Updated on 2026/06/26

写真a

 
HONDA Naoki
 
Organization
Graduate School of Medicine Center for Neurological Disease and Cancer Professor
Graduate School
Graduate School of Medicine
Undergraduate School
School of Medicine Department of Medicine
Title
Professor
Contact information
メールアドレス
Homepage
https://sites.google.com/view/data-driven-biology/
Profile
Theoretical biologist, Data-driven biology with mathematical modeling & Machine learning.
External link

Degree 1

  1. Doctor of Science （ 2008.3   Nara Institute of Science and Technology ） 

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Research Interests 10

  1. １細胞ゲノミクス

  2. 発生生物学

  3. 神経科学

  4. 機械学習

  5. 理論生物学

  6. システム生物学

  7. データ駆動生物学

  8. 定量生物学

  9. Mathematical biology

  10. Data-driven biology, Mathematical modeling, Machine learning

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Research Areas 4

  1. Informatics / Biological, health, and medical informatics  / Data-driven biology, Mathematical modeling, Machine learning

  2. Life Science / Developmental biology

  3. Life Science / Neuroscience - general  / 理論神経科学

  4. Life Science / Biophysics

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Current Research Project and SDGs 1

  1. Data-driven biology

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Research History 14

  1. Nagoya University   School of Medicine Department of Medicine   Professor

    2026.1

  2. Nagoya University   Graduate School of Medicine   Professor

    2024.10

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  3. Nagoya University   Graduate School of Medicine Center for Neurological Disease and Cancer   Professor

    2024.10

  4. Hiroshima University   Designated Professor

    2024.10

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  5. Nagoya University   One Medicine Transdisciplinary Life science-Medicine co-creation Platform   Professor

    2024.10

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  6. Kyoto University   Graduate School of Biostudies

    2021.4 - 2025.3

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  7. National Institutes of Natural Sciences

    2021.4 - 2025.2

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  8. Hiroshima University   Professor

    2021.4 - 2024.10

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  9. Kyoto University   Graduate School of Biostudies   Associate Professor

    2018.4 - 2021.3

  10. Kyoto University   Graduate School of Biostudies   Designated Associate Professor

    2017.4 - 2018.3

  11. Kyoto University   Graduate School of Medicine   Designated Associate Professor

    2013.5 - 2017.3

  12. Kyoto University   Graduate School of Informatics   Designated Assistant Professor

    2012.4 - 2013.4

  13. Kyoto University   Graduate School of Informatics   Researcher

    2009.2 - 2012.3

  14. Kyushu University   Faculty of science   Researcher

    2008.4 - 2009.1

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Education 2

  1. Nara Institute of Science and Technology

    2003.4 - 2008.3

  2. Doshisha University

    1998.4 - 2002.3

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Professional Memberships 6

  1. 神経回路学会

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  2. 神経科学学会

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  3. The Japanese Society for Artificial Intelligence

  4. 数理生物学会

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  5. 生物物理学会

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  6. The Molecular Biology Society of Japan

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Committee Memberships 3

  1. 日本数理生物学会   運営委員  

    2025.4 - 2027.3   

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    Committee type：Academic society

  2. 日本神経回路学会   プログラム委員長（Neuro2026）  

    2025.4 - 2026.8   

  3. 日本生物物理学会   分野別専門委員  

    2023.4 - 2025.3   

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Papers 57

  1. Toward the promotion of One Health - Part II: Interdisciplinary research cooperation between digital transformation and exposome. International journal Open Access

    Takeshi Yoneshiro, Yoshito Kumagai, Keiko Nohara, Shingo Iwami, Naoki Honda, Nobuhiko Ohno, Motohiro Nishida

    The journal of physiological sciences : JPS   Vol. 76 ( 2 ) page： 100078 - 100078   2026.7

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    Language：English   Publishing type：Research paper (scientific journal)  

    Human activities increasingly disrupt global ecosystems, contributing to climate change, biodiversity loss, and emerging health threats. In response, the One Health framework has gained attention as an integrative approach encompassing human, animal, and environmental health. In a symposium at APPW2025, experts in Exposome science and Digital Transformation discussed how interdisciplinary integration can advance predictive and preventive medicine. This review summarizes five key topics: Exposome as a determinant of disease risk, epigenetic mechanisms encoding environmental memory, environmental programming of brown adipose tissue, adaptive prioritization of environmental signals, and simulation-based drug repurposing. Collectively, these studies highlight a paradigm shift from conventional linear exposure-disease models toward a systems-level understanding integrating cumulative exposures, biological memory, and predictive modeling. The convergence of Exposome science and Digital Transformation provides a foundation for advancing One Health into a predictive and actionable scientific framework.

    DOI： 10.1016/j.jphyss.2026.100078

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  2. Inside insight: decoding how insight emerges from competing world models

    Kengo Inutsuka, Tadaaki Nishioka, Tom Macpherson, Mana Fujiwara, Takatoshi Hikida, Honda Naoki

    bioRxiv     2026.5

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    Authorship：Last author,　Corresponding author   Publisher：openRxiv  

    Abstract

    When and how does insight emerge? We conceptualize insight as a sudden realization arising from restructuring a world model—an internal interpretation linking actions to outcomes. However, this process remains inaccessible even with verbal report. Here we developed inside insight dynamics (IID), a machine-learning framework estimating latent world-model dynamics from behavioral data. We analyzed mouse data from two tasks differing in difficulty and requiring animals to shift from an initial world model to a new one. IID decoded timing of insight-like shifts and evolving reward beliefs within competing world models. We examined how these shifts were acquired through learning. We found that the harder task was better explained by gated learning, in which a new model becomes learnable only after being recognized, whereas the simpler task favored parallel learning, in which candidate models are learned in advance. Thus, IID opens a route to quantifying latent insight dynamics.

    DOI： 10.64898/2026.05.21.726889

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  3. Decomposing heterogeneity in disease progression speeds and pathways. International journal Open Access

    Yuichiro Yada, Honda Naoki

    NPJ digital medicine     2026.5

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    Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

    Understanding why patients with the same diagnosis exhibit markedly different disease progression-some rapidly, others slowly, with distinct symptom patterns-remains a major challenge in medicine. Here, we developed a machine learning framework called DiSPAH (Disease-progression Speed and Pathway Analysis based on a Hidden Markov model) to estimate both the pathway and speed of disease progression in individual patients. DiSPAH models disease progression as continuous-time transitions among latent disease states with a patient-specific progression speed. We applied DiSPAH to longitudinal clinical scores from an amyotrophic lateral sclerosis (ALS) cohort and inferred each patient's trajectory of the latent disease states and progression speed. These dynamics were associated with baseline clinical features and enabled prediction of future course from first-visit data. Our results highlight that jointly modeling progression pathway and speed improves prediction of heterogeneous disease courses, offering a powerful tool for personalized care and research in ALS and other chronic conditions.

    DOI： 10.1038/s41746-026-02665-8

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  4. The mental conflict in risk-taking behavior: Decoding bias between optimism and pessimism

    Iori Higashino, Ryo Ito, Yasushi Okochi, Kengo Inutsuka, Hiroshi Yokoyama, Rikako Kato, Yuichiro Yada, Ken-ichi Amemori, Honda Naoki

    bioRxiv     2026.5

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    Authorship：Last author,　Corresponding author   Publisher：openRxiv  

    Abstract

    Humans and animals often face risky situations that require decision-making. Such decisions can be high-risk, high-return at some times, and low-risk, low-return at other times, depending on the balance between optimism and pessimism. However, how this optimism–pessimism bias is regulated across contexts remains unclear. Here, we introduced a computational model of decision-making in a risk-taking task based on the free-energy principle, together with a machine-learning framework that inversely estimates cognitive updating and optimism–pessimism bias from behavioral data. Applying this framework to monkey behavioral data, we found that a monkey quickly and accurately recognized the degree of risk, while frequently switching between optimism and pessimism during the task. In addition, we identified a characteristic control rule for optimism–pessimism bias that is distinct from reward-dependent regulation. Our framework provided a principled tool for understanding the latent cognitive processes underlying risky decision-making in animals and humans.

    DOI： 10.64898/2026.05.01.722186

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  5. Inferring division-associated stochasticity from time-series single-cell transcriptomes

    Yasushi Okochi, Yoshihito Sawazaki, Yohei Kondo, Honda Naoki

    bioRxiv     2026.4

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    Authorship：Last author,　Corresponding author   Language：English   Publisher：openRxiv  

    Abstract

    Cell division is fundamental to multicellular organisms and stochastic partitioning of cellular components can strongly affect genome-wide gene expression states. However, how cell division-associated partitioning noise shapes the dynamics of proliferating cells is poorly understood. Here, we propose scDIVIDE, a neural stochastic differential equation framework to infer continuous cellular dynamics and division rates while accounting for partitioning noise. We combined birth–death–mutation processes from population genetics with dynamical optimal transport and revealed that the birth rate is embedded in the diffusion coefficient, enabling its inference from time-series scRNA-seq data. scDIVIDE accurately inferred birth rates in synthetic data and the inferred birth rates recapitulated turnover-related programs in mouse hematopoiesis data. By exploiting the birth–diffusion coupling, scDIVIDE provides a biologically-informed constraint on growth rate estimation, outperforming existing methods in predicting future cell distributions. scDIVIDE provides a conceptual avenue for quantitatively dissecting how partitioning noise shapes fate decisions in multicellular systems.

    DOI： 10.64898/2026.04.14.718485

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  6. Filament-resolved simulations reproduce self-organization of lamellipodia and filopodia

    Masaya Fukui, Yohei Kondo, Nen Saito, Honda Naoki

    bioRxiv     2026.3

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    Authorship：Last author,　Corresponding author   Publisher：openRxiv  

    Abstract

    The dynamic assembly of actin filaments underlies diverse cellular morphologies such as lamellipodia, filopodia, and reticulated networks. However, how filament-scale interactions among actin-binding proteins produce distinct actin architectures remains unclear. We developed a filament-resolved computational model of actin self-organization regulated by the Arp2/3 complex and fascin. Individual F-actin filaments are represented as elastic chains, and their stochastic polymerization, Arp2/3-mediated branching, and fascin-mediated crosslinking and bundling are explicitly modeled. The simulations reproduce three actin architectures observed in minimal reconstitution experiments, including lamellipodia-like branched networks, filopodia-like bundled protrusions, and reticulated meshworks, as a function of Arp2/3 and fascin concentrations. We quantify these regimes using actin density, orientational order, and spikiness, which robustly separate the three morphologies across conditions. To connect filament organization to shape change, we further couple the actin network to membrane deformation using a phase-field formulation. This coupling shows how localized remodeling concentrates load to drive pseudopodial protrusions, whereas highly branched networks distribute stresses and stabilize rounded shapes. The model links molecular interactions to emergent architecture and cell-scale morphodynamics.

    DOI： 10.64898/2026.03.15.711798

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  7. A data-driven framework linking the connectome to spatial gene expression gradients inspired by chemoaffinity theory. International journal Open Access

    Jigen Koike, Ken Nakae, Riichiro Hira, Yuichiro Yada, Honda Naoki

    Proceedings of the National Academy of Sciences of the United States of America   Vol. 123 ( 10 ) page： e2516572123   2026.3

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    Understanding how brain-wide neural circuits are genetically wired remains a fundamental question in neuroscience. While Sperry's chemoaffinity theory [Sperry, Proc. Natl. Acad. Sci. U.S.A. 50, 703-710 (1963)] posits that molecular gradients provide positional cues for axonal projections, its application has been largely limited to localized sensory systems. Here, we present SPERRFY (Spatial Positional Encoding for Reconstructing Rules of axonal Fiber connectivitY), a data-driven framework that operationalizes Sperry's theory at the whole-brain scale. By integrating connectomic data with spatial transcriptomic profiles from the Allen Mouse Brain Atlas, SPERRFY infers latent positional gradients that underlie axonal wiring. Using canonical correlation analysis (CCA), we extract top gradient pairs that align with observed neural connectivity patterns, capturing both global (interregional) and local (intraregional) organizational principles. Connectivity reconstruction based on these gradients shows strong predictive performance, and permutation-based null models confirm the biological relevance of the inferred structures. Furthermore, SPERRFY can screen for candidate genes that may contribute to positional wiring information, providing molecular insight into the developmental logic of brain-wide circuitry. Our results extend Sperry's foundational theory beyond the sensory domain, offering a unified, data-driven framework for understanding genetically encoded connectivity across the entire brain.

    DOI： 10.1073/pnas.2516572123

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  8. Zero-shot reconstruction of mutant spatial transcriptomes Open Access

    Yasushi Okochi, Takaaki Matsui, Shunta Sakaguchi, Takefumi Kondo, Honda Naoki

    Patterns   Vol. 7 ( 6 ) page： 101521 - 101521   2026.3

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    Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

    DOI： 10.1016/j.patter.2026.101521

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  9. Gradual proactive regulation of body state by reinforcement learning of homeostasis. International journal Open Access

    Mana Fujiwara, Honda Naoki

    Neuroscience research   Vol. 223   page： 105021 - 105021   2026.2

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    Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

    Living systems maintain physiological variables such as temperature, blood pressure, and glucose within narrow ranges-a process known as homeostasis. Homeostasis involves not only reactive feedback but also anticipatory adjustments shaped by experience. Prior homeostatic reinforcement learning (HRL) models have provided a computational account of anticipatory regulation under homeostatic challenges. However, existing formulations lack mechanisms for gradual, trial-by-trial adjustment and for extinction learning. To address this issue, we developed a continuous HRL framework that enables trial-wise tuning of anticipatory regulation. The model incorporates biologically informed components: asymmetric reinforcement, weighting negative outcomes more than positive outcomes; and a dual-unit, context-gated inhibitory mechanism. We applied the framework to thermoregulatory conditioning with ethanol-induced hypothermia and successfully reproduced cue-triggered compensation, gradual tolerance, and rapid reacquisition after extinction. We then extended the framework to multiple physiological variables influenced by shared neural or hormonal control signals, where compensating one variable can necessarily incur costs in others (e.g., heating at the expense of a fuel-like resource). Under uneven regulatory priorities, deviations propagated through shared control, yielding cascading, system-wide failure to stabilize near the ideal state-a failure mode discussed in autonomic dysregulation (e.g., dysautonomia, myalgic encephalomyelitis/chronic fatigue syndrome). Overall, our framework provides a computational basis to advances a systems-level understanding of multi-organ homeostatic dysregulation in vivo.

    DOI： 10.1016/j.neures.2026.105021

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  10. Intermediate interaction strategies for collective behavior Open Access

    Yuto Kikuchi, Honda Naoki, Mayuko Iwamoto

    Physica A: Statistical Mechanics and its Applications   Vol. 682   page： 131171 - 131171   2026.1

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    Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

    DOI： 10.1016/j.physa.2025.131171

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  11. Optogenetic LTP Manipulation and Mathematical Modeling to Investigate Value Plasticity of the Instructive Signal in Mice Open Access

    Takashi Nagashima, Iori Higashino, Fumiko Arima-Yoshida, Kanae Hiyoshi, Masashi Nagase, Yuichiro Yada, Honda Naoki, Ayako Watabe

    BIO-PROTOCOL   Vol. 16 ( 1398 ) page： e5714   2026

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Bio-Protocol, LLC  

    DOI： 10.21769/bioprotoc.5714

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  12. Leveraging machine learning to uncover the hidden links between trusting behavior and biological markers. International journal Open Access

    Zimu Cao, Daiki Setoyama, Monica Natsumi Daudelin, Toshio Matsushima, Yuichiro Yada, Motoki Watabe, Takatoshi Hikida, Takahiro A Kato, Honda Naoki

    Dialogues in clinical neuroscience   Vol. 27 ( 1 ) page： 201 - 215   2025.12

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    Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

    Understanding the decision-making mechanisms underlying trust is essential, particularly for individuals with mental disorders who often experience difficulties in forming interpersonal trust. In this study, we aimed to explore biomarkers associated with trust-based decision-making through quantitative analysis. However, quantifying internal decision-making processes is challenging, as they are not directly observable. To address this, we developed a machine learning method based on a Bayesian hierarchical model to quantitatively infer latent decision-making parameters from behavioural data collected during a trust game. Applying this method to data from patients with major depressive disorder (MDD) and healthy controls (HCs), we estimated individualised model parameters that regulate trust-related decisions. The model successfully predicted participants' behaviours in the task. Although no significant group-level differences were observed in the estimated parameters between the MDD and HC groups, we uncovered hidden links between trust-related decision-making processes and specific blood biomarkers. Notably, metabolites such as 5-aminolevulinic acid, acetylcarnitine, and 2-aminobutyric acid were significantly associated with individual differences in trusting behaviour. These findings provide valuable insight into the biological basis of trust-based decision-making. They also offer a novel framework for integrating behavioural modelling with biomarker discovery, potentially informing the development of targeted interventions to enhance social functioning and overall well-being.

    DOI： 10.1080/19585969.2025.2513697

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  13. Inverse modeling unveils governing law of mechano-chemical dynamics of epithelial migration. International journal Open Access

    Yuto Kikuchi, Yoshifumi Asakura, Kazuhiro Aoki, Yohei Kondo, Honda Naoki

    PLoS computational biology   Vol. 21 ( 12 ) page： e1013854 - 19   2025.12

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    Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

    Collective cell migration is fundamental to tissue homeostasis and underlies biological processes such as wound healing and cancer invasion. Previous work has proposed governing equations to describe how chemical and mechanical inputs regulate these movements, but the quantitative validity of such models remains to be thoroughly assessed. Here, we developed a machine-learning framework that infers the governing equation from live-cell imaging data. Applied to epithelial sheet migration driven by MAPK/ERK, our approach quantitatively predicted single-cell movement from local chemical and mechanical cues. Examination of the learned equations further indicated that cells process environmental signals by computing their spatiotemporal derivatives. Moreover, when applied to individual cells, our framework revealed cell-cell heterogeneity in the underlying migratory rules. Our framework offers a powerful tool for predictive modeling of multicellular dynamics in both physiological and pathological settings.

    DOI： 10.1371/journal.pcbi.1013854

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  14. Aversive experiences induce valence plasticity of instructive signals to change future learning rules in mice Open Access

    Suguru Tohyama, Takashi Nagashima, Iori Higashino, Fumiko Arima-Yoshida, Kanae Hiyoshi, Masashi Nagase, Yuichiro Yada, Naoki Honda, Ayako M. Watabe

    Communications Biology   Vol. 8 ( 1 ) page： 1002   2025.7

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

    DOI： 10.1038/s42003-025-08367-3

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    Other Link： https://www.nature.com/articles/s42003-025-08367-3

  15. Prediction of quantitative function of artificially-designed protein from structural information

    Ryosaku Ota, Masayuki Sakamoto, Wataru Aoki, Honda Naoki

    bioRxiv     2025.4

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    Authorship：Last author,　Corresponding author   Publisher：openRxiv  

    Abstract

    Artificially designed proteins are widely used in applications such as optogenetics and biosensing. While experimental optimization of these proteins is effective, it is also costly and labor-intensive. To address this challenge, computational approaches have been developed, primarily relying on sequence-based features. However, protein function is inherently tied to its three-dimensional (3D) structure, and incorporating structural information could enable more accurate predictions and provide deeper biological interpretability. Here, we proposed a structure-based analysis framework called ‘Foldinsight’ for predicting protein functionalities. In our framework, we first predict protein structures from sequences using AlphaFold2 and then utilize these structures to predict protein properties. Since proteins vary in the number of atoms and lack direct atomic correspondence, we applied molecular field mapping, which captures the energy states surrounding a protein and converts them into fixed-length numerical vectors. This transformation enables the application of machine learning, allowing protein properties to be predicted from structure-derived features. Applying this framework to channelrhodopsin mutants, we achieved predictive performance comparable to sequence-based models. Additionally, our structure-based analysis successfully identified key structural regions contributing to functional differences, highlighting the advantage of incorporating structural data into predictive modeling.

    DOI： 10.1101/2025.04.10.648284

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  16. Disentanglement of batch effects and biological signals across conditions in the single-cell transcriptome

    Shunta Sakaguchi, Masato Tsutsumi, Kentaro Nishi, Honda Naoki

    bioRxiv     2025.4

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    Authorship：Last author,　Corresponding author   Publisher：openRxiv  

    Abstract

    Batch effects, which arise due to technical variations across different experimental factors, pose a significant challenge for single-cell transcriptomic analysis. Although various batch correction methods have been developed to mitigate these effects, they often indiscriminately mix data from different batches, leading to the removal of biologically meaningful signals. This limitation hinders comparative analyses across multiple conditions, an essential aspect of scientific research. Recent approaches attempt to address this issue by mapping data to separate spaces, but they prevent direct comparisons between conditions. Here, we propose Kanade, a batch correction method based on a variational autoencoder. Kanade explicitly disentangles batch effects from biological signals by specializing latent variables for different types of information. Using both simulated and real datasets, we demonstrate that Kanade selectively correct batch effects while preserving essential biological features, enabling more accurate comparative analyses in single-cell transcriptomics.

    DOI： 10.1101/2025.04.10.648296

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  17. Inverse signal importance in real exposome: How do biological systems dynamically prioritize multiple environmental signals?

    Thoma Itoh, Yohei Kondo, Tomoya Nakayama, Ai Shinomiya, Kazuhiro Aoki, Takashi Yoshimura, Honda Naoki

    bioRxiv     2025.4

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    Authorship：Last author,　Corresponding author   Publisher：Cold Spring Harbor Laboratory  

    Abstract

    Living organisms integrate multiple signals from their exposome—the totality of environmental influences experienced throughout life—to adapt to complex, non-stationary environments. While organisms are thought to flexibly prioritize relevant signals depending on context, its regulatory mechanisms remain largely unknown. Laboratory studies with precisely controlled conditions fail to capture this adaptability by isolating organisms from the complex exposome. Here, we developed a machine learning framework, Inverse Signal Importance (ISI), to infer how organisms prioritize external cues from time-series data of environmental factors and physiological responses. We applied ISI to analyze gonadal development in medaka fish under natural outdoor conditions, tracking gonadosomatic index alongside environmental signals including water temperature, day length, and solar radiation over two years. Our analysis revealed that signal importance levels exhibit complex dynamics distinct from simple environmental periodicity and correlates significantly with specific gene expression patterns. Notably, genes associated with temperature-related signal importance display differential expression between outdoor and controlled laboratory conditions, suggesting their role in environmental adaptation. These findings indicate that ISI effectively captures latent physiological dynamics in adaptation of exposome. By decomposing biological responses into deterministic and adaptive components, ISI provides a novel approach to uncover mechanisms of organismal adaptation in natural environments.

    DOI： 10.1101/2025.03.31.646257

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  18. Machine learning-guided reconstruction of cytoskeleton network from live-cell AFM images Reviewed Open Access

    Hanqiu Ju, Henrik Skibbe, Masaya Fukui, Shige H. Yoshimura, Honda Naoki

    iScience   Vol. 27 ( 10 ) page： 110907 - 110907   2024.10

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    Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

    DOI： 10.1016/j.isci.2024.110907

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  19. Adaptive discrimination of antigen risk by predictive coding in immune system. Invited Reviewed

    Yoshido K, Naoki H*

    iScience   Vol. 26 ( 105754 )   2023

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  20. A transcriptional program underlying the circannual rhythms of gonadal development in medaka. Reviewed Open Access

    Nakayama T, Tanikawa M, Okushi Y, Itoh T, Shimmura T, Maruyama M, Yamaguchi T, Matsumiya A, Shinomiya A, Guh YJ, Chen J, Naruse K, Kudoh H, Kondo Y, Naoki H, Aoki K, Nagano AJ, Yoshimura T

    Proceedings of the National Academy of Sciences   Vol. 120 ( 52 ) page： e2313514120   2023

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    DOI： 10.1073/pnas.2313514120

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  21. Few-shot prediction of amyloid β accumulation from mainly unpaired data on biomarker candidates. Reviewed Open Access

    Yada Y*, Naoki H*

    npj Systems Biology and Applications   Vol. 9 ( 59 )   2023

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    Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1038/s41540-023-00321-5

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  22. Single-cell transcriptome atlas of Drosophila gastrula 2.0. Reviewed Open Access

    Sakaguchi S, Okochi Y, Tanegashima C, Nishimura O, Uemura T, Kadota M, Naoki H, Kondo T*

    Cell Reports   Vol. 42 ( 112707 )   2023

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    Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1016/j.celrep.2023.112707

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  23. Decoding reward–curiosity coflict in decision-making from irrational behaviors. Reviewed Open Access

    Konaka Y, Naoki H*

    Nature Computational Science   Vol. 3   page： 418 - 432   2023

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    DOI： 10.1038/s43588-023-00439-w

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  24. Intercellular exchange of Wnt ligands reduces cell population heterogeneity in embryogenesis. Reviewed Open Access

    Hatakeyama Y, Saito N, Mii Y Shinozuka T, Takemoto T, Honda N, Takada S

    Nature Communications   Vol. 14 ( 1924 )   2023

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    Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1038/s41467-023-37350-x

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  25. Dopamine error signal to actively cope with lack of expected reward. Reviewed Open Access

    Ishino S, Kamada T, Sarpong G, Kitano J, Tsukasa R, Mukohira H, Sun F, Li Y, Kobayashi K, Naoki H, Oishi N, Ogawa M*

    Science Advances   Vol. 9 ( 10 ) page： eade5420   2023

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    Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1126/sciadv.ade5420

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  26. Multidimensional fractal scaling analysis using higher order moving average polynomials and its fast algorithm. Reviewed Open Access

    Ju H, Honda N, Yoshimura SH, Kaneko M, Shigematsu T, Kiyono K*

    Signal Processing   Vol. 208 ( 108997 )   2023

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  27. Optimal COVID-19 testing strategy on limited resources. Reviewed Open Access

    Onishi T*, Naoki H*, Igarashi Y*

    PLoS ONE   Vol. 18 ( 2 ) page： e0281319 [   2023

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    Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1371/journal.pone.0281319

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  28. Regulation of male germline transmission patterns by the Trp53-Cdkn1a pathway. Reviewed Open Access

    Kanatsu-Shinohara M, Naoki H , Tanaka T, Tatehana M, Kikkawa T, Osumi N, Shinohara T*

    Stem Cell Reports   Vol. 17   page： 1 - 18   2022

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    DOI： 10.1016/j.stemcr.2022.07.007

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  29. Stem cell homeostasis regulated by hierarchy and neutral competition. Reviewed Open Access

    Nakamuta S, Yoshido K, Naoki H*

    Communication Biology   Vol. 5 ( 1268 )   2022

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    Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1038/s42003-022-04218-7

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  30. Hierarchical modeling of mechano-chemical dynamics of epithelial sheets across cells and tissue. Reviewed Open Access

    Asakura Y, Kondo Y, Aoki K, Naoki H*

    Scientific Reports   Vol. 11   page： 4069   2021

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    Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1038/s41598-021-83396-6

    Open Access

  31. Model-based prediction of spatial gene expression via generative linear mapping. Reviewed Open Access

    Okochi Y, Sakaguchi S, Nakae K, Kondo T, Naoki H*

    Nature Communications   Vol. 12 ( 3731 )   2021

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    Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1038/s41467-021-24014-x

    Open Access

  32. Somite boundary determination in normal and clock-less vertebrate embryos. Reviewed Open Access

    Naoki H* and Matsui T

    Development, Growth & Differentiation   Vol. 62   page： 177 - 187   2020

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    Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1111/dgd.12655

  33. Noise-resistant developmental reproducibility in vertebrate somite formation. Invited Reviewed Open Access

    Naoki H*, Akiyama R, Sari DWK, Ishii S, Bessho Y and Matsui T

      Vol. 15 ( 2 ) page： e1006579   2019

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    Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1371/journal.pcbi.1006579

    Open Access

  34. Identification of animal behavioral strategies by inverse reinforcement learning. Reviewed Open Access

    Yamaguchi S, Naoki H*, Ikeda M, Tsukada Y, Nakano S, Mori I and Ishii S

    PLoS Computational Biology   Vol. 14 ( 5 ) page： e1006122   2018

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    Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1371/journal.pcbi.1006122

    Open Access

  35. Time-lapse observation of stepwise regression of Erk activity in zebrafish presomitic mesoderm. Reviewed Open Access

    Sari DWK, Akiyama R, Naoki H, Ishijima H, Bessho Y and Matsui T*

    Scientific Reports   Vol. 8 ( 4335 )   2018

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    Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1038/s41598-018-22619-9

    Open Access

  36. Discovery of long-range inhibitory signaling to ensure single axon formation. Reviewed International coauthorship Open Access

    Takano T, Wu M, Nakamuta S, Naoki H, Ishizawa N, Namba T, Watanabe T, Xu C, Hamaguchi T, Yura Y, Amano M, Hahn KM and Kaibuchi K*

    Nature Communications   Vol. 8 ( 33 )   2017

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    Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1038/s41467-017-00044-2

    Open Access

  37. Nonrandom contribution of left and right testes to germline transmission from mouse spermatogonial stem cells. Reviewed Open Access

    Kanatsu-Shinohara M*, Naoki H and Shinohara T

    Biology of Reproduction   Vol. 97 ( 6 ) page： 902 - 910   2017

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    Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1093/biolre/iox141

  38. Propagating wave of ERK activation orients collective cell migration. Invited Reviewed Open Access

    Developmental Cell   Vol. 43   page： 305 - 317   2017

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  39. Revisiting chemoaffinity theory: Chemotactic implementation of topographic axonal projection Reviewed Open Access

    PLoS Computational Biology   Vol. 13 ( 8 ) page： e1005702   2017

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    Authorship：Lead author,　Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1371/journal.pcbi.1005702

    Open Access

  40. Reconstruction of spatial thermal gradient encoded in thermosensory neuron AFD in Caenorhabditis elegans. Reviewed Open Access

    Tsukada Y, Yamao M, Naoki H, Shimowada T, Ohnishi N, Kuhara A, Ishii S and Mori I*

    Journal of Neuroscience   Vol. 36 ( 9 ) page： 2571 - 2581   2016

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    Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1523/JNEUROSCI.2837-15.2016

    Open Access

  41. Multi-phasic bi-directional chemotactic responses of the growth cone Reviewed International coauthorship Open Access

    Scientific Reports   Vol. 6 ( 36256 )   2016

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    Authorship：Lead author,　Last author   Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1038/srep36256

    Open Access

  42. Two new FRET imaging measures: linearly proportional to and highly contrasting the fraction of active molecules. Reviewed Open Access

    Yamao M, Aoki K, Yukinawa N, Ishii S, Matsuda M and Naoki H*

    PLoS One   Vol. 11 ( 10 ) page： e0164254   2016

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    Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1371/journal.pone.0164254

    Open Access

  43. Uncertainty-dependent extinction of fear memory in an amygdala-mPFC neural circuit model. Reviewed Open Access

    Li Y, Nakae K, Ishii S and Naoki H*

    PLoS Computational Biology   Vol. 12 ( 9 ) page： e1005099   2016

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    Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1371/journal.pcbi.1005099

    Open Access

  44. Nonrandom germline transmission of mouse spermatogonial stem cells. Reviewed Open Access

    Kanatsu-Shinohara M*, Naoki H and Shinohara T

    Developmental Cell   Vol. 38   page： 248 - 261   2016

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    Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1016/j.devcel.2016.07.011

  45. Intercellular propagation of extracellular signal-regulated kinase activation revealed by in vivo imaging of mouse skin. Invited Reviewed

    Hiratsuka T, Fujita Y, Naoki H, Aoki K, Kamioka Y and Matsuda M*

    eLIFE   Vol. 4   page： e05178   2015

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    Language：English   Publishing type：Research paper (scientific journal)  

  46. Distinct predictive performance of Rac1 and Cdc42 in cell migration. Reviewed Open Access

    Yamao M, Naoki H (Co-first), Kunida K, Aoki K, Matsuda M and Ishii S*

    Scientific Reports   Vol. 5 ( 17527 )   2015

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    Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1038/srep17527

    Open Access

  47. Heterogeneity in ERK activity as visualized by in vivo FRET imaging of mammary tumor cells developed in MMTV-Neu mice Reviewed Open Access

    Kumagai Y, Naoki H, Nakasyo E, Kamioka Y, Kiyokawa E and Matsuda M*

    Oncogene   Vol. 34 ( 8 ) page： 1051 - 1057   2015

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    Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1038/onc.2014.28

  48. Mathematical Modeling of Neuronal Polarization During Development. Invited Reviewed

    Progress in Molecular Biology and Translational Science   Vol. 123   page： 127 - 141   2014

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    Authorship：Lead author,　Last author,　Corresponding author  

    DOI： 10.1016/B978-0-12-397897-4.00003-6

  49. Fluctuation of Rac1 activity is associated with the phenotypic and transcriptional heterogeneity of glioma cells. Invited Reviewed Open Access

    Yukinaga H, Shionyu C, Hirata E, Ui-Tei K, Nagashima T, Kondo S, Okada-Hatakeyama M, Naoki H and Matsuda M*

    Journal of Cell Science   Vol. 127 ( 8 ) page： 1805-1815 (2014) - 1815   2014

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  50. Dynamic Regulation of Myosin Light Chain Phosphorylation by Rho-kinase. Invited Reviewed Open Access

    6

    PLoS One   Vol. e39269   2012

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    Publishing type：Research paper (scientific journal)  

    DOI： 10.1371/journal.pone.0039269

    Open Access

  51. Flexible Search for Single-Axon Morphology during Neuronal Spontaneous Polarization. Invited Reviewed

    Naoki H*, Nakamuta S, Kaibuchi K and Ishii S

    PLoS One   Vol. 6 ( 4 ) page： e19034   2011

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    Authorship：Lead author,　Corresponding author  

  52. Multi-cellular logistics of collective cell migration. Invited Reviewed Open Access

    PLoS One   Vol. 6 ( 12 ) page： e27950   2011

    　More details

    Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1371/journal.pone.0027950

    Open Access

  53. A multiphysical model of cell migration integrating reaction-diffusion, membrane and cytoskeleton. Invited Reviewed

    Nonaka S, Naoki H (Co-first)* and Ishii S

    Neural Networks   Vol. 24   page： 979 - 989   2011

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    Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1016/j.neunet.2011.06.009

  54. Noise-Induced collective migration for neural crest cells. Reviewed

    Yamao M, Naoki H and Ishii S

      Vol. 6352   page： 155 - 163   2010

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    Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1007/978-3-642-15819-3_20

  55. One-chip sensing device (biomedical photonic LSI) enabled to assess hippocampal steep and gradual up-regulated proteolytic activities Reviewed

    Tamura H, Ng DC, Tokuda T, Naoki H, Nakagawa T, Mizuno T, Hatanaka Y, Ishikawa Y, Ohta J and Shiosaka S*

    Journal of Neuroscience Methods   Vol. 173   page： 114 - 120   2008

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    Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1016/j.jneumeth.2008.06.002

  56. Stochastic control of spontaneous signal generation for gradient sensing in chemotaxis. Reviewed

    Naoki H*, Sakumura Y and Ishii S

      Vol. 255   page： 259 - 266   2008

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    Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1016/j.jtbi.2008.08.012

  57. Local signaling with molecular diffusion as a decoder of Ca2+ signals in synaptic plasticity. Reviewed Open Access

    Molecular Systems Biology   Vol. 1 ( 2005.0027 )   2005

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    Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1038/msb4100035

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To the head of Papers.▲

Presentations 14

  1. Decoding mental conflict in decision-making with inverse free energy principle Invited International conference

    Honda Naoki

    TSVP Symposium: Computational and Physical Understanding of Biological Information Processing  2025.3.6 

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    Event date： 2025.3

    Language：English   Presentation type：Oral presentation (invited, special)  

    Venue：Okinawa institute of science and technology  

  2. Data-driven Biology with Generative model Invited International conference

    Honda Naoki

    OIST's Visiting Program (TSVP): Computational and Physical Understanding of Biological Information Processing  2025.3.3 

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    Event date： 2025.3

    Language：English   Presentation type：Oral presentation (invited, special)  

    Venue：Okinawa institute of science and technology  

  3. Burst stem cell dynamics regulated by hierarchy and neutral competition International conference

    Honda Naoki

    2025.2.1 

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    Event date： 2025.1 - 2025.2

    Language：English   Presentation type：Oral presentation (general)  

    Venue：Cambridge University   Country：United Kingdom  

  4. 生命科学におけるデータ駆動アプローチ Invited

    本田直樹

    理論生物学スプリングスクール2025  2025.1.9 

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    Event date： 2025.1

    Language：Japanese   Presentation type：Oral presentation (invited, special)  

  5. Data-driven Biology

    2025.1.9 

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    Event date： 2025.1

    Language：English   Presentation type：Oral presentation (invited, special)  

  6. データ駆動生物学 Invited

    本田直樹

    創薬科学セミナー  2024.12.11 

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    Event date： 2024.12

    Language：Japanese   Presentation type：Oral presentation (invited, special)  

  7. 変異体1細胞RNA-seqデータから教師データなしに空間トランスクリプトームを再構成する機械学習 Invited

    本田直樹

    AMED-LINK  2024.12.11 

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    Event date： 2024.12

    Language：English   Presentation type：Oral presentation (invited, special)  

  8. Mathematical modeling of cytoskeleton driven cellular morphodynamics Invited International conference

    2024.11.20 

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    Event date： 2024.11

    Language：English   Presentation type：Oral presentation (invited, special)  

    Country：Japan  

  9. 心の揺れ・葛藤のデータ駆動的解読

    本田直樹

    大阪大学 蛋白質研究所セミナー 「意思決定の分子 ・回路・計算機構」  2024.11.11 

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    Event date： 2024.11

    Language：Japanese   Presentation type：Oral presentation (invited, special)  

  10. 最適輸送に基づく臓器連関の推定 Invited

    本田直樹

    シンポジウム「データサイエンスと生命医科学研究のフロンティア」  2024.10.21 

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    Event date： 2024.10

    Language：Japanese   Presentation type：Oral presentation (invited, special)  

  11. 認知症関連疾患における未病のデータ駆動的予測に向けて Invited

    本田直樹

    脳の世紀シンポジウム  2024.9.21 

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    Event date： 2024.9

    Language：Japanese   Presentation type：Oral presentation (invited, special)  

  12. 最適輸送理論に基づく1細胞RNA-seqデータ解析 Invited

    本田直樹

    日本数理生物学会年会 

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    Event date： 2024.9

    Language：Japanese   Presentation type：Oral presentation (invited, special)  

  13. データ駆動生物学：神経コネクトーム、空間トランスクリプトーム、多細胞動態 Invited

    本田直樹

    理論生物学夏の学校  2024.9.9 

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    Event date： 2024.9

    Language：Japanese   Presentation type：Oral presentation (invited, special)  

  14. 実環境における「生命体の動的情報処理」とその遺伝的基盤のデータ駆動的解読 Invited

    本田直樹

    APPW2025 (解剖/生理/薬理学会合同大会) 

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    Language：Japanese   Presentation type：Oral presentation (invited, special)  

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To the head of Presentations.▲

KAKENHI (Grants-in-Aid for Scientific Research) 5

  1. 新自由エネルギー原理の確立

    Grant number：21H05170  2021.8 - 2024.3

    日本学術振興会  科学研究費助成事業 学術変革領域研究(B)  学術変革領域研究(B)

    本田 直樹

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  2. あいまい環境に対峙する脳・生命体の情報獲得戦略研究の推進

    Grant number：21H05167  2021.8 - 2024.3

    日本学術振興会  科学研究費助成事業  学術変革領域研究(B)

    小坂田 文隆, 雨森 賢一, 本田 直樹

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    本領域「あいまい脳」では、脳・生命体の情報獲得過程に学び、予測があいまいな場合の意思決定を統一的に説明できる理論（新自由エネルギー理論）を新たに提唱し、その理論によって提唱される神経メカニズムを実験によって実証することを目指す。本領域は、総括班と3つの計画班から構成される。総括班は、研究領域全体の研究方針の舵取りを行い領域内での融合研究を促進し、領域内外の研究者の知見や技術の共有を促進し、領域の研究成果を社会に広め実社会での応用へと繋げる役割を担う。今年度は、本学術変革領域研究（Ｂ）の採択直後に、今後の研究推進や領域運営について話し合うためにキックオフミーティングを開催した。さらに、互いの研究グループの研究進捗や情報共有のために、領域会議を2回開催した。領域アドバイザーからも御助言を頂き、研究や領域運営に反映させた。加えて、「あいまい脳」領域のロゴを作製し、さらに研究内容のコンセプトをイラスト化した。本領域の研究内容や研究成果などを公表するために領域の公式HPおよびSNSのTwitterアカウントを作製した。

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  3. 多細胞動態を司る支配方程式のデータ駆動的解読

    Grant number：21H03541  2021.4 - 2025.3

    日本学術振興会  科学研究費助成事業 基盤研究(B)  基盤研究(B)

    本田 直樹, 青木 一洋

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  4. Data-driven identification of governing equations for multicellular dynamics

    Grant number：23K21716  2021.4 - 2025.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

    Honda Naoki

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    Authorship：Principal investigator 

    Grant amount：\13650000 （ Direct Cost: \10500000 、 Indirect Cost：\3150000 ）

    In this study, we aimed to extract the governing rules of multicellular dynamics during the wound healing process of epithelial tissue, using ERK activity data obtained through FRET imaging. By applying a hierarchical modeling approach, we hypothesized that cells integrate multiple gradient signals (e.g., cell density, velocity, and ERK activity) to determine their migration direction. Time-series FRET data were analyzed with image processing to track individual cells and quantify the gradients they experienced. Based on these data, we evaluated the contribution of each signal to cellular acceleration and estimated the corresponding response functions. As a result, cells exhibited distinct responses to each signal, and the estimated response functions enabled accurate prediction of their migratory behaviors.

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  5. 臓器連関の包括的理解に基づく認知症関連疾患の克服に向けて

    Grant number：JPMJMS2024  2020.12 - 2026.3

    内閣府 科学技術振興機構  戦略的な研究開発の推進 ムーンショット型研究開発事業 

    本田直樹

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    Authorship：Coinvestigator(s) 

    新規イメージング・計測・操作技術の開発などにより、脳と全身臓器ネットワークの機能とその破綻を分子・細胞・個体レベルで解明します。それにより、2050年には、認知症関連疾患の超早期の発症予測法と予防法を開発し、先制医療を享受できる社会の実現を目指します。

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To the head of KAKENHI (Grants-in-Aid for Scientific Research).▲
 

Teaching Experience (On-campus) 2

  1. 基盤医学特論

    2024

  2. 医療データ科学I

    2024

To the head of Teaching Experience (On-campus).▲

Teaching Experience (Off-campus) 1

  1. 計算・統計・数理生物学

    （京都大学生命科学研究科）

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To the head of Teaching Experience (Off-campus).▲