担当区分：最終著者,　責任著者   出版者・発行元：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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担当区分：最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1073/pnas.2516572123

Open Access

PubMed

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担当区分：最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Patterns  

Mutant analysis is the core of biological/pathological research, and measuring spatial transcriptomes can facilitate the understanding of the disorganized tissue phenotype. However, the high cost and technical challenges of spatial transcriptome experiments hinder the investigation of large numbers of mutants. Spatial transcriptomes have also been computationally predicted from single-cell RNA sequencing data using teaching data of spatial expression of certain genes, but the lack of teaching data for most mutants remains challenging. In various machine-learning tasks, zero-shot learning offers potential for predictions without teaching data. Here, we provided ZENomix, the zero-shot framework for predicting mutant spatial transcriptomes without teaching data (e.g., mutant spatial atlases). ZENomix accurately predicted spatial transcriptomes in Alzheimer’s model mice, Alzheimer’s human brains, and Nodal-signaling-deficient mutant zebrafish embryos. We proposed a ZENomix-based screening approach, identifying Nodal-downregulated genes in zebrafish. We expect that ZENomix offers phenotypic insights by leveraging the enormous amount of mutant/disease single-cell RNA sequencing data.

DOI： 10.1016/j.patter.2026.101521

Open Access

Scopus

PubMed

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担当区分：最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Neuroscience Research  

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

Open Access

Scopus

PubMed

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掲載種別：研究論文（学術雑誌）   出版者・発行元：Elsevier BV  

DOI： 10.1016/j.physa.2025.131171

Open Access

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.21769/bioprotoc.5714

Open Access

PubMed

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担当区分：最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Dialogues in Clinical Neuroscience  

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

Open Access

Scopus

PubMed

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担当区分：最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Plos Computational Biology  

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

Open Access

Scopus

PubMed

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Communications Biology  

Aversive signals such as pain serve an instructive role in aversive learning to promote animal survival. While negative valence of aversive signals is considered to be innately assigned, the valence can be scaled by internal state and previous experiences. However, the neuronal mechanisms underlying state and experience-dependent valence modulation remain unexplored. Previous studies demonstrated synaptic potentiation in instructive signal pathways following robust aversive learning. Here, we hypothesized that long-term potentiation (LTP) in the parabrachial-to-central amygdala (PB-CeC/L) pathway, an important nociceptive circuit for producing pain and emotional learning, enhances the negative valence and thereby alter future learning rules. To test this hypothesis, we developed pathway-specific in vivo LTP induction methods and mathematical models. Our results suggest that LTP in the PB-CeC/L pathway alters aversive valence and future learning rules by enhancing subsequent learning and memory generalization. These results may help to identify a therapeutic target for post-traumatic stress disorder.

DOI： 10.1038/s42003-025-08367-3

Open Access

Web of Science

Scopus

PubMed

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その他リンク： https://www.nature.com/articles/s42003-025-08367-3

担当区分：最終著者,　責任著者   出版者・発行元：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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担当区分：最終著者,　責任著者   出版者・発行元：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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担当区分：最終著者,　責任著者   出版者・発行元：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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担当区分：最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.isci.2024.110907

Open Access

PubMed

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DOI： 10.1016/j.isci.2022.105754

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1073/pnas.2313514120

Open Access

担当区分：最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1038/s41540-023-00321-5

Open Access

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.celrep.2023.112707

Open Access

担当区分：最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1038/s43588-023-00439-w

Open Access

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1038/s41467-023-37350-x

Open Access

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1126/sciadv.ade5420

Open Access

DOI： 10.1016/j.sigpro.2023.108997

Open Access

担当区分：責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1371/journal.pone.0281319

Open Access

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.stemcr.2022.07.007

Open Access

担当区分：最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1038/s42003-022-04218-7

Open Access

担当区分：最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1038/s41598-021-83396-6

Open Access

担当区分：最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1038/s41467-021-24014-x

Open Access

担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1111/dgd.12655

担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1371/journal.pcbi.1006579

Open Access

担当区分：責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1371/journal.pcbi.1006122

Open Access

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1038/s41598-018-22619-9

Open Access

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1038/s41467-017-00044-2

Open Access

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1093/biolre/iox141

DOI： 10.1016/j.devcel.2017.10.016

担当区分：筆頭著者,　最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1371/journal.pcbi.1005702

Open Access

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1523/JNEUROSCI.2837-15.2016

Open Access

担当区分：筆頭著者,　最終著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1038/srep36256

Open Access

担当区分：最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1371/journal.pone.0164254

Open Access

担当区分：最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1371/journal.pcbi.1005099

Open Access

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.devcel.2016.07.011

記述言語：英語   掲載種別：研究論文（学術雑誌）  

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1038/srep17527

Open Access

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1038/onc.2014.28

担当区分：筆頭著者,　最終著者,　責任著者  

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

DOI： 10.1242/jcs.139733

Open Access

掲載種別：研究論文（学術雑誌）  

DOI： 10.1371/journal.pone.0039269

Open Access

担当区分：筆頭著者,　責任著者  

担当区分：最終著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1371/journal.pone.0027950

Open Access

担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.neunet.2011.06.009

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1007/978-3-642-15819-3_20

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.jneumeth.2008.06.002

担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.jtbi.2008.08.012

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1038/msb4100035

開催年月日： 2024年12月

記述言語：日本語   会議種別：口頭発表（招待・特別）  

開催年月日： 2024年12月

記述言語：英語   会議種別：口頭発表（招待・特別）  

開催年月日： 2024年11月

記述言語：英語   会議種別：口頭発表（招待・特別）  

国名：日本国  

開催年月日： 2024年11月

記述言語：日本語   会議種別：口頭発表（招待・特別）  

開催年月日： 2024年10月

記述言語：日本語   会議種別：口頭発表（招待・特別）  

開催年月日： 2024年9月

記述言語：日本語   会議種別：口頭発表（招待・特別）  

開催年月日： 2024年9月

記述言語：日本語   会議種別：口頭発表（招待・特別）  

開催年月日： 2024年9月

記述言語：日本語   会議種別：口頭発表（招待・特別）  

記述言語：日本語   会議種別：口頭発表（招待・特別）