Deciphering the origin of developmental stability: The role of intracellular expression variability in evolutionary conservation
Evolution & Development (March 2024)
Masato TSUTSUMI, Ph.D.
Mathematical Life Dynamics Project
Hello, I am Masato Tsutsumi.
I am currently working as an Assistant Professor in the Mathematical Life Dynamics Project at the Life Science Center for Survival Dynamics, Tsukuba Advanced Research Alliance (TARA), University of Tsukuba.
I also hold a concurrent affiliation as a Visiting Researcher at the Data-Driven Biology Laboratory, Graduate School of Medicine, Nagoya University.
My research aims to treat life phenomena as a hierarchical system of morphology, psychology, and behavior and to quantify them as information. Specifically, I work on quantification of morphology using deep generative models, quantification of psychological state (e.g., social stress) using mathematical models, and quantitative analysis of behavior based on inter-individual interactions, with the goal of understanding universal principles that describe the state and dynamics of life.
Interests
- Quantification using deep learning
- Morphometry
- Social defeat stress
- Quantitative biology
Education
Assistant Professor, 2025/12 -
Life Science Center for Survival Dynamics, Tsukuba Advanced Research Alliance (TARA), University of Tsukuba
(Mathematical Life Dynamics Project: Assoc. Prof. Nen Saito)Visiting Researcher, 2025/12 -
Nagoya University Graduate School of Medicine
(Data-Driven Biology Laboratory: Prof. Naoki Honda)Designated Assistant Professor, 2024/10 - 2025/11
Nagoya University Graduate School of Medicine
(Data-Driven Biology Laboratory: Prof. Naoki Honda)Designated Researcher, 2023/10 - 2024/9
Hiroshima University Graduate School of Integrated Sciences for Life
(Data-Driven Biology Laboratory: Prof. Naoki Honda)Doctor of Science, 2020/4 - 2023/9
Graduate School of Science, The University of Tokyo
(Supervisor: Prof. Chikara Furusawa)Master of Science, 2018/4 - 2020/4
Graduate School of Science, The University of Tokyo
(Supervisor: Prof. Chikara Furusawa)Bachelor of Science, 2014/4 - 2018/3
Department of Physics, Faculty of Science, The University of Tokyo
Research Interests
Deep Learning
VAE, YOLO
Morphometrics
Geometric morphometrics
Biology
Quantitative biology, EvoDevo
Projects
MORPHO-VAE
Deep learning based morphological feature extraction. Landmark-free and robust to missing data.
KANADE
A batch-effect removal method for single-cell transcriptomics.
mindGAP
A hierarchical variational Bayesian framework that estimates population- and individual-level psychiatric energy landscapes—and the gap between them—from longitudinal questionnaire data.
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DOLO
Markerless multi-larva tracking with YOLO. Related manuscript “Chemosensory input suppresses cannibalism…” submitted.
Publications
4
Papers
27
Presentations
6
Invited Talks
A deep learning approach for morphological feature extraction based on variational auto-encoder: an application to mandible shape
npj Systems Biology and Applications (July 6, 2023)
Mind the gap: quantifying population–individual gap in depressive symptom dynamics through energy landscapes
Submitted (Nature Human Behaviour)
Chemosensory input suppresses cannibalism by stabilizing social feeding boundaries in Drosophila larvae
Submitted
Decoding Anadara shell morphology with deep learning
bioRxiv (April 21, 2026)
Disentanglement of batch effects and biological signals across conditions in the single-cell transcriptome
bioRxiv (April 16, 2025)
A method for morphological feature extraction based on variational auto-encoder: an application to mandible shape
Preprint (May 19, 2022)
Towards quantification of diverse life phenomena: morphology, behavior, and psychology as information
SSTB2026 - Spring School for Theoretical Biology 2026- (March 5, 2026)
Development of quantitative analysis methods for biological morphology using deep learning
Japan Society for Industrial and Applied Mathematics Annual Meeting 2024 (September 16, 2024)
Development of quantitative analysis methods for biological morphology using deep learning
Online Seminar Series "Future of Evolutionary Biology with AI" (March 9, 2024)
Development of quantitative analysis methods for biological morphology using deep learning
8th Theoretical Immunology Workshop (February 14, 2024)
Quantification of various biological morphologies using machine learning
Hiroshima University Mathematical Life Sciences Program Seminar (November 10, 2022)
Quantification and application of biological morphology using machine learning: Focusing on primate mandibles
National Institute for Basic Biology Departmental Seminar (October 2021)
Quantitative Analysis of Biological Morphology and Behavior Based on Deep Learning: Development of Morpho-VAE and DOLO
Frontiers of coevolutionary phenotypic emergence research: tracing how research unfolds from its roots (February 20, 2026)
Analysis of morphology and behavior using deep learning methods
Principles for Creating Life Functions through Evolutionary Information Assembly (January 13, 2026)
Development of a markerless multi-individual tracking system for controlling cannibalistic behavior in crickets
47th Annual Meeting of the Japanese Society for Comparative Physiology and Biochemistry (December 5, 2025)
Morphological quantification using deep learning: Morpho-VAE
99th Symposium on Science of Form "Shape that brings motion" (November 30, 2025)
Visualization of morphological variation using 3D Variational Autoencoder
34th Annual Meeting of the Japanese Society for Bioimaging
Behavioral analysis of double mutant Drosophila larvae using DOLO (Drosophila tracking with YOLO)
1st Systems Behavioral Science Workshop (May 30, 2025)
Estimation of psychological state transitions during/after emergency declarations using energy landscape analysis
SSTB2025 - Spring School for Theoretical Biology 2025- (February 20, 2025)
Proposal of morphological quantitative analysis methods using deep learning
Annual Meeting of the Japanese Society for Mathematical Biology 2024 (September 12, 2024)
Quantification of mouse behavior under social defeat stress using mathematical models
CPSY TOKYO 2024 (March 28, 2024)
Mathematical model of rumination in the brain using Bayesian inference
Spring School for Theoretical Biology 2024 (February 20, 2024)