arXiv subjects

Shinji Deguchi

Publications and source records attributed to Shinji Deguchi.

8 recordsLinked to original sources

arXiv2026

Elastohydrodynamic coupling enhances flow generation by coordinated ciliary beating

Ciliary arrays pump fluid at low Reynolds number through non-reciprocal beating and phase coordination between neighbouring cilia. Previous studies have demonstrated that antiplectic metachronal waves are more effective than symplectic waves in enhancing transport, and have proposed several physically intuitive explanations for this preference. What remains incomplete is a predictive analytical understanding of how hydrodynamic coupling and beat geometry determine the flow-maximising phase difference. Here, we address this problem in two steps: we first use reinforcement learning to identify flow-maximising coordination in a bead--spring cilia model, and then introduce an analytically tractable reduced model, termed a tilted-slider model, to analyse the weak-coupling limit. Reinforcement learning identifies antiplectic coordination as the flow-maximising state in linear arrays, and shows that the phase difference between neighbouring cilia accounts for most of the flow enhancement. We then use the tilted-slider model to show that a shift of the time-averaged position opposite to the effective-stroke direction enhances fluid transport through its coupling with the elastic restoring force. The reduced model further reveals that antiplectic coordination can be optimal, consistent with previous studies, whereas symplectic coordination can instead become optimal depending on beat geometry. These results identify a simple elastohydrodynamic mechanism underlying flow-maximising metachronal coordination.

physics.bio-ph↗

arXiv2026

Cell-induced wrinkling patterns on soft substrates

Cells exert traction forces on compliant substrates and can induce surface instabilities that appear as characteristic wrinkling patterns. Here, we develop a mechanical description of cell-induced wrinkling on soft substrates using a thin film elastic framework based on the F\"{o}ppl-von K\'{a}rm\'{a}n equations coupled to a phase-field model of a single cell. We model in-plane contractile stresses driven by cellular activity and study how their magnitude, spatial distribution, and symmetry determine the onset of wrinkling and the resulting pattern selection. The theory predicts transitions between distinct morphologies, such as radial, circumferential, and anisotropic wrinkle arrangements, and provides scaling relations for wrinkle wavelength and amplitude as functions of elastic parameters and imposed cellular forcing. We compare these predictions with available experimental observations of cell-driven wrinkling on compliant gels and find good agreement for both qualitative pattern classes and quantitative wavelength trends. Our results offer a minimal modelling framework to interpret wrinkling assays and connect observed surface patterns to underlying cellular forces.

cond-mat.soft↗

arXiv2025

Optimal Undulatory Swimming with Constrained Deformation and Actuation Intervals

In nature, many unicellular organisms are able to swim with the help of beating filaments, where local energy input leads to cooperative undulatory beating motion. Here, we investigate by employing reinforcement learning how undulatory microswimmers modeled as a discretized bead-bend-spring filament actuated by torques which are constrained locally. We show that the competition between actively applied torques and intrinsic bending stiffness leads to various optimal beating patterns characterized by distinct frequencies, amplitudes, and wavelengths. Interestingly, the optimum solutions depend on the action interval, i.e.\ the time scale how fast the microswimmer can \rev{change the applied torques} based on its internal state. We show that optimized stiffness- and action-interval-dependent beating is realized by bang-bang solutions of the applied torques with distinct optimum time-periodicity and phase shift between consecutive joints, which we analyze in detail by a systematic study of possible bang-bang wave solution patterns of applied torques. Our work not only sheds light on how efficient beating patterns of biological microswimmers can emerge based on internal and local constraints, but also offers actuation policies for potential artificial elastic microswimmers.

physics.bio-ph↗

arXiv2024

Adaptive flexibility of cells through nonequilibrium entropy production

Cellular adaptation to environmental changes relies on the dynamic remodeling of subcellular structures. Among these, sarcomere structures are fundamental to the organization and function of the cytoskeletal architecture. In muscle-type cells, sarcomeres exhibit ordered structures of consistent lengths, optimized for stable force generation. By contrast, nonmuscle-type cells display a higher degree of structural variability, with sarcomeres of varying lengths that contribute not only to force generation but also to adaptive remodeling upon environmental cues. While these differences in sarcomere structures have traditionally been attributed to the unique properties of specific proteins expressed in each cell type, the functional implications of such structural variability remain unclear. Here, we present a nonequilibrium physics framework to elucidate the role of sarcomere variability in cytoskeletal adaptation. Specifically, we demonstrate that the effective binding strength of sarcomere components can be evaluated by analyzing structural randomness using Shannon entropy. The increased entropy associated with the inherent randomness of sarcomere structures in nonmuscle-type cells lowers the energy barrier for cytoskeletal remodeling, enabling flexible adaptation to environmental demands. Meanwhile, the ordered sarcomere arrangements in muscle-type cells correspond to higher binding energies and more stable cytoskeletal configurations. Although structural disorder is often regarded as unfavorable in terms of stability, our study suggests that it plays a key role in enabling adaptive responses in cellular systems.

q-bio.CB↗

arXiv2024

A statistical-mechanical framework for mechanically adaptive cytoskeletal organization

Living cells continuously remodel their cytoskeleton in response to mechanical cues. Although these responses have been extensively documented, it remains unclear why continuous changes in the mechanical environment give rise to distinct intracellular architectures rather than gradual structural variation. Here, we introduce a statistical-mechanical framework in which alternative cytoskeletal organizations are represented as ensembles of microscopic configurations, allowing configurational entropy to compete with mechanically dependent interaction energies. Rather than reproducing the full molecular complexity of the cytoskeleton, the model asks which features of mechanically adaptive organization emerge from this minimal physical description. The framework predicts three successive structural transitions corresponding to stress fiber formation, alignment, and lateral aggregation. When these transitions are placed on a common cellular-tension axis that increases with substrate stiffness, the predicted sequence is consistent with our measurements of correlation length and anisotropy in senescent fibroblasts. The preservation of this stiffness-dependent sequence despite altered cellular physiology suggests that the observed ordering reflects a robust physical principle rather than a cell-state-specific phenomenon. Together, these results establish a statistical-mechanical framework for understanding how continuous mechanical cues bias the statistical selection of distinct cytoskeletal architectures.

q-bio.CB↗

arXiv2024

Scale-dependent physical constraints on active intracellular fluctuations

Living cells exhibit nonequilibrium dynamics that shape intracellular processes across length scales, from nanoscale molecular assembly to the organization of macroscopic organelles. While dynamics at micrometer scales are known to be constrained by the actin meshwork at low frequencies, the physical principles governing active fluctuations at the nanoscale remain elusive. Here, we present an analytical framework integrating fluorescence correlation spectroscopy with nonequilibrium modeling to delineate the physical scaling of intracellular mechanics. Applying this framework to fibroblasts, we demonstrate that, in contrast to larger components, nanoscale active fluctuations remain prominent at high frequencies and are predominantly driven by local nonmuscle myosin II activity, establishing a distinct functional hierarchy in intracellular mechanics: local active forces promote rapid spatial exploration for nanoscale molecules, whereas macroscopic actin constraints ensure the structural stability required for larger molecular complexes and organelles. To integrate these scale-dependent behaviors within a single physical framework, we formulated a model that captures the transition of active fluctuations across length scales, revealing that the physical properties of the cytoplasm are governed by the balance between active driving forces and passive structural constraints. Furthermore, applying this model to cellular senescence reveals a reduction in nonequilibrium complexity associated with cytoskeletal rigidification. Thus, our findings bridge the dimensional gap between local molecular kinetics and macroscopic constraints, providing a fundamental physical basis for understanding the hierarchical organization of intracellular dynamics.

physics.bio-ph↗

arXiv2021

Wrinkle force microscopy: a new machine learning based approach to predict cell mechanics from images

Combining experiments with artificial intelligence algorithms, we propose a new machine learning based approach to extract the cellular force distributions from the microscope images. The full process can be divided into three steps. First, we culture the cells on a special substrate allowing to measure both the cellular traction force on the substrate and the corresponding substrate wrinkles simultaneously. The cellular forces are obtained using the traction force microscopy (TFM), at the same time that cell-generated contractile forces wrinkle their underlying substrate. Second, the wrinkle positions are extracted from the microscope images. Third, we train the machine learning system with GAN (generative adversarial network) by using sets of corresponding two images, the traction field and the input images (raw microscope images or extracted wrinkle images), as the training data. The network understands the way to convert the input images of the substrate wrinkles to the traction distribution from the training. After sufficient training, the network is utilized to predict the cellular forces just from the input images. Our system provides a powerful tool to evaluate the cellular forces efficiently because the forces can be predicted just by observing the cells under the microscope, which is a way simpler method compared to the TFM experiment. Additionally, the machine learning based approach presented here has the profound potential for being applied to diverse cellular assays for studying mechanobiology of cells.

physics.bio-ph↗

arXiv2019

Image based cellular contractile force evaluation with small-world network inspired CNN: SW-UNet

We propose an image-based cellular contractile force evaluation method using a machine learning technique. We use a special substrate that exhibits wrinkles when cells grab the substrate and contract, and the wrinkles can be used to visualize the force magnitude and direction. In order to extract wrinkles from the microscope images, we develop a new CNN (convolutional neural network) architecture SW-UNet (small-world U-Net), which is a CNN that reflects the concept of the small-world network. The SW-UNet shows better performance in wrinkle segmentation task compared to other methods: the error (Euclidean distance) of SW-UNet is 4.9 times smaller than 2D-FFT (fast Fourier transform) based segmentation approach, and is 2.9 times smaller than U-Net. As a demonstration, we compare the contractile force of U2OS (human osteosarcoma) cells and show that cells with a mutation in the KRAS oncogne show larger force compared to the wild-type cells. Our new machine learning based algorithm provides us an efficient, automated and accurate method to evaluate the cell contractile force.

physics.bio-ph↗