findings to clinical practice, the lab also works on developing diagnostic and predictive AI using real medical data. One example is AI that predicts future diabetes risk (prediabetes) from electrocardiogram data, which can be collected with minimal invasiveness.
MembersFaculty
Members of the Life Systems Science Lab consist of faculty 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: “Laptop coding programs (Unsplash)” Tirza van Dijk tirzavandijk / CC0 (cropped)
- Predicting metabolism from single-cell gene expression: “Tesla-NVIDIA GPU cluster ( )” ChrisDag / CC BY 2.0 (cropped)
- Mechanism-based multi-omics analysis: “Liquid Chromatography Mass Spectrometer” Cwszot | Dagui1929 | CasJu | YassineMrabet / CC0 (cropped)
- Capturing disease progression in time and space: “Histological section of the large intestine (colon)” Otávio Astor Vaz Costa / CC BY 4.0 (cropped)
- AI that predicts diabetes risk from electrocardiograms: “ECG Mortara” Sarang / Public domain (cropped)
- Related lab (Bioinformatics Lab): “Eppendorf epMotion 5075 liquid handling robot” Magnus Manske / CC BY-SA 3.0 (cropped)
- Related lab (Metabolism, Endocrinology and Diabetes Lab): “Self Monitoring of Blood Glucose” Omstaal / CC BY-SA 4.0 (cropped)
- Related lab (Diagnostic Imaging and IVR Lab): “Radiology Picture” James Moore200 / CC BY-SA 4.0 (cropped)
- Related lab (Cardiovascular Surgery Lab): “Ventricular assist device” Madhero88 / CC BY-SA 3.0 (cropped)
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