Biology subjects

Naoki, H.

Publications and source records attributed to Naoki, H..

At least 19 recordsLinked to original sources

bioRxiv2026

Spatially resolved mapping of tau amplification rates via differentiable simulation of prion-like propagation

Neurodegenerative diseases exhibit characteristic yet heterogeneous patterns of pathological spread, whose underlying determinants remain unclear. A central challenge is that inferring spatially heterogeneous propagation kinetics from neuroimaging data constitutes a high-dimensional inverse problem that has remained intractable at the whole-brain scale. Here, we present a differentiable reaction-diffusion framework that enables inference of spatially resolved tau amplification rates from tau PET data. By integrating MRI-informed forward simulation with error backpropagation, our approach reconstructs subject-specific voxel-wise maps of tau amplification rates across the human brain. Analysis of the inferred maps showed that the amplification rates have a positive spatial association with amyloid PET, suggesting that they capture aspects of regional vulnerability to amyloid-driven tau accumulation. Furthermore, integration with transcriptomic data identified gene expression programs associated with regional variation in amplification. These findings provide a data-driven framework linking molecular architecture to large-scale propagation dynamics in neurodegeneration.

neuroscience↗

bioRxiv2026

Monju: Multi-criteria clustering in single-cell omics

Clustering is a fundamental step in single-cell omics analysis. Although single-cell omics data can, in principle, be partitioned according to multiple biologically meaningful criteria, existing methods typically cluster cells using a single criterion. To address this problem, we developed Monju, a multi-criteria clustering method based on a deep generative mixture model. Monju divides cells into biologically reasonable submodels, each of which is equipped with an interpretable latent space. Furthermore, although the partitioning of cells into submodels varies across random seeds, each solution remains biologically plausible, collectively yielding multi-criteria clustering. Moreover, by integrating these multiple clustering solutions to perform meta-clustering, Monju enables the assessment of cluster stability. We applied Monju to human peripheral blood CITE-seq data and demonstrated that it can achieve multi-criteria clustering. Monju therefore provides a powerful and practical framework for dissecting cellular heterogeneity from multiple biological perspectives.

bioinformatics↗

bioRxiv2026

hB-PAC: A non-invasive aging clock for quantifying individual differences in aging

Quantifying differences in aging among individuals of the same chronological age could provide a direct measure of biological aging. However, existing methods often estimate biological age by predicting chronological age and treat such individual differences as prediction errors. We developed InferAging, a framework that estimates biological age by explicitly modeling individual deviations from chronological age. In progeroid zebrafish (klotho mutant; kl-/-), InferAging identified accelerated and delayed agers within the same chronological age group. A non-invasive variant using behavioral and morphological snapshots reproduced the transcriptome-integrated estimates without molecular input or lifelong tracking. The inferred aging state was associated with metabolic decline, intestinal barrier dysfunction, inflammation, and mucosal immune abnormalities beyond chronological age. These results demonstrate that non-invasive phenotypes can reveal molecularly supported individual aging states.

systems biology↗

bioRxiv2026

Inside insight: decoding how insight emerges from competing world models

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. Yet these latent dynamics remain difficult to access, even with behavior and verbal report. Here we developed inside insight dynamics (IID), a machine-learning framework that estimates latent world-model dynamics from behavioral data. Using IID, we analyzed mouse behavior in indirect- and direct-rule tasks, each requiring a shift from an initial world model to a rule-consistent representation. IID inferred the "when" of insight-like shifts by estimating the timing of transitions between competing world models, and examined the "how" by comparing alternative learning processes underlying them. This analysis revealed distinct mechanisms of world-model learning: the indirect- and direct-rule tasks were better explained by gated learning and parallel learning, respectively. Thus, IID opens a route to quantifying latent insight dynamics from observable behavior alone.

neuroscience↗

bioRxiv2026

Mind the gap: quantifying population-individual gap in depressive symptom dynamics through energy landscapes

People do not always feel as they appear. Someone who seems stable may struggle internally, whereas someone who appears distressed may experience it differently. This gap matters in psychiatry, where assessment relies on symptom scales and external evaluation. Here we developed mindGAP (Measuring INDividual-population GAPs in psychiatric energy landscapes), a hierarchical variational Bayesian framework that uses longitudinal questionnaire data to estimate population-level and individual-level pairwise maximum entropy models (pMEMs). We applied mindGAP to time-series PHQ-9 data from 248 participants. The population landscape contained three major states, whereas individual-level landscapes often diverged from this shared structure. We quantified this gap as individual-population landscape divergence, which was associated not only with depressive severity but also with modern-type depression-related traits (TACS-22) and interpersonal sensitivity-self traits (IPS-22). Thus, mindGAP opens a route to quantifying a previously unquantified gap between population-level and individual-level symptom organization.

bioinformatics↗

bioRxiv2026

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

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.

neuroscience↗

bioRxiv2026

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

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.

bioinformatics↗

bioRxiv2026

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

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.

biophysics↗

bioRxiv2025

Gradual proactive regulation of body state by reinforcement learning of homeostasis

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, and found that when regulatory priorities across variables were uneven, a deviation in one variable propagated through shared control to others, yielding a cascading, system-wide failure to return to ideal state (non-recovery)--a pattern reminiscent of autonomic dysregulation (e.g., dysautonomia, ME/CFS). Overall, our framework provides a computational basis to advances a systems-level understanding of multi-organ homeostatic dysregulation in vivo.

neuroscience↗

bioRxiv2025

Inverse modeling unveils governing law of mechano-chemical dynamics of epithelial migration

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.

systems biology↗

bioRxiv2025

Sperrfy the brain: A data-driven realization of Sperry's Chemoaffinity theory in the neural connectome

Understanding how brain-wide neural circuits are genetically wired remains a fundamental question in neuroscience. While Sperrys chemoaffinity theory 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 Sperrys theory at the whole-brain scale. By integrating connectomic data with spatial transcriptomic profiles from the Allen Mouse Brain Atlas, SPERRFY deciphers 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 (inter-regional) and local (intra-regional) organizational principles. Connectivity reconstruction based on these gradients achieves high predictive performance (AUC = 0.90), and permutation-based null models confirm the biological relevance of the inferred structures. Furthermore, SPERRFY identifies candidate genes that may contribute to positional wiring information, providing molecular insight into the developmental logic of brain-wide circuitry. Our results extend Sperrys foundational theory beyond the sensory domain, offering a unified, data-driven framework for understanding genetically encoded connectivity across the entire brain.

neuroscience↗

bioRxiv2025

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

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.

bioinformatics↗

bioRxiv2025

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

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.

bioinformatics↗

bioRxiv2025

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

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 signals 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.

bioinformatics↗

bioRxiv2024

Machine learning-guided reconstruction of cytoskeleton network from Live-cell AFM Images

How actin filaments (F-actins) are dynamically reorganized in motile cells at the level of individual filaments is an open question. To this end, we developed a high-speed atomic force microscopy (HS-AFM) to live-imagine intracellular dynamics of the individual F-actins. However, noise and low resolution made it difficult to fully recognize individual F-actins in the HS-AFM images. To tackle this problem, we developed a new machine learning method that quantitatively recognizes individual F-actins. The method estimates F-actin orientation from the image while improving the resolution. We found that F-actins were oriented at {+/-}35{degrees} toward the membrane in the lamellipodia, which is consistent with Arp2/3 complex-induced branching. Furthermore, in the cell cortex our results showed non-random orientation at four specific angles, suggesting a new mechanism for F-actin organization demonstrating the potential of our newly developed method to fundamentally improve our understanding of the structural dynamics of F-actin networks.

biophysics↗

bioRxiv2022

Zero-shot reconstruction of mutant spatial transcriptomes

Mutant analysis is the core of biological/pathological research, and measuring spatial gene expression can facilitate the understanding of the disorganised tissue phenotype1-5. The large numbers of mutants are worth investigating; however, the high cost and technically challenging nature of experiments to measure spatial transcriptomes may act as bottlenecks6. Spatial transcriptomes have been computationally predicted from single-cell RNA sequencing data based on teaching data of spatial gene expression of certain genes7; nonetheless, this process remains challenging because teaching data for most mutants are unavailable. In various machine-learning tasks, zero-shot learning offers the potential to tackle general prediction problems without using teaching data8. Here, we provide the first zero-shot framework for predicting mutant spatial transcriptomes from mutant single-cell RNA sequencing data without using teaching data, such as a mutant spatial reference atlas. We validated the zero-shot framework by accurately predicting the spatial transcriptomes of Alzheimers model mice3 and mutant zebrafish embryos with lost Nodal signaling9. We propose a spatially informed screening approach based on zero-shot framework prediction that identified novel Nodal-downregulated genes in zebrafish. We expect that the zero-shot framework will provide novel phenotypic insights by leveraging the enormous mutant/disease single-cell RNA sequencing data collected.

bioinformatics↗

bioRxiv2022

Prediction of amyloid β accumulation from multiple biomarkers using a hierarchical Bayesian model

Accumulation of amyloid-beta (A{beta}) in the brain is associated with neurodegeneration in Alzheimers disease and can be an indicator of early disease progression. Thus, the non-invasively and inexpensively observable features related to A{beta} accumulation are promising biomarkers. However, in the experimental discovery of biomarkers in preclinical models, A{beta} and biomarker candidates are usually not observed in identical sample populations. This study established a hierarchical Bayesian model that predicts A{beta} accumulation level solely from biomarker candidates by integrating incomplete information. The model was applied to 5xFAD mouse behavioral experimental data. The predicted A{beta} accumulation level obeyed the observed amount of A{beta} when multiple features were used for learning and prediction. Based on the evaluation of predictability, the results suggest that the proposed model can contribute to discovering novel biomarkers, that is, multivariate biomarkers relevant to the accumulation state of abnormal proteins.

neuroscience↗

bioRxiv2022

Decoding reward-curiosity conflict in decision-making from irrational behaviors

Humans and animals are not always rational. They not only rationally exploit rewards but also explore an environment, even if reward is less expected, owing to their curiosity. However, the mechanism of such curiosity-driven irrational behavior is largely unknown. Here, we developed a novel decision-making model for a two-choice task based on the free energy principle, which is a theory integrating recognition and action selection. The model successfully described irrational behaviors depending on the curiosity level. We then proposed a machine learning method to decode temporal curiosity from behavioral data, which enables us to quantitatively compare estimated curiosity and neural activities. By applying it to rat behavioral data, we found that the irrational choices sticking to one option was reflected to the negative curiosity level. Our decoding approach can be a fundamental tool for identifying the neural basis for reward-curiosity conflicts. Specifically, it could be effective in diagnosing mental disorders.

neuroscience↗