The European Physical Journal E

Mechanical properties of epithelial tissues play essential roles in morphogenesis and physiological function. In this study, we analytically derived the in-plane bulk modulus, shear modulus, and Poissons ratio of a three-dimensional cell vertex model of epithelial monolayers. We showed that the model can robustly reproduce a near-zero in-plane Poissons ratio, a mechanical feature reported in cultured epithelial tissues. Numerical simulations further confirmed that the theoretically predicted Poissons ratio accurately describes the response of the model under finite, biologically relevant strains. In addition, the model exhibits not only morphological bistability between squamous-like and columnar-like states, but also mechanical bistability characterized by distinct elastic responses. Together, these results provide a minimal three-dimensional framework that links cell-scale mechanical interactions and epithelial morphology to tissue-scale elastic properties.

Cytoplasmic mechanical properties are often treated as constant background parameters, yet whether they change systematically during development remains unclear. Here, we directly measured cytoplasmic mechanics during early embryogenesis of Caenorhabditis elegans by establishing active microrheology using micrometer-sized magnetic droplets. Active microrheology revealed a progressive decrease in creep compliance from the 1-cell to the 8-cell stage, indicating a progressive stiffening of the local cytoplasmic environment during development. This decrease persisted even when cytokinesis was inhibited, demonstrating that it cannot be explained solely by geometric changes associated with cell division. Passive microrheology using 40-nm fluorescent beads showed a consistent decrease in probe mobility over development. Together, these results demonstrate that cytoplasmic mechanical properties undergo a gradual, developmentally programmed change during embryogenesis that cannot be explained by cell division-associated geometry alone.

Vertex models are widely used within the field of developmental biology to study tissue morphogenesis. These models are well-suited for modeling deformation at the cellular level where movement is driven by local forces. However, understanding how these microscopic movements coordinate to yield macroscopic phenomena such as the shapes of entire tissues remains a challenge. Here we study a top-down approach using differentiable programming on a simplified vertex model of a laminar tissue, and investigate whether the attributes of individual cells can be tuned to make the mesh as a whole acquire a predefined shape. We let the mesh evolve according to simple rules defined by the input to each polygon, and evaluate the resulting shape against a target boundary. Additionally, we show how the high degeneracy of the output can be reduced by constraining the polygon distributions: first, by adding simple penalties on tissue-wide attributes; and second, by dividing the tissue into regions, within which we bias the attributes toward characteristic values. Our study shows how a simple vertex model can be combined with differentiable programming to model developing tissues, and provides insight into the way individual cells must coordinate to yield macroscopic phenomena such as pre-programmed shapes.

Understanding how biological shape and movement interact with surrounding fluids represents a fundamental challenge at the intersection of biology, physics, and engineering. Fish locomotion exemplifies this challenge: body morphology and swimming kinematics together determine the hydrodynamic forces and flow structures that enable efficient propulsion and maneuverability. Whereas biologists have long sought to connect morphological variation to swimming performance, traditional morphometric approaches provide limited insight into the fluid mechanical consequences of shape differences. Similarly, although computational fluid dynamics can reveal detailed flow physics, simulating hydrodynamics across diverse and dynamic morphologies remains prohibitively expensive for systematic investigation. To bridge this gap, we introduce a data-driven framework that connects fish body shape dynamics to hydro-dynamic performance through compact morphospace parameterization and reduced-order modeling. Using CFD simulations of 15 fish species from the Digital Life Project database (www.digitallife3d.org/3d-model), we generate hydrodynamic datasets capturing the shape-flow relationship. Principal Component Analysis (PCA) extracts four dominant shape parameters from dorsal body profiles, which are then integrated into an Inverse-Design with Dynamic Mode Decomposition (ID-DMD) framework to model the resulting fluid dynamics. The resulting modal analysis suggests that locomotion strategies emerge from specific shape-flow interactions. We further demonstrate the frameworks utility through single- and multi-objective shape optimization, showing how it enables efficient exploration of the morphology-hydrodynamics relationship. This approach offers a novel analysis and design tool for understanding how biological form and motion interact with fluid mechanics, with applications ranging from bio-inspired vehicle development to evolutionary biomechanics.

With the development of microfluidics, it has now become possible to assess the susceptibility of bacteria to antibiotics at the single-cell level instead of relying on population measurements. Such studies are particularly relevant when the growth of bacterial population in the presence of antibiotics is heterogeneous. Here, we build a model to describe such a case, and apply it to experimental measurements on a small population of E. Coli exposed to ciprofloxacin, a drug which is well known for triggering a bistable response.

Take-off is a fast and energy-efficient strategy for bipedal animals, such as birds, to achieve rapid movement; however, how muscle physiology scales to govern this universal behavior remains unresolved. Research in other species physiologies is not readily applicable. As a result, important questions, whether theropod dinosaurs such as Tyrannosaurus rex were capable of jumping, remain unanswered. In this article, we coupled Lagrangian dynamics with Hills muscle equations and developed new experimental methods to quantify joint rotational stiffness and damping, thereby enabling a systematic description of lower-limb mechanics. The approach establishes a novel kinetic framework that links muscle contractile properties to lower-limb performance without invoking control optimization. Animal observations and tabletop mechanisms validate the framework. The mechanics model reveals that the take-off time of about 0.1 s across body masses of 0.003 to 90 kg is achievable, as heavier birds generate proportionally higher reaction forces. Additionally, Tyrannosaurus rex should be capable of jumping, based on the available physiology data. Beyond evolutionary insights, our framework provides a new methodology for analyzing the mechanical properties of biological joints and informing the design of scalable bio-inspired robots.

The Poisson-Nernst-Planck (PNP) system is an accurate model of electrodiffusion of ionic species. It is commonly used in situations where nanoscale resolution is required, for instance close to ion channels in the membranes of biological cells. The inherent stiffness of the equations has made them challenging to solve and has limited the applicability of the system. In particular, the time step required for stable solutions has typically needed to be very short (nanoseconds), which makes simulations on the time scale of an action potential (milliseconds) difficult. Recently, it has been observed that avoiding operator splitting and instead solving the concentration equations and the electrostatic equation in a coupled manner relaxes the time-step limitation considerably. However, no theoretical explanation of this observation has been provided. Here, we aim to explain why the coupled scheme allows much larger time steps. We illustrate the mechanism by considering special cases that define necessary, but not sufficient, conditions for stability. We also show that these conditions remain relevant for the fully coupled PNP model in 3D.

Flow and extensional deformation of mucin networks are fundamental in mucus biophysics, governing how mucus functions as a protective and lubricating, and transport-facilitating layer. While the shear and oscillatory rheology of mucin solutions have been characterized in considerable detail, their behavior under extensional deformation remains comparatively understudied. Here, we report a concentration-dependent transition in extensional flow response of mucin solutions using a bespoke dripping-onto-substrate extensional rheometer. We show that mucin solutions at the lower concentrations undergo linear filament thinning, whereas semidilute mucin solutions form highly extensible filaments, with radius decaying exponentially in time, consistent with the elastocapillary thinning observed in solutions of high molecular weight synthetic polymers. Remarkably, at higher mucin concentrations inter-chain mucin associations produce a sudden reduction in the apparent elastocapillary relaxation time. We demonstrate how increasing macromolecular concentration redistributes the balance between viscous and elastic stresses during capillary thinning in a biopolymer network and reveal a concentration-driven reduction in mucin filament extensibility. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/725541v2_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@1f593acorg.highwire.dtl.DTLVardef@1b23686org.highwire.dtl.DTLVardef@119add3org.highwire.dtl.DTLVardef@e31908_HPS_FORMAT_FIGEXP M_FIG C_FIG

Cell migration is fundamental to various biological processes, including morphogenesis, wound healing, and cancer metastasis. Durotaxis--directed migration of cells in response to spatial variations in stiffness--has been extensively studied using engineered substrates with prescribed stiffness. However, recent work has increasingly shifted toward understanding cell migration in fibrous matrices that can be actively remodeled by the actomyosin contractility, as commonly observed in tumor and epithelial cells. Despite these advances, a theoretical framework explaining how cells structurally remodel their surrounding matrix to promote their own durotaxis, and which cellular forces govern this behavior, remains elusive. To address this gap, we developed a biomechanical model in which polarized cells contract and migrate over a fibrous matrix. Using this model, we first confirmed that cells on an externally strained matrix preferentially migrate along the direction of applied strain. Then, we investigated how cells autonomously remodel the matrix to create stiffness patterns favorable for durotaxis. In the presence of intercellular adhesion, cells acted collectively to stiffen the matrix, after which a small subset of cells escaped the main population and migrated outward. This behavior is reminiscent of intravasation during cancer metastasis, where cohesive cell clusters generate local matrix remodeling that facilitates the departure of more motile subpopulations. These results illustrate how matrix stiffening driven by cell cohesion and contractility regulates durotactic behavior and provide mechanistic insight into collective invasion processes relevant to cancer metastasis.

Cell migration plays a central role in numerous physiological and pathological processes and emerges from the coordinated interplay between intracellular force generation, adhesion dynamics, and mechanical interactions with the environment. A minimal, mechanistically grounded understanding of these processes is required to disentangle the respective contributions of cell-intrinsic and environmental cues. Here, a two-dimensional in silico cell motility model is introduced to describe mesenchymal migration driven by intracellular traction forces generated within actin-rich protrusions anchored to a substrate. The model explicitly accounts for adhesion nucleation, maturation, force buildup and rupture, and relies on a small set of physically interpretable parameters. A systematic mechanical analysis identifies parameter regimes that permit effective cell translocation and delineates conditions leading to stalled or mobile cells. Within motile regimes, the model reproduces a broad spectrum of cell morphologies and migratory behaviours. In particular, cell trajectories exhibit the statistical features of a persistent random walk, with a crossover from ballistic to diffusive motion that arises solely from adhesion dynamics and force balance, without imposing polarization or directional bias. Cell morphology is shown to strongly regulate migration speed, persistence, and pausing behaviour. Altogether, this model provides a minimal reference framework for cell migration on non-deformable substrates and establishes a baseline for future studies of mechanically driven guidance. By construction, it is well suited for extension to deformable fibrous substrates, where cell-induced matrix remodeling and stiffness feedback are expected to bias migration and regulate cell encounters relevant to tissue morphogenesis and anastomosis.

Hydrodynamic synchronization of motile cilia is essential for biological functions such as fluid transport, locomotion, and developmental patterning. It comprises the generation and the response to local flows in complex geometries. Besides their central role in physiology, direct experimental tests of ciliary responses to local flows at cellular length and time scales have remained elusive, largely due to the absence of tools capable of applying controlled, and localized flow stimuli. Here, we introduce programmable, nanometer-thin Ti/Pt microactuators that generate well-defined hydrodynamic forcing at biologically relevant frequencies while operating at biocompatible sub-Volt voltages. This platform is pioneering a controlled local hydrodynamic stimulation of individual motile cilia. We quantify the flow fields and forces produced by single microactuators using particle image velocimetry. Applying local oscillatory flows close to motile cilia of the green alga Chlamydomonas reinhardtii, we probe their dynamic response by quantifying phase-locking between cilia and microactuators. This quantification is aided by combining machine-learning-based image segmentation, oscillator phase reconstruction, and circular statistics. During actuation, we observe signatures of phase-locking: those include a reversible modulation of the fluctuations in phase-difference between cilium and actuator and a systematic shift in ciliary beating frequency. Beyond providing a bio-compatible and precise platform for local hydrodynamic stimulation, our approach establishes an experimental framework for directly testing theories of hydrodynamic synchronization and load adaptation in systems of motile cilia.

Wild-type T4 lysozyme (T4L) is used as a benchmark to evaluate conformational sampling across generative AI, AI-accelerated molecular simulation (AMS), and physics-based enhanced molecular dynamics (EMD). A four-state model: exposed/open, exposed/closed, buried/open, and buried/closed; is defined using physically meaningful collective variables. While generative AI methods (AF-cluster, MSA subsampling of AlphaFold2, ConforFold, AlphaFlow, ESMFlow, ConfRover, BioEmu) largely sample only the exposed/open state, AMS integrating generative ensembles with iterative molecular dynamics, recovering all states and reproducing equilibrium populations similar to EMD and experimental smFRET signatures.

A major challenge in neuroscience is to predict how currents in nanodomains affect voltage and ionic concentrations. Cable and Rall theory provide analytic current-voltage relations by neglecting concentration gradients, and the impact of concentration gradients is usually studied numerically with the Poisson-Nernst-Planck (PNP) model. A precise quantitative understanding of the combined dynamics remains limited because analytic current-voltage-concentration relations are missing. In this work we derive such relations using a novel approach based on cross-diffusion equations. For narrow cylindrical domains, we derive time-dependent and steady-state expressions that explicitly show how currents affect voltage and ionic concentrations. We find that the influx of only one ion can significantly change the concentrations of all the other ions even if no channels for these ions are present. After a current injection we compute a biphasic voltage transient where the small-time asymptotic corresponds to the steady-state solution of the cable equation. We show that the accuracy of cable theory prediction for the voltage depends on how the current is distributed among the various ions. Finally, we develop an iterative method to accurately compute steady-state profiles for voltage and concentrations using first-order results by subdividing a cylinder into small segments.

Bacterial communities grow as dynamic populations that respond to their environments. A clinically relevant example is the inactivation of beta-lactam antibiotics by intracellular beta-lactamase in E. faecalis resistant strains. In these populations, resistant bacteria act as antibiotic sinks, detoxifying the environment and allowing sensitive bacteria to survive treatment through a cooperative interaction. In this work, we study strongly coupled planktonic and biofilm populations of mixed sensitive-resistant E. faecalis bacteria under antibiotic stress using fluorescent microscopy. The presence of resistant bacteria in the system benefits both resistant and sensitive cells, leading to mixed planktonic and biofilm populations at super-inhibitory drug concentrations. We show that a beta-lactam antibiotic with or without the addition of a beta-lactam inhibitor can lead to a population inversion effect, characterized by a non-monotonic relation between initial and final fractions of resistant bacteria. The effect is observed in both the planktonic and biofilm populations and is modulated by the total initial cell density. A well-mixed model with competition mediated by resource sharing and cooperation from global degradation of toxins predicts the experimentally observed behavior. These observations suggest underlying population-level mechanisms that are largely independent of biofilm spatial structure.

Characterizing conformational transitions between distinct structural states is essential for understanding protein function but remains challenging due to the timescale limitations of atomistic molecular dynamics. While coarse-grained models like Martini accelerate sampling, classical elastic-network or G[o]-like restraints often trap proteins in a single energy basin, precluding the study of transition pathways between distinct functional states. Here, we present CTGoMartini, a comprehensive Python package designed to simulate protein conformational transitions using G[o]-Martini models in explicit membranes. CTGoMartini addresses key methodological limitations of existing approaches by redefining native contacts as a dedicated interaction type, thereby eliminating spurious protein aggregation artifacts in multi-copy simulations. The package implements both switching and multiple-basin approaches (Exponential and Hamiltonian mixing) to sample transitions between experimentally defined states. Furthermore, it integrates Hamiltonian replica exchange molecular dynamics (HREMD) with PyMBAR analysis, enabling efficient optimization of mixing parameters that govern barrier heights and relative state stabilities. We demonstrate the power of CTGoMartini through two biologically significant membrane protein systems: (1) capturing the inward-open to outward-open transition of the lipid transporter SPNS2, revealing the molecular mechanism of S1P translocation; and (2) elucidating how membrane surface tension and anionic lipids (POPA, PIP2) modulate the conformational equilibrium of the mechanosensitive ion channel TREK1. By streamlining model construction, simulation, and analysis, CTGoMartini offers an easy-to-use platform that connects static structural snapshots with their underlying dynamic functional mechanisms. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/721921v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@75eb26org.highwire.dtl.DTLVardef@1a12accorg.highwire.dtl.DTLVardef@e927org.highwire.dtl.DTLVardef@1cb0dcd_HPS_FORMAT_FIGEXP M_FIG C_FIG

The patch-clamp experimental technique is widely used to study the electrical properties of ion channels in biological and artificial lipid membranes. The key to the high quality of the experiments is the manufacturing of glass pipettes that provide highly electrically resistant contact between the edge of the pipette tip and the lipid bilayer. Preparation of the pipettes is particularly challenging for studies of the mitochondrial membranes due to the need for very small pipette tip sizes. Here, we present a robust procedure for producing pipettes suitable for experiments with native mitochondrial membranes. This procedure involves a two-step approach: initial fabrication of relatively large glass micropipettes using a standard micropipette puller, followed by tip refinement using a microforger to achieve smooth glass surface and reduced opening size. Pipette tip diameters and surface structure were examined using field emission - scanning electron microscopy (FE-SEM) imaging to assess the effects of variable parameters on pipette geometry and size. The resulting pipettes were validated in patch-clamp recording of the mitochondrial inner membranes. This approach enables the reproducible production of optimized pipettes for mitochondrial patch-clamp experiments, improving the quality and throughput of electrophysiological recordings of the mitochondrial ion channels.

Coarse-grained structure-based models (CG-SBMs; or G[o] models) are simplified potential energy functions of biomolecules or biomolecular complexes that encode their structure. Molecular dynamics simulations of such SBMs have been successfully used to study long time-scale dynamics such as protein and RNA folding, and large conformational transitions of biomolecular complexes. SBMs have several advantages: (1) Their MD simulations are computationally inexpensive, making extensive sampling easily accessible to many researchers. (2) They are easy to modify and can be adapted for the specific biomolecular problem that needs to be investigated. However, the force-fields of SBMs are not usually included in commonly used biomolecular simulation packages resulting in a barrier to their use. Here, we present SuBMIT (Structure Based Models Input Toolkit; https://github.com/sglabncbs/submit), a toolkit for generating coarse-grained SBM input files for performing MD simulations with GROMACS and OpenMM/OpenSMOG. Simulations whose input files can be generated using the different flavors of CG-SBMs present in SuBMIT include the folding and conformational ensembles of proteins with intrinsically disordered regions, 3D-domain-swapping in proteins and the dynamics of RNA-protein assemblies (e.g., simple RNA viruses).

Although rhizosphere microbiomes are known to enhance plants resistance to water stress, it is believed that only fungi actively contribute to the transport and uptake of water. We investigated the biomechanical impact of bacterial motility on water transport in soil by combining surface tension measurements and water infiltration experiments in soil microcosms. We observed that flagellar-based motility in Bacillus subtilis cells reduces the apparent surface tension of fluids by up to 15%. The effect reported depends on cell density and swimming speed, confirming its biomechanical origin, and was able to accelerate water infiltration and rewetting of soil. We conclude that Bacillus subtilis facilitates soil water transport through the deformation of air water interfaces in pores.

Apohas Liquid State Intelligence Platform (LSIP) records ellipsometric waveforms from injections depositing sub-microgram quantities of antibody drop-by-drop onto a liquid reservoir. We previously showed that a behavioural feature extracted from the waveforms, VIBE1, identified antibodies carrying multiple biophysical liabilities in an industrial dataset of 71 monoclonal antibodies, and enriched for clinical failure across a larger dataset of 235 therapeutic antibodies [1]. Here, we use an auxiliary coalescence-sensor channel to decode VIBE1 by separating the coalescence event from its propagation through the substrate. The pertitration drop-to-drop standard deviation of pinch-off time,{sigma}{tau} , explains most of VIBE1s variance across the dataset (R2 = 0.92, n = 1182). High-speed imaging at 10,000 frames per second reveals that all imaged drops initially thin at the same Newtonian capillary-inertial rate while the neck remains wide. In drops from certain antibodies, the thinning bridge then decelerates as internal strain builds in the narrowing neck. This elasto-capillary stiffening response has a timescale{lambda} that decreases as pinch-off time{tau} i increases across the imaged set.{sigma}{tau} is therefore a readout of the antibodys propensity to undergo a transient gel-like stiffening response during coalescence, and that variability is what VIBE1 captures. The signal is concentration dependent, and absent in bovine serum albumin (BSA) tested at up to an order of magnitude higher molarity than the antibodies, despite BSA being a strongly surface-active globular protein. The instrument is configured so that complex behaviours of this kind appear in its recorded waveforms; the gel-like coalescence response we identify here is one such phenomenon.

Hygroscopic surfaces act as local vapor sinks that reshape the condensation field around them, but whether distributed biological structures do the same has not been investigated. We have established that hyphae of fungal colonies functionally behave as vapor sinks, creating a dry region of width{delta} around themselves when placed on a cooled substrate. In addition, the radial distribution of droplet sizes steepens during condensation, and the rate at which droplets evaporate locally after chamber drying increases. In order to quantify this behavior, we employed a combination of time-resolved imaging and survival analysis to determine how long individual droplets persist on the surface surrounding the colony. These data were used to derive three quantitative measures of the vapor-sink effect. Each measure was found to be directly proportional to the vapor-sink strength of the substrate, as calibrated against NaCl-agar hydrogels of known water activity (LOOCV RMSE = 0.031 for recovered aw). These findings were consistent across three fungal genera (35 experiments), and all species fell along calibration lines defined by the hydrogel standards. This result is consistent with a diffusion-limited vapor-depletion framework. The measured genus-level{delta} ratios agreed to within 6% of predictions from structural absorbing capacity, and field measurements on Gymnosporangium-infected apple leaves were consistent with the same signatures under natural conditions. These results establish a non-contact method for inferring the material properties of thin hygroscopic biological surfaces from their condensation patterns.