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#cell

14 Beiträge9 Beteiligte1 Beitrag heute
Rxiv mechanobio

📰 "Embedding physical symmetries into machine-learned reduced plasma physics models via data augmentation"
arxiv.org/abs/2506.14048 #Physics.Plasm-Ph #Dynamics #Cell

arXiv logo
arXiv.orgEmbedding physical symmetries into machine-learned reduced plasma physics models via data augmentationMachine learning is offering powerful new tools for the development and discovery of reduced models of nonlinear, multiscale plasma dynamics from the data of first-principles kinetic simulations. However, ensuring the physical consistency of such models requires embedding fundamental symmetries of plasma dynamics. In this work, we explore a symmetry-embedding strategy based on data augmentation, where symmetry-preserving transformations (e.g., Lorentz and Galilean boosts) are applied to simulation data. Using both sparse regression and neural networks, we show that models trained on symmetry-augmented data more accurately infer the plasma fluid equations and pressure tensor closures from fully kinetic particle-in-cell simulations of magnetic reconnection. We show that this approach suppresses spurious inertial-frame-dependent correlations between dynamical variables, improves data efficiency, and significantly outperforms models trained without symmetry-augmented data, as well as commonly used theoretical pressure closure models. Our results establish symmetry-based data augmentation as a broadly applicable method for incorporating physical structure into machine-learned reduced plasma models.
Rxiv mechanobio

📰 "Cell-mechanical parameter estimation from 1D cell trajectories using simulation-based inference"
doi.org/doi:10.1371/journal.po
pubmed.ncbi.nlm.nih.gov/405318
#CellMigration #Mechanical #Cell

doi.orgCell-mechanical parameter estimation from 1D cell trajectories using simulation-based inferenceTrajectories of motile cells represent a rich source of data that provide insights into the mechanisms of cell migration via mathematical modeling and statistical analysis. However, mechanistic models require cell type dependent parameter estimation, which in case of computational simulation is technically challenging due to the nonlinear and inherently stochastic nature of the models. Here, we employ simulation-based inference (SBI) to estimate cell specific model parameters from cell trajectories based on Bayesian inference. Using automated time-lapse image acquisition and image recognition large sets of 1D single cell trajectories are recorded from cells migrating on microfabricated lanes. A deep neural density estimator is trained via simulated trajectories generated from a previously published mechanical model of cell migration. The trained neural network in turn is used to infer the probability distribution of a limited number of model parameters that correspond to the experimental trajectories. Our results demonstrate the efficacy of SBI in discerning properties specific to non-cancerous breast epithelial cell line MCF-10A and cancerous breast epithelial cell line MDA-MB-231. Moreover, SBI is capable of unveiling the impact of inhibitors Latrunculin A and Y-27632 on the relevant elements in the model without prior knowledge of the effect of inhibitors. The proposed approach of SBI based data analysis combined with a standardized migration platform opens new avenues for the installation of cell motility libraries, including cytoskeleton drug efficacies, and may play a role in the evaluation of refined models.
Rxiv mechanobio

📰 "Embedding physical symmetries into machine-learned reduced plasma physics models via data augmentation"
arxiv.org/abs/2506.14048 #Physics.Plasm-Ph #Dynamics #Cell

arXiv logo
arXiv.orgEmbedding physical symmetries into machine-learned reduced plasma physics models via data augmentationMachine learning is offering powerful new tools for the development and discovery of reduced models of nonlinear, multiscale plasma dynamics from the data of first-principles kinetic simulations. However, ensuring the physical consistency of such models requires embedding fundamental symmetries of plasma dynamics. In this work, we explore a symmetry-embedding strategy based on data augmentation, where symmetry-preserving transformations (e.g., Lorentz and Galilean boosts) are applied to simulation data. Using both sparse regression and neural networks, we show that models trained on symmetry-augmented data more accurately infer the plasma fluid equations and pressure tensor closures from fully kinetic particle-in-cell simulations of magnetic reconnection. We show that this approach suppresses spurious inertial-frame-dependent correlations between dynamical variables, improves data efficiency, and significantly outperforms models trained without symmetry-augmented data, as well as commonly used theoretical pressure closure models. Our results establish symmetry-based data augmentation as a broadly applicable method for incorporating physical structure into machine-learned reduced plasma models.
Rxiv mechanobio

📰 "Coupling Anisotropic Curvature and Nematic Order: Mechanisms of Membrane Shape Remodeling"
arxiv.org/abs/2506.14347 #Physics.Bio-Ph #Morphogenesis #Cond-Mat.Soft #Cell

arXiv logo
arXiv.orgCoupling Anisotropic Curvature and Nematic Order: Mechanisms of Membrane Shape RemodelingThis study theoretically investigates how anisotropic curved membrane components (CMCs) control vesicle morphology through curvature sensing, nematic alignment, topological defects, and volume constraints. By comparing arc-shaped and saddle-shaped CMCs, we identify a rich spectrum of steady-state phases. For fully CMC-covered vesicles, arc-shaped components drive a pearling-to-cylinder transition as nematic interactions strengthen, while on partially CMC-covered vesicles, the saddle-shaped CMCs stabilize necks between the convex regions of bare membrane. We map the steady-state shapes of vesicles partially covered by arc-like and saddle-shaped CMCs, exposing how different vesicle shapes depend on the interplay between nematic interactions and volume constraints, revealing several novel phases. By investigating the in-plane nematic field, we find that topological defects consistently localize to high-curvature regions, revealing how intrinsic and deviatoric curvature effects cooperate in membrane remodeling. These findings establish a unified framework for understanding how proteins and lipid domains with anisotropic intrinsic curvature shape cellular structures -- from organelle morphogenesis to global cell shape.
Rxiv mechanobio

📰 "Scaling in two-dimensional Rayleigh-B\'enard convection"
arxiv.org/abs/2506.13213 #Physics.Flu-Dyn #Dynamics #Cell

arXiv logo
arXiv.orgScaling in two-dimensional Rayleigh-Bénard convectionAn equation for the evolution of mean kinetic energy, $ E $, in a 2-D or 3-D Rayleigh-Bénard system with domain height $ L $ is derived. Assuming classical Nusselt number scaling, $ Nu \sim Ra^{1/3} $, and that mean enstrophy, in the absence of a downscale energy cascade, scales as $\sim E/L^2 $, we find that the Reynolds number scales as $ Re \sim Pr^{-1}Ra^{2/3} $ in the 2-D system, where $ Ra $ is the Rayleigh number and $ Pr $ the Prandtl number, which is a much stronger scaling than in the 3-D system. Using the evolution equation and the Reynolds number scaling, it is shown that $ \tildeτ > c Pr^{-1/2}Ra^{1/2} $, where $ \tildeτ $ is the non-dimensional convergence time scale and $ c $ is a non-dimensional constant. For the 3-D system, we make the estimate $ \tildeτ \gtrsim Ra^{1/6} $ for $ Pr = 1 $. It is estimated that the total computational cost of reaching the high $ Ra $ limit in a simulation is comparable between 2-D and 3-D. The results of the analysis are compared to DNS data and it is concluded that the theory of the `ultimate state' is not valid in 2-D. Despite the big difference between the 2-D and 3-D systems in the scaling of $ Re $ and $ \tildeτ $, the Nusselt number scaling is similar. This observation supports the hypothesis of Malkus (1954) that the heat transfer is not regulated by the dynamics in the interior of the convection cell, but by the dynamics in the boundary layers.
Rxiv mechanobio

📰 "Chirality across scales in tissue dynamics"
arxiv.org/abs/2506.12276 #Physics.Bio-Ph #Cond-Mat.Soft #Dynamics #Forces #Cell

arXiv logo
arXiv.orgChirality across scales in tissue dynamicsChiral processes that lack mirror symmetry pervade nature from enantioselective molecular interactions to the asymmetric development of organisms. An outstanding challenge at the interface between physics and biology consists in bridging the multiple scales between microscopic and macroscopic chirality. Here, we combine theory, experiments and modern inference algorithms to study a paradigmatic example of dynamic chirality transfer across scales: the generation of tissue-scale flows from subcellular forces. The distinctive properties of our microscopic graph model and the corresponding coarse-grained viscoelasticity are that (i) net cell proliferation is spatially inhomogeneous and (ii) cellular dynamics cannot be expressed as an energy gradient. To overcome the general challenge of inferring microscopic model parameters from noisy high-dimensional data, we develop a nudged automatic differentiation algorithm (NADA) that can handle large fluctuations in cell positions observed in single tissue snapshots. This data-calibrated microscopic model quantitatively captures proliferation-driven tissue flows observed at large scales in our experiments on fibroblastoma cell cultures. Beyond chirality, our inference algorithm can be used to extract interpretable graph models from limited amounts of noisy data of living and inanimate cellular systems such as networks of convection cells and flowing foams.
Rxiv mechanobio

📰 "Machine learning accelerated finite-field simulations for electrochemical interfaces"
arxiv.org/abs/2506.10548 #Cond-Mat.Mtrl-Sci #Physics.Chem-Ph #Dynamics #Cell

arXiv logo
arXiv.orgMachine learning accelerated finite-field simulations for electrochemical interfacesElectrochemical interfaces are of fundamental importance in electrocatalysis, batteries, and metal corrosion. Finite-field methods are one of most reliable approaches for modeling electrochemical interfaces in complete cells under realistic constant-potential conditions. However, previous finite-field studies have been limited to either expensive ab initio molecular dynamics or less accurate classical descriptions of electrodes and electrolytes. To overcome these limitations, we present a machine learning-based finite-field approach that combines two neural network models: one predicts atomic forces under applied electric fields, while the other describes the corresponding charge response. Both models are trained entirely on first-principles data without employing any classical approximations. As a proof-of-concept demonstration in a prototypical Au(100)/NaCl(aq) system, this approach not only dramatically accelerates fully first-principles finite-field simulations but also successfully extrapolates to cell potentials beyond the training range while accurately predicting key electrochemical properties. Interestingly, we reveal a turnover of both density and orientation distributions of interfacial water molecules at the anode, arising from competing interactions between the positively charged anode and adsorbed Cl$^-$ ions with water molecules as the applied potential increases. This novel computational scheme shows great promise in efficient first-principles modelling of large-scale electrochemical interfaces under potential control.
Rxiv mechanobio

📰 "Tuning viscoelasticity and fine structure of living materials via synthetic adhesion logic and rheological perturbations"
doi.org/doi:10.1101/2025.06.04
pubmed.ncbi.nlm.nih.gov/405018
#Mechanical #Adhesion #Cell

bioRxiv · Tuning viscoelasticity and fine structure of living materials via synthetic adhesion logic and rheological perturbationsEngineered living materials (ELMs) at the multicelluar level represent an innovation that promises programmable properties for biomedical, environmental, and consumer applications. However, the rational tuning of the mechanical properties of such ELMs from first principles remains a challenge. Here we use synthetic cell-cell adhesins to systematically characterize how rheological and viscoelastic properties of multicellular materials made from living bacteria can be tuned via adhesin strength, cell size and shape, and adhesion logic. We confirmed that the previous results obtained for non-living materials also apply to bacterial ELMs. Additionally, the incorporation of synthetic adhesins, combined with the adaptability of bacterial cells in modifying various cellular parameters, now enables novel and precise control over material properties. Furthermore, we demonstrate that rheology is a powerful tool for actively shaping the microscopic structure of ELMs, enabling control over cell aggregation and particle rearrangement, a key feature for complex material design. These results deepen our understanding of tuning the viscoelastic properties and fine structure of ELMs for applications like bioprinting and microbial consortia design including natural systems. ### Competing Interest Statement The authors have declared no competing interest. NSF, 2214020, 2229070, 2143126 NIH, GM145893
Rxiv mechanobio

📰 "Mechanical force locally damages, remodels and stabilizes the lattice of spindle microtubules"
doi.org/doi:10.1101/2025.06.05
pubmed.ncbi.nlm.nih.gov/405016
#CellDivision #Mechanical #Cell

bioRxiv · Mechanical force locally damages, remodels and stabilizes the lattice of spindle microtubulesTo segregate chromosomes at cell division, the spindle must maintain its structure under force. How it does so remains poorly understood. To address this question, we use microneedle manipulation to apply local force to spindle microtubule bundles, kinetochore-fibers (k-fibers), inside mammalian cells. We show that local load directly fractures k-fibers, and that newly created plus-ends often have arrested dynamics, resisting depolymerization. Force alone, without fracture, is sufficient for spindle microtubule stabilization, as revealed by laser ablating k-fibers under local needle force. Doublecortin, which binds a compacted microtubule lattice, is lost around the force application site, suggesting local force-induced structural remodeling. In turn, EB1, which recognizes GTP-tubulin, is locally enriched at stabilization sites, both before and after force-induced fracture. Together, our findings support a model where force-induced damage leads to local spindle microtubule lattice remodeling and stabilization, which we propose reinforces the spindle where it experiences critical loads. ### Competing Interest Statement The authors have declared no competing interest. National Institutes of Health, R35GM136420 National Science Foundation, 1548297 CZ Biohub, https://ror.org/00knt4f32
Rxiv mechanobio

📰 "Tissue-like compression stiffening in biopolymer networks induced by aggregated and irregularly shaped inclusions"
doi.org/doi:10.1101/2025.06.02
pubmed.ncbi.nlm.nih.gov/405016
#Mechanical #Cell

bioRxiv · Tissue-like compression stiffening in biopolymer networks induced by aggregated and irregularly shaped inclusionsBiological tissues experience mechanical compression under various physiological and pathological conditions and often exhibit compression stiffening, in which their stiffness increases during compression, a phenomenon that plays a crucial role in regulating cell behavior and maintaining mechanical homeostasis. However, most isolated biopolymer networks, such as fibrin and collagen hydrogels that form the extracellular matrix and actin network that forms the internal cytoskeleton, undergo compression softening, raising questions about how tissues achieve compression stiffening despite the softening properties of their extracellular and intracellular matrix components. Previous studies have shown that spherical inclusions at large volume fractions can induce compression stiffening in biopolymer networks, but they do not account for the effects of aggregation and irregular morphologies of cellular assemblies or other components in tissues. Here, we demonstrate a novel mode of compression stiffening induced by aggregated or irregularly shaped inclusions that occurs at significantly lower volume fractions. Using carbonyl iron particles and coffee ground particles, we find that the morphological diversity of inclusions enables tissue-like compression stiffening at a low volume fraction of inclusions. Through a set of experiments and computational analyses, we demonstrate that these particles can percolate at low volume fractions. We further show that the percolation of stiff inclusions creates a stress-supporting network and enables tension-dominated stress propagation in fibrin fibers, both of which drive macroscopic stiffening during compression. These findings provide insights into the regulation of tissue stiffness and have implications for designing biomaterials with physiologically relevant mechanical properties for biomedical applications. Significance Statement Biological tissues experience a variety of mechanical forces. Many tissues, such as brain, liver, fat, and blood clots, become stiffer under physiological compressive loads, a property known as compression stiffening. In contrast, most biopolymer networks, which are the primary structural components for tissues, soften under compression. Here, we show that incorporating a small amount of aggregated or irregularly shaped particles into biopolymer gels induces robust compression stiffening. These inclusions percolate through the gel and rearrange non-affinely under compression, stretching surrounding fibers and contributing to mechanical reinforcement. Together, these effects reproduce tissue-like compression stiffening. Our findings not only provide new physical models for understanding tissue mechanics but also offer insights for designing biomaterials to achieve physiologically relevant mechanical responses. ### Competing Interest Statement The authors have declared no competing interest. US National Science Foundation, DMR-2309043, CMMI-1548571 National Institute of General Medicine Sciences, R35-GM-136259 National Institute of Biomedical Imaging and Bioengineering, R01-EB-017753 Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship
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