Laboratories

Data-driven Biology

KEYWORDS

  • Data-driven approaches
  • Bioinformatics
  • Systems biology
  • Omics analysis (genome, epigenome, transcriptome, proteome, metabolome)
  • Single-cell and trans-omics analysis
  • Bayesian modeling
  • Machine learning and deep learning
  • Inverse reinforcement learning
  • Decision-making models
  • Multicellular dynamics modeling
  • Disease network and hierarchical modeling
  • Mathematical modeling of cancer and psychiatric disorders
  • Image analysis and spatial transcriptomics
  • Organ-level trans-omics modeling

HEAD

HONDA Naoki

Professor

Researchers

LAB MEMBER

Faculty Position Researchers
YADA Yuichiro Associate Professor Researchers
KONDO Yohei Designated Lecturer Researchers
AKASHI Tomohiro Assistant Professor Researchers
OTA Ryosaku Designated Associate Professor Researchers
SAKAGUCHI Shunta Designated Associate Professor Researchers
FUJIOKA Shusei Designated Associate Professor Researchers
OKOCHI Yasushi Assistant Professor Researchers
FUJIWARA Mana Researcher
KANEKO Takaaki Researcher

CONTACT

Email honda.naoki.t1◎f.mail.nagoya-u.ac.jp (Please send a message after replacing "◎" mark with "@" mark. )
HP Private Page

OUTLINE

In data-driven biology, we combine computational modeling with experimental approaches in biology to uncover fundamental principles of living systems. Advances in state-of-the-art measurement technologies—such as next-generation sequencing, mass spectrometry, and live imaging—now enable the acquisition of vast omics datasets (including genomics, epigenomics, transcriptomics, proteomics, and metabolomics). At the same time, there is a growing need for integrated analytical frameworks that can fully leverage these rich data resources.
Rather than relying too heavily on pre-existing biological hypotheses, we pursue a data-driven approach that extracts structure and mechanisms directly from data. Our goal is to understand complex biological phenomena—such as neurological and psychiatric disorders, cancer, development, and behavior—as dynamical systems. To this end, we draw on mathematical models and machine learning/AI methods including Bayesian modeling and inverse reinforcement learning, iterating between real data and theory to better understand how life works.

RESEARCH PROJECTS

1. Integrated analysis of single-cell and multi-cellular omics data

We develop computational methods for omics data, including batch correction, spatial reconstruction, and inference of inter-organ interactions. By leveraging optimal transport, deep learning, and related techniques, we aim to uncover biological mechanisms across scales—from individual cells to whole organisms.

2. Data-driven modeling of cognitive, behavioral, and neural processes

Using frameworks such as Bayesian inference and reinforcement learning, we develop methods to decode latent fluctuations in mental states or shifts in decision-making strategies from human and animal behavioral data. We also work on integrative analyses that combine brain connectivity with gene expression data to reveal organizing principles of neural circuit wiring.

3. Pathophysiological analysis through data-driven mathematical modeling

We pursue data-driven mathematical modeling integrating machine learning and mechanistic models to deepen understanding of disease progression, enable early detection, and improve prognosis prediction. Using hierarchical Bayesian approaches, state-space models, and partial differential equations, we model pathological processes represented as biomarker trajectories, longitudinal electronic health record patterns, and diffusion processes of proteins in the brain, and leverage these models for disease prediction and understanding.

4. Computational biology through mathematical modeling and simulation

We seek to understand living systems through mathematical modeling and simulation. Our work includes, for example, models that view learning in immune responses through the lens of predictive coding, and theoretical studies of dynamics emerging from competition and hierarchical organization in stem cell populations. By iterating between experimental data and theory, we aim to uncover the physical and computational logic that supports life.

BIBLIOGRAPHY

2025
  1. Tohyama S., Nagashima T., Higashino I., Arima-Yoshida F., Hiyoshi K., Nagase M., Yada Y., Honda N., Watabe A.M.* “Aversive experiences induce valence plasticity of instructive signals to change future learning rules in mice.” Communications Biology, in press.
  2. Cao Z., Setoyama D., Monica-Natsumi D., Matsushima T., Yada Y., Watabe M., Hikida T., Kato A.T., Honda N.* “Leveraging Machine Learning to Uncover the Hidden Links between Trusting Behavior and Biological Markers.” Dialogues in Clinical Neuroscience 27(1), 201–215 (2025).
  3. Koike J., Yada Y., Hira R., Honda N.* “Sperrfy the brain: A data-driven realization of Sperry's Chemoaffinity theory in the neural connectome.” bioRxiv (2025). [Preprint]
  4. Sakaguchi S., Tsutsumi M. (Co-first), Nishi K., Honda N. “Disentanglement of batch effects and biological signals across conditions in the single-cell transcriptome.” bioRxiv (2025). [Preprint]
  5. Ota R.*, Sakamoto M., Aoki W., Honda N. “Prediction of quantitative function of artificially-designed protein from structural information.” bioRxiv (2025). [Preprint]
  6. Itoh T., Kondo Y.*, Nakayama T., Shinomiya A., Aoki K., Yoshimura T., Honda N. “Inverse signal importance in real exposome: How do biological systems dynamically prioritize multiple environmental signals?” bioRxiv (2025). [Preprint]
2024
  1. Ju H., Skibbe H., Fukui M., Yoshimura S.H., Honda N.* “Machine learning-guided reconstruction of cytoskeleton network from Live-cell AFM Images.” iScience 27, 10110907 (2024).
  2. Takeuchi R.F., Sato A.Y., Ito K.N., Yokoyama H., Miyata R., Ueda R., Kitajima K., Kamaguchi R., Suzuki T., Isobe K., Honda N., Osakada F.* “Posteromedial cortical networks encode visuomotor prediction errors.” bioRxiv (2024). [Preprint]
2023
  1. Nakayama T., Tanikawa M., Okushi Y., Itoh T., Shimmura T., Maruyama M., Yamaguchi T., Matsumiya A., Shinomiya A., Guh Y.J., Chen J., Naruse K., Kudoh H., Kondo Y., Honda N., Aoki K., Nagano A.J., Yoshimura T. “A transcriptional program underlying the circannual rhythms of gonadal development in medaka.” Proceedings of the National Academy of Sciences 120(52), e2313514120 (2023).
  2. Yada Y., Honda N. “Few-shot prediction of amyloid β accumulation from mainly unpaired data on biomarker candidates.” npj Systems Biology and Applications 9, 59 (2023).
  3. Sakaguchi S., Okochi Y., Tanegashima C., Nishimura O., Uemura T., Kadota M., Honda N., Kondo T.* “Single-cell transcriptome atlas of Drosophila gastrula 2.0.” Cell Reports 42, 112707 (2023).
  4. Konaka Y., Honda N.* “Decoding reward–curiosity conflict in decision-making from irrational behaviors.” Nature Computational Science 3, 418–432 (2023).
  5. Hatakeyama Y., Saito N., Mii Y., Shinozuka T., Takemoto T., Honda N., Takada S. “Intercellular exchange of Wnt ligands reduces cell population heterogeneity in embryogenesis.” Nature Communications 14, 1924 (2023).
  6. Ishino S., Kamada T., Sarpong G., Kitano J., Tsukasa R., Mukohira H., Sun F., Li Y., Kobayashi K., Honda N., Oishi N., Ogawa M.* “Dopamine error signal to actively cope with lack of expected reward.” Science Advances 9(10), eade5420 (2023).
  7. Ju H., Honda N., Yoshimura S.H., Kaneko M., Shigematsu T., Kiyono K.* “Multidimensional fractal scaling analysis using higher order moving average polynomials and its fast algorithm.” Signal Processing 208, 108997 (2023).
  8. Onishi T., Honda N., Igarashi Y.* “Optimal COVID-19 testing strategy on limited resources.” PLoS ONE 18(2), e0281319 (2023).
  9. Yoshido K., Honda N.* “Adaptive discrimination of antigen risk by predictive coding in immune system.” iScience 26, 105754 (2023).
  10. Honda N. “Data-driven Interpretation of Emotional Fluctuation and Irrationality.” Medical Science Digest, Special Issue: Hikikomori and Psychiatric Disorders, 49(3), 12–15 (2023). [Japanese] [Review Articles]
2022
  1. Nakamuta S., Yoshido K., Honda N.* “Stem cell homeostasis regulated by hierarchy and neutral competition.” Communications Biology 5, 1268 (2022).
  2. Kanatsu-Shinohara M., Honda N., Tanaka T., Tatehana M., Kikkawa T., Osumi N., Shinohara T.* “Regulation of male germline transmission patterns by the Trp53-Cdkn1a pathway.” Stem Cell Reports 17, 1–18 (2022).
  3. Okochi Y., Matsui T., Sakaguchi S., Kondo T., Honda N. “Zero-shot reconstruction of mutant spatial transcriptomes.” bioRxiv (2022). [Preprint]
2021
  1. Okochi Y., Sakaguchi S., Nakae K., Kondo T., Honda N.* “Model-based prediction of spatial gene expression via generative linear mapping.” Nature Communications 12, 3731 (2021).
  2. Asakura Y., Kondo Y., Aoki K., Honda N.* “Hierarchical modeling of mechano-chemical dynamics of epithelial sheets across cells and tissue.” Scientific Reports 11, 4069 (2021).
  3. Honda N. “Data-driven Biology: Mathematical Modeling Rooted in Data.” Sugaku Kagaku (Mathematical Sciences), Special Issue: Mathematical Modeling and Life Science, September 2021. [Japanese] [Review Articles]
2020
  1. Honda N.*, Matsui T. “Somite boundary determination in normal and clock-less vertebrate embryos.” Development, Growth & Differentiation 62, 177–187 (2020).
  2. Honda N. “Modeling and Data Analysis of Animal Behavior Based on Reinforcement and Inverse Reinforcement Learning.” Jikken Igaku (Experimental Medicine), Supplement “Applying Machine Learning to Life Science,” 38(20), 202–209 (2020). [Japanese] [Review Articles]
  3. Okouchi Y., Sakaguchi S., Honda N. “Machine Learning-Based Spatial Gene Expression Reconstruction from scRNA-seq Data.” Jikken Igaku (Experimental Medicine), Supplement “Applying Machine Learning to Life Science,” 38(20), 63–69 (2020). [Japanese] [Review Articles]
2019
  1. Honda N.*, Akiyama R., Sari D.W.K., Ishii S., Bessho Y., Matsui T. “Noise-resistant developmental reproducibility in vertebrate somite formation.” PLoS Computational Biology 15(2), e1006579 (2019).
  2. Honda N. “Wiring Principles from Axonal Chemotaxis to Neural Circuit Formation.” Seibutsu Butsuri (Biophysics) 59(3), 141–143 (2019). [Japanese] [Review Articles]
  3. Honda N. “4-2 Gephi: Intuitive Layout of Mouse Brain Neural Network Structures.” In: Professional Data Visualization Techniques – Beyond Excel (Ed. Yasunobu Igarashi), 2019. [Japanese] [Books].
2018
  1. Sari D.W.K., Akiyama R., Honda N., Ishijima H., Bessho Y., Matsui T.* “Time-lapse observation of stepwise regression of Erk activity in zebrafish presomitic mesoderm.” Scientific Reports 8, 4335 (2018).
  2. Yamaguchi S., Honda N.*, Ikeda M., Tsukada Y., Nakano S., Mori I., Ishii S. “Identification of animal behavioral strategies by inverse reinforcement learning.” PLoS Computational Biology 14(5), e1006122 (2018).
  3. Honda N. “Chapter 7: Identification of Biological Information Processing Based on Quantitative Data.” In: Advances in Biotechnology via AI Integration (Ed. Mitsuyoshi Ueda), 2018. [Japanese] [Books].
2017
  1. Kanatsu-Shinohara M.*, Honda N., Shinohara T. “Nonrandom contribution of left and right testes to germline transmission from mouse spermatogonial stem cells.” Biology of Reproduction 97(6), 902–910 (2017).
  2. Aoki K.*, Kondo Y., Honda N., Hiratsuka T., Ito R.E., Matsuda M. “Propagating wave of ERK activation orients collective cell migration.” Developmental Cell 43, 305–317 (2017).
  3. Honda N.* “Revisiting chemoaffinity theory: Chemotactic implementation of topographic axonal projection.” PLoS Computational Biology 13(8), e1005702 (2017).
  4. Takano T., Wu M., Nakamuta S., Honda N., Ishizawa N., Namba T., Watanabe T., Xu C., Hamaguchi T., Yura Y., Amano M., Hahn K.M., Kaibuchi K.* “Discovery of long-range inhibitory signaling to ensure single axon formation.” Nature Communications 8, 33 (2017).
  5. Honda N.*, Uegaki K., Ishii S. “Self-organization mechanism of microtubule orientation patterns in axons and dendrites.” bioRxiv 163014 (2017). [Preprint]
  6. Shinohara M., Honda N., Shinohara T. “Mechanisms for Maintaining the Functional Lifespan of Spermatogonial Stem Cells.” Jikken Igaku (Experimental Medicine) 35(8), 1297–1302 (2017). [Japanese] [Review Articles]
2016
  1. Honda N., Nishiyama M., Togashi K., Igarashi Y., Hong K., Ishii S. “Multi-phasic bi-directional chemotactic responses of the growth cone.” Scientific Reports 6, 36256 (2016).
  2. Yamao M., Aoki K., Yukinawa N., Ishii S., Matsuda M., Honda N.* “Two new FRET imaging measures: linearly proportional to and highly contrasting the fraction of active molecules.” PLoS One 11(10), e0164254 (2016).
  3. Li Y., Nakae K., Ishii S., Honda N.* “Uncertainty-dependent extinction of fear memory in an amygdala-mPFC neural circuit model.” PLoS Computational Biology 12(9), e1005099 (2016).
  4. Kanatsu-Shinohara M.*, Honda N., Shinohara T. “Nonrandom germline transmission of mouse spermatogonial stem cells.” Developmental Cell 38, 248–261 (2016).
  5. Tsukada Y., Yamao M., Honda N., Shimowada T., Ohnishi N., Kuhara A., Ishii S., Mori I.* “Reconstruction of spatial thermal gradient encoded in thermosensory neuron AFD in Caenorhabditis elegans.” Journal of Neuroscience 36(9), 2571–2581 (2016).
2015~2005
  1. Yamao M., Honda N.* (Co-first), Kunida K., Aoki K., Matsuda M., Ishii S.* “Distinct predictive performance of Rac1 and Cdc42 in cell migration.” Scientific Reports 5, 17527 (2015).
  2. Kumagai Y., Honda N., Nakasyo E., Kamioka Y., Kiyokawa E., Matsuda M.* “Heterogeneity in ERK activity as visualized by in vivo FRET imaging of mammary tumor cells developed in MMTV-Neu mice.” Oncogene 34(8), 1051–1057 (2015).
  3. Hiratsuka T., Fujita Y., Honda N., Aoki K., Kamioka Y., Matsuda M.* “Intercellular propagation of extracellular signal-regulated kinase activation revealed by in vivo imaging of mouse skin.” eLife 4, e05178 (2015).
  4. Yukinaga H., Shionyu C., Hirata E., Ui-Tei K., Nagashima T., Kondo S., Okada-Hatakeyama M., Honda N., Matsuda M.* “Fluctuation of Rac1 activity is associated with the phenotypic and transcriptional heterogeneity of glioma cells.” Journal of Cell Science 127(8), 1805–1815 (2014).
  5. Honda N.*, Ishii S. “Mathematical Modeling of Neuronal Polarization During Development.” Progress in Molecular Biology and Translational Science 123, 127–141 (2014).
  6. Honda N., Yamao M., Ishii S. “System Identification of Cell Migration.” Seitai no Kagaku (Science of the Living Body) 65(5), 468–469 (2014). [Japanese] [Review Articles]
  7. Yamao M., Honda N., Ishii S. “Chapter 11: Collective Cell Migration.” In: Trends in Biophysics: From Cell Dynamics Toward Multicellular Growth Phenomena (Ed. Pavel Kraikivski), pp. 205–235 (2013). [Books]
  8. Kaneko-Kawano T.*, Takasu F., Honda N., Sakumura Y., Ishii S., Ueba T., Eiyama A., Okada A., Kawano Y., Suzuki K. “Dynamic Regulation of Myosin Light Chain Phosphorylation by Rho-kinase.” PLoS One 7(6), e39269 (2012).
  9. Yamao M., Honda N.*, Ishii S. “Multi-cellular logistics of collective cell migration.” PLoS One 6(12), e27950 (2011).
  10. Nonaka S., Honda N.* (Co-first), Ishii S. “A multiphysical model of cell migration integrating reaction-diffusion, membrane and cytoskeleton.” Neural Networks 24, 979–989 (2011).
  11. Honda N.*, Nakamuta S., Kaibuchi K., Ishii S. “Flexible Search for Single-Axon Morphology during Neuronal Spontaneous Polarization.” PLoS One 6(4), e19034 (2011).
  12. Yamao M., Honda N., Ishii S. “Noise-Induced collective migration for neural crest cells.” Lecture Notes in Computer Science 6352, 155–163 (2010).
  13. Honda N.*, Sakumura Y., Ishii S. “Stochastic control of spontaneous signal generation for gradient sensing in chemotaxis.” Journal of Theoretical Biology 255, 259–266 (2008).
  14. Tamura H., Ng D.C., Tokuda T., Honda N., Nakagawa T., Mizuno T., Hatanaka Y., Ishikawa Y., Ohta J., Shiosaka S.* “One-chip sensing device (biomedical photonic LSI) enabled to assess hippocampal steep and gradual up-regulated proteolytic activities.” Journal of Neuroscience Methods 173, 114–120 (2008).
  15. Honda N., Sakumura Y., Ishii S.* “Local signaling with molecular diffusion as a decoder of Ca²⁺ signals in synaptic plasticity.” Molecular Systems Biology 1, 2005.0027 (2005).

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