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bioRxiv2026

Establishing label-free quantitative X-ray dose-response profiling by Holo-Tomographic Flow Cytometry

Flow cytometry (FC) offers multiparametric analysis capabilities that can quantify cellular damage after exposure to cytotoxic agents. Here, we present a comprehensive study establishing a label-free quantitative X-ray dose-response profiling using a novel FC modality based on 3D Quantitative Phase Imaging, termed Holo-Tomographic Flow Cytometry (HTFC). This approach enables fully label-free 3D refractive index (RI) measurements, allowing detailed and quantitative characterization of the biophysical properties and morphology of living cells exposed to X-rays. By analyzing datasets of 3D RI tomograms from cells irradiated at graded doses, we identify intracellular biophysical markers that define a robust X-ray dose-response curve. Validation against standard clonogenic survival assays on three model cancer cell lines reveal a high correlation (>90%). HTFC not only eliminates labeling and operator bias but also markedly reduces experimental time from 1-2 weeks to 24 hours, offering a fully automated and objective readout. While clonogenic survival remains the benchmark for radiosensitivity assessment, our findings establish HTFC as a powerful label-free platform for fast assessment of radiation damage. This technology paves the way for predictive biosensors that can capture patient-specific responses, thereby supporting the transition from conventional, uniform radiotherapy protocols to personalized treatment strategies.

biophysics↗

bioRxiv2026

The glycine-arginine-rich motif of 53BP1 modulates RNA interactions necessary for its liquid-liquid phase separation during DNA Damage Response

The DNA damage response relies on the rapid assembly of repair factors into foci with properties of liquid-liquid phase separation, driven by de novo transcription of damage-induced RNAs. 53BP1 is a key component of these condensates, yet the molecular determinants driving this process remain unknown. Here, through computational, structural and in vitro approaches, we identify the oligomerization domain of 53BP1 and its glycine-arginine-rich (GAR) motif as crucial for RNA interactions and phase separation. Biophysical characterization reveals that 53BP1-RNA condensates can progressively mature into a more stable state, and that GAR mutants display aberrant material properties. Using a cellular model of telomere fusion events, we demonstrate that the GAR motif is essential for 53BP1-mediated DNA repair, which depends on the combined contributions of RNA binding and appropriate condensate biophysical properties. Therefore, RNA-driven 53BP1 condensation is functionally required to maintain genome integrity.

biophysics↗

bioRxiv2026

Sub-Millivolt Voltage Imaging Reveals Gap Junction-Mediated Bioelectric Contact Inhibition

Sub-millivolt membrane potential (Vm) dynamics in multicellular non-excitable networks have remained largely invisible due to a lack of sufficiently sensitive imaging tools. Here, we introduce rEstus2s, a next-generation genetically encoded voltage indicator that overcomes this barrier by enabling high-resolution Vm imaging across the full physiological resting Vm range (-100 to 0 mV). Using rEstus2s, we uncover bioelectric contact inhibition (BCI), a fundamental biophysical principle where gap junction coupling acts as a passive noise filter to stabilize Vm. We demonstrate that the time-dependent variance of Vm (electrical volatility) relative to the number of cells (n) in a network follows a 1/n scaling law, reflecting a transition from stochastic single-cell behavior to collective electrical stability. While Ca2+-activated oncogenic ion channels, including ANO1 and KCa3.1, promote pronounced electrical volatility in isolated cells, BCI effectively attenuates volatility in electrically coupled networks. Disruption of gap junction coupling in cancer cells abolishes BCI and restores high electrical volatility. These findings establish a unifying biophysical framework for understanding how multicellularity maintains electrical homeostasis in health and disease. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=168 SRC="FIGDIR/small/701308v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@120fcb9org.highwire.dtl.DTLVardef@affa2aorg.highwire.dtl.DTLVardef@150ffa7org.highwire.dtl.DTLVardef@c05392_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2026

Bringing calorimetry (back) to life

Micro-calorimetry offers significant potential as a quantitative method for studying the structure and function of biological systems, for instance, by probing the excess heat released by cellular or sub-cellular structures, isothermal or not, when external parameters change. We present the conceptual framework of nonequilibrium calorimetry, and as illustrations, we compute the heat capacity of biophysical models with few degrees of freedom related to ciliar motion (rowing model) and molecular motor motion (flashing ratchets). Our quantitative predictions reveal intriguing dependencies of the (nonequilibrium) heat capacity as a function of relevant biophysical parameters, which can even take negative values as a result of biological activity.

biophysics↗

bioRxiv2026

Tensile Expansion Microscopy Applies Mechanical Force to Super-resolve Fixed and Image Live Cellular Samples

Understanding biophysical phenomena requires techniques that access biologically relevant spatial and temporal scales. Expansion Microscopy (ExM) is a sample preparation approach which achieves super-resolution spatial scales by leveraging osmotic forces in a swellable hydrogel to physically separate structures to distances larger than the diffraction limit of light. Yet, in traditional osmotic ExM only pre- and post- expanded samples can be imaged. Further, fragmentation, hydrogel deformation, and signal loss are common while requiring samples to be chemically fixed. Therefore, there is little control of the expansion, reproducibility can be challenging, and dynamics of biological samples at applicable temporal scales cannot be observed. Here, we develop Tensile Expansion Microscopy (TExM) to mechanically expand fixed and, notably, living cellular samples. Highly-stretchable and tough double network alginate- Ca2+/polyacrylamide hydrogels are expanded by tensile forces applied using an electromechanical iris expansion device during continuous imaging on a fluorescence microscope. We incorporate two-photon polymerized microscale fluorescent fiducial markers to track samples and distortion during expansion. The hydrogels controllably and repeatedly expand up to 3.3x with distortions less than 12 {micro}m across 1.3 mm2. TExM is first applied to fixed NIH 3T3 fibroblast cells with immunohistochemistry-stained microtubules, achieving super-resolutions of 100 nm. Then, TExM is demonstrated with living HeLa cells with internal fluorescent reporters showing increased cell size and cell-to-cell separation under 3.2x linear expansion. Overall, TExM allows for continuous, stepwise, and precise temporal modulation of lateral substrate strain, enabling real time monitoring of dynamics of both fixed and viable live cell processes at higher spatial resolutions. TExM can further investigate broad biophysical questions due to its compatibility with other analytical imaging methods that are sensitive to water or fixatives used in traditional osmotic ExM.

biophysics↗

bioRxiv2026

Decoding the Allosteric Paradox: A Dual Framework Integrating AI Cofolding Models with Landscape-Guided Interpretable AI Framework of Ligand-Protein Binding

Artificial intelligence (AI) has transformed prediction of protein structure and biomolecular interactions, yet modeling of allosteric regulation remains a persistent and unresolved challenge. We develop a dual explainable AI framework that systematically interrogates AI Co-Folding models AlphaFold3, Protenix, Boltz-2, Chai-1, and DynamicBind on rigorously stratified datasets of orthosteric and allosteric ligand-protein complexes. While all AI models excel in accurate modeling of orthosteric ligand binding, a universal and architecture-independent collapse emerges in prediction of allosteric complexes. The biophysical logic for this dichotomy is unveiled through physics-based lens of the energy landscape theory and local frustration analysis. Orthosteric binding creates dominant energetic funnels via ligand-induced minimal frustration quenching, while allosteric sites preserve neutral frustration landscapes in both apo and holo protein states. The findings show that conformational heterogeneity and evolutionary plasticity encoded in allosteric binding landscapes may conceal the recurrent recognition patterns AI models are trained to detect. By linking prediction outcomes to frustration landscapes, this study recasts AI shortcomings in allosteric ligand binding as diagnostic indicators of fundamental biophysical constraints, establishing a physics-informed framework that turns the allosteric blind spot into mechanistic insight for next-generation landscape-aware predictive tools.

biophysics↗

bioRxiv2026

Experimental Data Driven AI Framework for Flexible Protein Conformational Reconstruction

Deep learning has revolutionized structural biology by prediction with near experimental accuracy static protein folds from amino acid sequence alone. However, proteins function as dynamic ensembles of protein conformation states, and current sequence-only models often fail to capture the specific conformational states and heterogeneity dictated by cellular environments or ligand binding. While recent generative models can sample broad conformational landscapes, they remain unconstrained by physical reality, often hallucinating plausible but experimentally invalid states. Here, we present AlphaSAXS, an end-to-end framework that constrains artificial intelligence (AI) inference using Small Angle X-ray Scattering (SAXS) experimental solution scattering data. By integrating real-space pair distance distributions (P(r)) directly into the AlphaFold architecture, AlphaSAXS effectively steers the structural hypothesis toward the experimentally observed structures. We demonstrate that AlphaSAXS resolves documented failure modes of sequence-only models in Apo-Holo transitions, successfully distinguishing between states with identical sequences but distinct scattering profiles. Furthermore, we introduce a hybrid inference protocol that couples deep learning with biophysical hydration modeling, enabling the reconstruction of solution state protein ensembles compatible with experimental data. This work establishes a paradigm for experimentally guided AI, bridging the gap between probabilistic sampling and biophysical measurement.

biophysics↗

bioRxiv2026

Decoding Allosteric Grammar with Explainable AI Integrating Protein Language Models and Energy Landscape Analysis: Neutral Frustration at Allosteric Binding Sites Encodes Regulatory Versatility in Protein Kinases

Allosteric regulation enables protein kinases to integrate diverse cellular signals, yet the energetic organization principles that encode spatial and evolutionary diversity of regulatory binding sites lack a complete understanding. We introduce an explainable artificial intelligence (AI) framework that uses protein language models (PLMs) not as predictive endpoints, but as diagnostic probes of biophysical organization of regulatory regions. By integrating PLM-based binding site predictions with the energy landscape-based frustration analysis, we determine that the detectability of orthosteric and allosteric binding sites reflects their energetic embedding within the protein energy landscape. Using PLM predictions as unbiased probes across a structurally and functionally diverse dataset of 453 human kinases, we observe a striking and reproducible dichotomy in predictive behavior: orthosteric ATP-binding pockets are detected with high confidence, whereas allosteric sites consistently evade robust detection. Orthosteric catalytic sites reside within minimally frustrated, optimized energetic regions that are consistently detected with high confidence. In contrast, allosteric sites are enriched in neutrally frustrated zones, producing diffuse and context-dependent predictions, revealing that the "allosteric blind spot" arises from intrinsic biophysical design rather than algorithmic limitations. Atomic-resolution analysis of ABL kinase spanning multiple conformational states and complexes bound to diverse ligands provides mechanistic validation of this principle. The myristoyl allosteric pocket in ABL remains neutrally frustrated across complexes with physiological ligands, chemically diverse modulators, from allosteric inhibitors to activators, and conformations engaged with SH2-SH3 regulatory domains. We propose that allosteric sites are encoded in persistent neutrally frustrated regions optimized for context-dependent regulatory modulation. By using explainable AI to interrogate the energetic architecture of protein kinases, this work reveals how the organization of the protein energy landscape shapes functional plasticity and algorithmic detectability of regulatory binding sites.

biophysics↗

bioRxiv2026

Ionic strength modulates structural disorder and protein oligomerization in the marginally disordered Phd transcription factor

Some proteins combine sequence features that are typical both for folded proteins and intrinsically disordered proteins (IDPs). The borderline properties of these so-called "marginal" IDPs render their conformational ensembles highly sensitive to the environmental changes, which may be important for their function. Here, we investigate the prokaryotic transcription factor Phd, which regulates the phd-doc toxin-antitoxin module through an allosteric mechanism involving disorder-order transition. Using an ensemble of biophysical techniques, we show that the protein is completely disordered at low ionic strength, whereas increasing salt concentration promotes its collapse into a partially ordered monomeric state, followed by the formation of a structured dimer. Using a thermodynamic model, we decipher the linkage between ionic strength, protein stability, oligomer state and degree of disorder. Via small-angle X-ray scattering we derive the structural ensemble of dimeric Phd, revealing a gradation of disorder as a function of salt. The sequence and biophysical properties of Phd position it at the boundary between macroscopically distinct conformational ensembles, representing a large pool of states capable of engaging in functional disorder-to-order interactions, enabling Phd to act as conformational rheostat. Together with previous crystallographic data, this charts the full spectrum of disorder-to-order states in the bacterial transcription factor and underscores the structural plasticity of IDPs with the marginal sequence properties. SIGNIFICANCEWhile globular proteins adopt stable three-dimensional structures, intrinsically disordered proteins (IDPs) remain flexible and dynamically sample partially ordered states. This behavior is largely determined by amino acid composition; however, some proteins lie at the borderline between order and disorder. Here, we focus on such a protein, the Phd transcription, and show how its conformation changes from completely disordered to fully ordered. These transitions are modulated by ionic strength and binding to macromolecules, including homodimerization. Using a thermodynamic model, we map the Phd conformational space, revealing a broad ensemble of states with varying degrees of disorder. The borderline amino acids properties enable Phd to function as a conformational rheostat, coupling functional interactions to the series of graded order-disorder transitions.

biophysics↗

bioRxiv2026

Accurate protein stability prediction for small domains using mega-scale experiments

Predicting absolute protein folding stability is a long-standing challenge in biophysics, with broad applications in protein design and in understanding genetic variation and evolution. Physics-based simulations have shown limited success at predicting stability and are often computationally intractable, and machine learning methods have been constrained by the lack of sufficiently large experimental datasets. We recently introduced cDNA display proteolysis, a cell-free approach that can measure folding stability for nearly one million protein domains in parallel. Here, we applied this method to measure stability for 1.8 million diverse protein domains 60-80 amino acids in length primarily taken from the MGnify metagenomic database and spanning over 200,000 sequence families. Using this new "MGnify Stability dataset", we developed the predictive models SaProt{Delta}G and ESM3{Delta}G, which accurately predict absolute folding stability for small domains with root mean squared error of 0.8 kcal/mol over a 6 kcal/mol range (Spearman rank correlation of 0.88). These predictors show high accuracy at predicting effects of substitutions, insertions, and deletions, successfully identify global trends toward higher stability in thermophilic organisms, and improve discrimination of stable and unstable computationally designed proteins. Our results illustrate how megascale biophysical measurements can complement existing evolutionary and structural data to enable accurate absolute stability prediction for small domains.

biophysics↗

bioRxiv2026

Domain Dissolution in Supported Lipid Bilayers Triggered by Unsaturated Phospholipid Addition

Significant cellular processes, including protein sorting, signal transduction, and pathogen entry, amongst others, are associated with membrane microdomains, also known as lipid rafts. Lipid rafts, due to their unique biophysical properties compared to their surrounding environment, which stem from their distinct lipid and protein profiles, have garnered interest in methods and techniques that tune their coexisting liquid-ordered/liquid-disordered state, aiming to disrupt or destabilize them. Since cholesterol stabilizes the membrane domain, cholesterol-depleting compounds like cyclodextrin can be used to destabilize and disrupt the membrane rafts. Overall, given the membrane rafts importance in biological processes, it is crucial to understand the biophysical factors that influence its stability. In this study, we present a new method for disrupting and dissolving lipid rafts in a model system of phase-separated supported lipid bilayer (SLB) patches composed of DOPC, DPPC, and cholesterol. Using fluorescence microscopy to monitor the liquid ordered (Lo) and liquid disordered (Ld) phases of the SLB patches, we observed that adding DOPC liposomes causes a transformation of the co-existing Ld and Lo phases into a single-phase bilayer. On the other hand, adding liposomes that match the lipid content of the phase-separated SLB patch increase the areas of the existing Ld and Lo phases. This work also offers a new method for redistributing raft-localized molecules, confirmed by tracking the redistribution of cholera toxin bound to GM1 after domain dissolution with DOPC liposomes. The work describes an alternative method for dynamically altering membrane composition and dissolving domains via liposome addition, rather than lipid depletion or exchange.

biophysics↗

bioRxiv2026

Disentangling RNA evolution and thermodynamics in genomic language models

Genomic language models (gLMs) trained only on large-scale nucleic acid sequence data seem to capture signals of RNA structure, yet the specifics of how remain unclear. Using the categorical Jacobian (CJ) operation, a model-agnostic operation for querying pairwise dependencies, we systematically compared three flagship gLMs: RNA-FM, Evo 2, and gLM2. We found that CJ signals recover base pairs supported by evolutionary covariation analyses, consistent with findings in protein language models. Surprisingly, CJ also recovers base pairs lacking evolutionary support but predicted by biophysical nearest-neighbor models. Is it possible gLMs have "learned" RNA thermodynamics? We noticed nearest-neighbor RNA folding models often predict reflected structures when given reversed sequences, consistent with these models modular and grammar-like nature. We leveraged this observation to create a simple "mirror test" that we found gLMs routinely fail, indicating they have not learned generalizable biophysics-based rules for RNA structure. Nevertheless, their apparent thermodynamic signal potentially confounds interpreting gLM pairwise dependencies as evidence of evolutionary conservation. We therefore introduce a method using synthetic sequences as a control for detecting significant learned signal. Our results demonstrate that gLMs can mimic thermodynamics through learned sequence context rather than general physical principles, but solutions exist for disentangling patterns in language models.

biophysics↗

bioRxiv2026

Anionic bacterial sphingolipids increase membrane stiffness

Recent genetic and bioinformatic studies have led to the discovery that many bacterial species encode the genes required to produce sphingolipids. Shotgun lipidomic studies have identified numerous sphingolipid species with novel structures that do not exist in eukaryotic organisms. The impacts of these lipids on the biophysical properties of bacterial membranes have not yet been determined. In this study, we purify a novel anionic bacterial sphingolipid, ceramide phosphoglycerate (CPG), and investigate its effect on membrane zeta potential and bending stiffness. CPG and its precursor, ceramide 1-phosphate (C1P), are shown to increase the magnitude of the membrane zeta potential. These sphingolipids also increase the stiffness of these membranes, with CPG increasing rigidity more than C1P or ceramide. This work provides experimental and computational methods of lipid isolation and characterization that may be broadly applicable to a variety of uncharacterized bacterial sphingolipids. SIGNIFICANCEThe diversity of bacterial sphingolipids far exceeds those found in eukaryotes. However, the function and biophysical properties of these lipids are unknown. Characterization of these lipids is a challenge as they are not commercially available. In this study, we developed experimental methods to purify the anionic sphingolipid ceramide phosphoglycerate and incorporate it into liposomes for analysis. Furthermore, we built computational tools to determine the bending stiffness of sphingolipid-containing vesicles from thermal fluctuation data.

biophysics↗

bioRxiv2026

Stretched Exponential Modeling Reveals Drug-Specific Kinetics in Human Engineered Skeletal Muscle

Understanding muscle tissue is key not only for advancing our knowledge in biophysical function of the human body, but can also open new paths to better description of pathological states, improve treatment and training and even provide insights for drug development. While using excised muscles is an excellent way to study muscular function, this limits access to non-human tissue, as human muscle tissue donation is very limited. The past decade has seen a rise in 3D in vitro models of human skeletal muscle, however many current experimental approaches to create such models often struggle with the high variability of primary cells or the limited translational relevance in cases where murine lines are used. Furthermore, the state-of-the-art functional analysis is typically focusing at peak force, which overlooks critical kinetic information. In this study, we present an systematic approach to generate functional engineered skeletal muscle tissues (ESMs), measure the contraction force dynamics and model these with a new approach to extract the effect of a series of standard pharmacological modulators. Within 14 days, LHCN-M2 ESMs mature into aligned, multinucleated myofibers expressing key sarcomeric markers and exhibiting robust excitation-contraction coupling. To decode these dynamics, we introduce a novel mathematical model based on stretched exponential functions. This framework accurately captures the heterogeneous contraction and relaxation phases across diverse phenotypes using only six interpretable parameters. We validated the platforms sensitivity using a library of pharmacological modulators. Our kinetic modeling revealed a distinct parameter fingerprint for different drug classes such as the specific changes of contraction kinetics that peak force alone could not detect. Additionally the presence of the necessary drug targets as well as the state of maturation were investigated by proteomic profiling. Together, study provides a scalable, high-fidelity human platform for high-content pharmacological screening and muscle biophysics.

biophysics↗

bioRxiv2026

Intersegmental transfers drive target search in an RNA-targeting CRISPR system

Sequence-specific RNA-binding proteins (RBPs) must efficiently locate their targets among a multitude of cellular RNAs. Cas13, an RNA-guided CRISPR protein, represents an ideal model system in which to study this search process. Cas13 combats bacteriophage infection by cleaving RNA nonspecifically upon binding of its crRNA to the target RNA sequence; thus, Cas13s search for its RNA target comes with a time constraint determined by the rate of phage multiplication. The mechanism by which Cas13 locates its target within this critical window remains unknown. Here, we investigate Cas13s mechanism of target search through integration of biophysical modeling, activity assays, and biochemical characterization. We show that Cas13 employs facilitated diffusion to accelerate its search, and find that Cas13s search time when targeting RNAs of different lengths cannot be explained by 1D sliding, the search mechanism used by many DNA-binding proteins. We propose that Cas13 primarily searches for its RNA target by intersegmental transfers (ITs), non-specifically binding the RNA at two locations and directly switching between them without fully dissociating from the RNA. We develop a biophysical model for ITs in an RNA context that we subsequently validate experimentally. Furthermore, we demonstrate that ITs can differentially accelerate the search process for a broad class of RNA-binding proteins, as opposed to their DNA-binding counterparts, due to RNAs short persistence length and the heterogeneity of RNA lengths in the cell. Our results illuminate how Cas13 achieves rapid target recognition in a complex RNA environment, and implicate ITs as a potentially widespread solution to the RNA search problem.

biophysics↗

bioRxiv2026

Dual Carbohydrate Recognition by the Chitinase-like Protein CHI3L1 Through Distinct Glycosaminoglycan and Chitin-Binding Interfaces

CHI3L1 (YKL-40) is a chitinase-like glycoprotein involved in immune regulation, tissue remodeling, and cancer, yet the molecular principles governing its glycan interactions remain incompletely defined. Previous reports suggested that CHI3L1 can bind to chitin oligosaccharides (COS) and glycosaminoglycan (GAG) ligands, however, the molecular basis and binding sites underlying these interactions remain controversial. Here, a combination of biophysical and computational methods is employed to shed light on carbohydrate interactions of the protein and delineate a potential crosstalk between its glycan-binding interfaces. Our results demonstrate that COS and GAGs bind to distinct, non-overlapping sites on CHI3L1. Both ligand classes exhibit a strong dependence of binding affinity on the degree of polymerization. Molecular dynamics simulations, supported by mutational analysis, identify a GAG-binding site centered on residues R144, R145, and K147 and reveal an additional distal interaction site for longer GAG ligands. Biophysical and biochemical assays fail to confirm a previously proposed allo- or orthosteric interaction between both binding sites. However, physiologically relevant protein-protein interactions mediated by the chitin binding site of CHI3L1 are differentially regulated by GAG and COS ligands. COS inhibit binding of galectin-3 to CHI3L1, whereas GAG ligands enhance the affinity between the proteins by ca. 14-fold. Together, these findings establish CHI3L1 as a dual carbohydrate-binding protein with distinct recognition interfaces and reveal a previously unrecognized role for GAGs in modulating CHI3L1-mediated signaling interactions.

biophysics↗

bioRxiv2026

Moderately Reduced Contractility Decreases Epithelial Cell-Cell Contact Rupture Under Large External Stretch

Epithelial sheets must maintain robust barrier function while enduring severe mechanical deformations across various physiological environments. While baseline actomyosin contractility is understood to stabilize intercellular junctions and hence cell-cell contact integrity, how cell-generated active forces interact with external physical strain to dictate contact integrity remains poorly understood. In this study, we investigated the biophysical trade-offs between actomyosin contractility and barrier resilience when Madin-Darby Canine Kidney (MDCK) cell islands are subject to large stretch. In contrast to a high concentration (50 M) of the non-muscle myosin II inhibitor blebbistatin that disrupted cell-cell contacts, we first identified a lower concentration (10 M) that maintained cell-cell contact integrity in the absence of any stretch. Such moderate inhibition of non-muscle myosin II reduced, but preserved some level of actin bundle organization. Remarkably, when challenged with a pathological 38% linear stretch using a custom-built biaxial stretching device, 10 M blebbistatin treated epithelial islands exhibited significantly fewer cell-cell contact ruptures than untreated controls, demonstrating a potent protective effect against mechanical strain. Traction force microscopy revealed diminished cell-generated strain energy by over 60% indicating a partial but significant reduction in contractility upon 10 M blebbistatin treatment. Nanoindentation measurements revealed that moderate contractility inhibition decreased the cellular Youngs modulus by more than 40%. Consequently, moderate contractility inhibition safeguards epithelial junctions through a dual mechanical effect: it simultaneously reduces baseline active tensile stresses due to cell contractility and lowers the passive elastic forces generated within the softened cell island during external stretch. Our findings indicate that this systemic reduction in forces dominates over any loss of biochemical adhesion strength at cell-cell contacts. We propose that shifting the epithelium from a rigid, highly stressed continuum to a more compliant, relaxed state by moderate contractility inhibition can serve as a general biophysical mechanism to preserve barrier integrity under severe mechanical challenge.

biophysics↗

bioRxiv2026

Fast Diffusion of Bound Ca: Analytical and Experimental Characterization of One- and Two-Dimensional Traveling Waves

Reaction diffusion (RD) systems play a fundamental role in numerous biochemical and biophysical processes. Here, we present a novel analytical framework for solving RD equations by applying the Wentzel Kramers Brillouin Jeffreys (WKBJ) formalism to Ca nanodomains generated by individual membrane channels, a widely used paradigm for intracellular Ca signaling. Previous models have primarily focused on stationary Ca nanodomains while neglecting diffusion and saturation of intracellular Ca buffers and sensors. In contrast, we derive analytical solutions without these simplifying assumptions. Our analysis demonstrates that sustained Ca influx generates continuously expanding distributions of free Ca, whereas Ca bound buffers and sensors propagate as traveling waves. These predictions are supported experimentally by measurements of one-dimensional fluorescence profiles produced by single-channel activity and two-dimensional profiles generated by whole cell Ca currents. The analytical framework developed here readily extends Michaelis Menten type kinetics to reaction diffusion systems and may therefore be broadly applicable to biochemical and biophysical processes in which diffusion cannot be neglected.

biophysics↗