Laboratories

Back
Top > Laboratories > Department of Integrative Cellular Informatics > Data-driven Biology

Advanced Medical Science（Cooperating field）Data-driven Biology

Introduction

The Laboratory for Data-driven Biology integrates statistical science, informatics, and computational modeling with experimental molecular biology to unravel the fundamental principles underlying biological phenomena. Advances in cutting-edge measurement technologies—such as next-generation sequencing, mass spectrometry, and live-cell imaging—have enabled the acquisition of vast and diverse omics datasets, including genome, epigenome, transcriptome, proteome, and metabolome. However, there is an increasing need for unified analytical frameworks to make full use of such data.　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　Rather than relying solely on pre-existing biological hypotheses, we aim to understand complex biological systems—such as cancer, psychiatric disorders, development, and behavior—through a data-driven approach. By leveraging mathematical models such as Bayesian modeling and inverse reinforcement learning, alongside machine learning, AI technologies, and high-performance computing, we strive to bridge data and theory to uncover the governing principles of life.

Research Projects

  1. Integrated Analysis of Single-cell and Multicellular Omics Data　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　We develop computational methods to reconstruct spatial and temporal cellular dynamics by integrating single-cell transcriptomics and epigenomics data. Projects include studies on Drosophila embryogenesis, cancer tissue heterogeneity, and multi-omics reconstruction of organ-level interactions.

  2. Data-driven Modeling of Decision-making and Behavior　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　Using behavioral data from humans and animals, we employ inverse reinforcement learning and predictive coding to quantify latent cognitive processes. Our goal is to elucidate the relationship between neural activity and behavior, with applications to neuropsychiatric disorders.

  3. Discovery and Prediction of Disease-related Biomarkers　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　We explore molecular mechanisms and systemic behaviors in diseases such as cancer and psychiatric disorders using data-driven network analysis. Bayesian inference and graph neural networks (GNNs) are applied to model hierarchical and heterogeneous clinical omics data.

  4. Object-oriented Analysis of Complex Biomedical Big Data　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　We develop novel methods to analyze complex, non-numeric data such as images, trees, and histograms. These object-oriented data analysis frameworks are used to study tumor microenvironments and immunological diversity in a mathematically principled manner.

  5. Predictive Biology via Mathematical Modeling and Simulation　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　We model spatiotemporal biological processes—such as ERK signaling waves and somite formation—by combining physical models with large-scale simulations. Projects include deep learning-based simulations of extracellular signaling systems and multi-cellular interactions.

Faculty Members

FacultyPositionDepartment
HONDA Naoki Professor Data-driven Biology
YADA Yuichiro Associate Professor Data-driven Biology
KONDO Yohei Designated Lecturer Data-driven Biology
AKASHI Tomohiro Assistant Professor Data-driven Biology
OTA Ryosaku Designated Associate Professor Data-driven Biology
SAKAGUCHI Shunta Designated Associate Professor Data-driven Biology
TSUTSUMI Masato Designated Associate Professor Data-driven Biology
FUJIOKA Shusei Designated Associate Professor Data-driven Biology
OKOCHI Yasushi Assistant Professor Data-driven Biology
FUJIWARA Mana Researcher Data-driven Biology

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).

Research 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, Supercomputing and large-scale simulation, Object-oriented data analysis, Image analysis and spatial transcriptomics, Organ-level trans-omics modeling

Private Page

Call for Graduate Student

Modern life sciences are rapidly evolving toward a data-intensive paradigm that goes beyond intuition and tradition. Technologies such as next-generation sequencing and single-cell analysis generate vast biological datasets, providing us with unprecedented opportunities to uncover the inner workings of life. Yet, with such data comes a pressing challenge: how do we extract meaningful insights?

In our laboratory, we harness the power of statistics, mathematical modeling, machine learning, and AI to study complex phenomena such as cancer, psychiatric disorders, development, and behavior. By making hidden structures and future changes visible through data-driven analysis, we aim to push the boundaries of biological understanding.

If you enjoy biology, mathematics, programming, or artificial intelligence—or simply want to combine them—our lab offers an environment where you can thrive. Students from diverse academic backgrounds are welcome. Let’s explore the frontiers of data-driven biology together.

Contact

For inquiries regarding the content of this website, please contact Naoki Honda.

Email：　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　honda.naoki.t1[at]f.mail.nagoya-u.ac.jp　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　Address：　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　Data-driven Biology Laboratory　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　5th Floor, Medical Science Research Building 2　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　65 Tsurumai-cho, Showa-ku, Nagoya 466-8550(466-0065）, Japan　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　Phone：+81-52-744-1980