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Physics Physics Biological Physics

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Wednesday 14 October · still arriving

Biological Physics

Memory-resolved prediction of ligand residence times on the minutes timescale

Hossein Batebi, Lucas Tepper and Roland R. Netz

AI-enabled computational drug discovery is advancing rapidly in predicting molecular structures, binding poses and affinities, but ligand dissociation kinetics remain harder to access. Bound-state residence time, important for biological response and kinetic selectivity, is not fixed by equilibrium affinity but depends on the dissociation free-energy landscape and dissipative friction effects. Here, we show that restrained umbrella-sampling trajectories can be used to calculate residence times on the minutes timescale. Time-resolved coordinate fluctuations yield friction profiles that incorporate non-Markovian effects on dissociation kinetics. Combining them with the free-energy profile, we predict a residence time of $\tau_{\rm res}$ = 275 s for the adenosine A$_{2A}$ receptor with ZM241385 (comparable to the experimental value of 125 s) and reproduce the experimental ranking of wild type and two receptor mutants. For HSP90 with compound 18 and RNase S with the S15 peptide, we predict $\tau_{\rm res}$ = 2,169 s and 2,259 s (experimental values are 833 s and 2,439 s). Neglecting memory effects on residence kinetics overestimates $\tau_{\rm res}$ by factors of 2.6 to 29.
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Biological Physics

Full Three-dimensional Cell Geometry Matters for Epithelial Fluidization

Toshikaze Chiba, Jan Rozman and Matej Krajnc

Epithelial dynamics are often studied using two-dimensional models that omit cell height. Here, we investigate how this third dimension influences tissue fluidization driven by cell self-propulsion. We identify three effects absent from conventional 2D models. First, cell height controls fluidization through a reentrant transition: squamous and columnar tissues are more resistant to fluidization than cuboidal ones. Second, neighbor exchanges through transient scutoid states substantially enhance cell motility, compared to classic T1 transitions. Third, self-propelling cells develop pronounced apicobasal shape asymmetry, with the experimentally less accessible basal surface providing a more accurate indicator of cells' target shape index and motility.
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Biological Physics · Cross-listed from q-bio.QM

Maximum likelihood, neural networks and posterior mean for inferring fractional Brownian motion parameters

Daniel S. Han, Vaishnavi Ananthanarayanan and Matthew J. Simpson

Fractional Brownian motion (fBM) is a model of anomalous diffusion parameterised by a Hurst exponent $H \in (0,1)$ and a generalised diffusion coefficient $D_H > 0$. We jointly estimate $H$ and $D_H$ using maximum likelihood (MLE) from density measurements with Poisson counting noise and single-particle trajectory with Gaussian localisation error. We show that one density measurement in time cannot infer both $H$ and $D_H$ and that several time points are needed, which matters most for inference precision. For trajectories, we show that a neural network trained with a squared error loss approximates the posterior mean of $H$, which can be computed exactly at negligible cost. Paired with the profile-likelihood estimate of $D_H$, the posterior mean has the lowest root mean squared error in both $H$ and $D_H$, except near $H=0$ and $H=1$ where MLE estimates $H$ better. In vesicle trajectories from two cell types, $H$ and $D_H$ form a continuum rather than distinct modes of motion. The posterior probability that each window within a track is subdiffusive or superdiffusive shows that $26\%$ of MRC5 fibroblast and $21\%$ of HeLa vesicle trajectories contain both subdiffusive and superdiffusive windows, against $2\%$ and $7\%$ expected from estimation uncertainty alone. Intracellular cargoes therefore switch between subdiffusion and superdiffusion over time rather than forming populations with fixed $H$.
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Biological Physics · Cross-listed from q-bio.NC

How Long Is the Coast of a Neuron? Resolution-Dependent Interaction Order and Computational Capacity

Nima Dehghani

Mandlebrot's coastline paradox reframes measurement as a joint property of an object and its ruler. Here we ask how observational resolution shapes the apparent structure of neural computation. We introduce a scale-indexed decomposition that separates the predictive information captured by direct dependencies from the residual outside that model family. Exact constructions show that projection can convert higher-order dependence into pairwise structure. Projection can increase the direct fraction of surviving information even as total predictive information decreases. A schematic branch-to-soma cascade grounds these effects in local coincidence detection, saturating integration and somatic mixing. Resolving organized nonlinear subunits improves approximation and raises effective representational rank. In a nested hierarchy, these gains occur without increasing information about the target. This distinction separates information availability from access by a restricted readout. A point-neuron or direct population model therefore establishes an effective description at a specified scale. It does not uniquely identify the finer computation that generated the observed relation. Which resolution is adequate depends on the observable and perturbation of interest.
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Biological Physics

A dynamic mathematical model for sub-lethal damage recovery: Integrating recovery processes into multi-target theory to visualize damage states

Renta Shibata, Fuminobu Sato, Yoko Akiyama et al. (+1 more)

Quantifying the biological effects of radiation is essential for radiotherapy and radiation protection. While research has centered on statistical analyses of experimental data, this approach often fails to elucidate underlying mechanisms, limiting the development of generalizable models. Describing time dependent phenomena, such as sub-lethal damage (SLD) recovery during fractionated irradiation, presents a particularly significant challenge for existing static models. To address this, we developed a mechanistic mathematical model that extends the classical multi-target, single-hit model by introducing explicit recovery transitions among damage states, allowing the simultaneous description of radiation-induced damage accumulation and biological recovery. We applied this model to Elkind and Sutton's classical split-dose experimental data to determine its parameters. The model reproduced the major features of split-dose survival curves for treatments with 0-, 2.5-, and 23-hour recovery intervals. We visualized the temporal changes in damage state distributions within the cell populations. These simulations provided a process-level interpretation of the characteristic shoulder-like response appearing after an inter-fraction recovery interval. Furthermore, the estimated recovery half-life was about 120 minutes, which is broadly consistent with reported timescales of DNA double-strand break repair in mammalian cells, supporting the biological interpretability of the model parameters. The proposed framework provides a simple, interpretable description of radiation-induced cell death dynamics, including SLD recovery, and may serve as a basis for the improved mechanistic modeling of radiobiological effects under different irradiation protocols.
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All authors Renta Shibata, Fuminobu Sato, Yoko Akiyama, Yuichiro Manabe

Monday 12 October

Biological Physics

The point is a trap: Mathematics of Elastic States in Complex Multiscale Systems

Juan G. Diaz Ochoa

Observables in complex biological systems are often reduced in their inherent complexity, resulting in abstract, a priori mathematical representations based on simple concepts such as points in spaces or interconnected nodes in lattices or networks. This reduction is relevant not only for the formulation of multiscale mathematical models and field theories but also for experimental planning and data collection to gain systematic evidence for the nature of such systems, thus extracting robust causal relationships that would lead to better control of the system. However, mathematical abstraction remains challenging and problematic, especially given the tightly intertwined elements between objects and their environment, the system's affordance, and the fact that all these aspects limit fundamental mathematical concepts such as set theory, thus hindering the definition of phase spaces in extreme cases. In this work, I introduce a potential axiomatic representing a nonreducible representation of high order structures representing entanglement between observables in complex systems and their environment, considering not only their coupling but also their affordance in relation to the overall system and context, and thus intend to formalize concepts already used in complex systems such as Kantian wholes. In light of this axiomatic, I identify states that are rather less elastic with respect to their environment, discuss how low elasticity enables the application of set theory, while high elasticity limits the mathematical representation of complex systems, and present an exemplary Ising model with a network of elastic states with respect to different contexts as a limiting case for mathematical modelling in this context.
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Biological Physics · Cross-listed from cs.LG

From Simulated Fluids to Living Tissue: Zero-Shot Foundation Model Transfer to Perturbed Embryos

Louisa Cornelis, Haewon Jeong, Irina Espejo et al. (+1 more)

Morphogenesis is driven by the interplay of genetic regulation and tissue mechanics. In vivo video data is limited, particularly under genetic mutations and environmental perturbations, making generalization beyond the most representative conditions in the dataset challenging. However, at tissue scales, the dynamics of Drosophila can be described as a continuum flow, motivating the usage of representations learned across other physical systems as priors for developmental dynamics. We investigate finetuning Walrus, a Continuum Dynamics Foundation Model pretrained across a broad set of simulated continuous systems, on tissue velocity fields from only 12 wild-type Drosophila blastoderm videos. We evaluate autoregressive prediction both in-distribution and zero-shot under unseen temperature and genetic perturbations. Zero-shot refers to evaluation on perturbation conditions never seen during wild-type finetuning, without any additional training. In-distribution, a Fourier Neural Operator model trained from scratch achieves best performance on VRMSE. However, Walrus (CRPS) is the strongest model under the most severe distribution shift of unseen samples of mutated embryos and temperature conditions. Interestingly, predictions capture the shape of the mean surface tissue velocity trajectory and its separation between 17{\deg}C and 27{\deg}C, including the timing of the peak at Germ Band Extension onset, a biologically meaningful milestone of embryo development. Our results provide preliminary support that representations learned from different dynamical systems can transfer to experimental morphogenesis in developmental regimes, provided they admit a continuum description. More broadly, our results motivate further study of physics foundation models as a new complementary tool for the simulation of morphogenesis in low-data regimes.
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All authors Louisa Cornelis, Haewon Jeong, Irina Espejo, Payel Mukhopadhyay

Sunday 11 October

Biological Physics · Cross-listed from cond-mat.soft

Emergence of Active Foams in a Minimal Cognitive Flocking Model

Aryan Dwivedi, Diganta Bhaskar and Anil Kumar Dasanna

We investigate a minimal visual perception-driven model of self-propelled particles that interact through purely position-based cohesive steering within a finite vision cone, in the absence of velocity alignment and physical volume exclusion. Building on a previously proposed minimal cognitive model, we extend the interaction range from the previously studied short-range and distance-independent limits to an intermediate regime, enabling a systematic investigation of how a finite vision range influences collective behavior. We find that this regime exhibits a rich state diagram that combines characteristics of both limiting cases while giving rise to qualitatively new collective states. Besides the gas, nematic-band, and polar file states, we uncover the emergence of active foams – system-spanning fractal-like networks sustained by dynamically evolving particle streams. These structures arise solely from non-reciprocal vision-based interactions and are absent in both the short and long vision-range limits. For larger vision angles, the system self-organizes into a variety of aggregate morphologies, including condensates, rings, and multi-twist filaments, revealing a strong interplay between interaction range and anisotropic perception in shaping collective organization.
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Biological Physics

Sensing via whole-body interactions with complex terrains: physical insights from robotic models

Chen Li

In nature, animals interact with complex terrains and often generate forces, deformations, and vibrations across the body and environment. Such interaction-induced mechanical information may guide behavior, yet its generation and use are difficult to resolve because movement rapidly and continually reconfigures the interactions that produce it. Sensory neurophysiology and biomechanics have revealed how mechanical information is transformed within sensory, neural, and musculoskeletal subsystems, whereas studies in complex terrains reveal rich whole-body interactions and behavioral outcomes. Connecting these levels remains a challenge. Here, I present a focused perspective on how biologically guided robotic physical models can help identify candidate mechanical information generated through whole-body interactions, test its physical mechanisms and behavioral benefits, and generate biological hypotheses. Three case studies illustrate this approach, multi-legged locomotor transitions to traverse dense clutter, limbless vertical body bending to propel through uneven terrain, and rapid spider leg crouching to help vibration sensing of prey on a web. Across these systems, correspondence between robots and animals supports the biological plausibility of these candidate physical mechanisms and motivates better measurements and models to determine how the relevant mechanical information is sensed and used to modulate behavior.
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Biological Physics · Cross-listed from physics.optics

Dual deconvolution multifocal fluorescence response matrix microscopy for aberration-free high-throughput super-resolution imaging

Sumin Lim, Sanghyeon Park, Tran Thinh Le et al. (+3 more)

Fluorescence response matrix imaging enables separate computational correction of excitation and emission aberrations through dual deconvolution, but single-focus acquisition requires a large number of camera frames, limiting its practical use. Multifocal excitation can accelerate acquisition by recording many illumination positions in parallel, but denser foci also increase inter-focus crosstalk, which can bias the estimated excitation and emission optical transfer functions and degrade aberration correction. Here, we introduce high-throughput aberration-free fluorescence response matrix imaging with adaptive multifocal illumination, in which the inter-focus spacing of the multifocal lattice is adjusted according to specimen-dependent crosstalk. A programmable digital micromirror device generates multifocal excitation lattices, and the resulting detector-resolved fluorescence responses are assembled into a two-coordinate fluorescence response matrix. Dual deconvolution separately estimates and corrects the excitation and emission optical transfer functions, and the aberration-corrected matrix is then combined in the spatial-frequency domain to reconstruct the aberration-free image. This strategy reduces the number of required camera frames by approximately three orders of magnitude, allowing response matrices with $\sim 10^6$ illumination positions to be acquired within seconds to a few minutes. The accelerated acquisition preserves the aberration-correction capability of dual deconvolution and achieves an estimated lateral resolution of $\sim$100 nm. Applied to heterogeneous mouse brain and kidney tissue, the method enables high-resolution, depth-resolved volumetric imaging while correcting specimen-dependent aberrations. Together, these results establish adaptive multifocal illumination as a practical route to high-throughput aberration-free fluorescence response matrix imaging.
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All authors Sumin Lim, Sanghyeon Park, Tran Thinh Le, Jin Hee Hong, Seokchan Yoon, Wonshik Choi

Biological Physics · Cross-listed from q-bio.NC

Simple model of neurogliovascular unit activity with noradrenaline leading force

Andrey Yu. Verisokin, Darya V. Verveyko, Alexey R. Brazhe et al. (+1 more)

In this work we present a model of a neuroglivascular unit associated with a neuronal ensemble. We combine calcium dynamics in the astrocyte, the cascade of synthesis of arachidonic acid and its derivatives, as well as the activity of the network of inhibitory and excitatory neurons with changes in local blood flow. We add equations to the model for extracellular neurotransmitters such as GABA, glutamate, and norepinephrine. We show that the resulting model can exhibit different types of behavior, and also shows a significant connection between the functioning of the neural network and its supporting components.
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All authors Andrey Yu. Verisokin, Darya V. Verveyko, Alexey R. Brazhe, Stanislav V. Lagosha

Saturday 10 October

Biological Physics · Cross-listed from q-bio.QM

Data-Driven Discovery of a Hyperelastic Strain-Energy Function for Veins Using Constitutive Artificial Neural Networks

Marisa Bazzi, Divya Adil, Gavin Mays et al. (+4 more)

Veins are commonly subjected to altered mechanical loading in clinical settings such as vein grafting, arteriovenous fistula creation, and tissue-engineered vascular graft implantation, making accurate constitutive models of venous tissue clinically important. Venous mechanical behavior differs characteristically from that of arteries, and existing constitutive models struggle to simultaneously fit the circumferential and axial stress-stretch responses across vein types. Here, we present a constitutive artificial neural network (CANN) framework that discovers a compact, interpretable hyperelastic constitutive model from biaxial mechanical data across three types of veins (saphenous, jugular, and inferior vena cava). The framework incorporates an expanded candidate function library with structurally motivated terms representing progressive fiber recruitment and fiber-family interactions. Sobol sensitivity analysis, correlation-based pruning, and response-level family collapse were then used to reduce the discovered basis to a compact strain-energy density function (SEF). The resulting eight-term SEF achieved significantly higher fitting accuracy than established venous constitutive models across all three types of veins $(R^2>0.9, p<0.01)$. These findings establish a framework for constitutive model discovery in veins and propose a candidate constitutive form for further validation across larger, independent cohorts.
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All authors Marisa Bazzi, Divya Adil, Gavin Mays, Ellen Kuhl, Christopher K. Breuer, Jay D. Humphrey, Alison L. Marsden

Biological Physics · Cross-listed from cond-mat.soft

A Minimal Theoretical Model for Morphodynamic Selection in Soft Matter Systems

Weihua Mu and Da Wang

Soft matter systems often display shape transitions, mode competition, and threshold-like dynamical responses driven by the coupling between mechanics, geometry, and biochemical or active fields. Here we present a minimal theoretical framework for describing such morphodynamic selection using a small number of physically interpretable order parameters. The model combines elastic relaxation, active driving, and nonlinear feedback, yielding distinct regimes corresponding to weak deformation, single-mode selection, and coupled shape transformation. We derive the governing equations, identify the relevant dimensionless control parameters, and show how the phase behavior can be organized in terms of threshold competition and effective coupling strengths. The framework provides a compact route for connecting analytical theory, numerical simulations, and experimentally observable shape dynamics in soft and biological matter.
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Biological Physics · Cross-listed from cond-mat.soft

Active remodeling rescues a microswimmer from geometric trapping

Ayushi Bhatt, Nico Schramma, Jasper van der Gucht et al. (+1 more)

Natural habitats confront microorganisms with heterogeneous, mechanically complex surroundings that shape how they move and explore. Most studies of motility in complex media treat the environment as either a homogeneous fluid or a rigid obstacle field. Here we instead study a medium that is simultaneously soft and reconfigurable — deformable by the organism moving through it. We track the unicellular biflagellate microalga Chlamydomonas reinhardtii swimming in a soft, disordered suspension of microgel particles comparable in size to the cell itself, across a range of gel packing fractions. With increasing packing fraction, trajectories change from long straight runs to short, frequently reorienting ones, and the effective translational diffusivity decreases monotonically. The bimodal run/tumble speed distribution, characteristic of the free-swimming gait, collapses toward a single mode as the packing fraction increases, by near-proportional slowing of both gait states rather than a redistribution of cells between them. Recasting trajectories as alternating hops and traps reveals the signature of a deformable medium: hop lengths halve as the gel densifies, approaching the microgel aggregate scale, while trapping times do not increase with increasing packing fraction. Independent force estimates place the cell's propulsive force close to an order of magnitude above that needed to deform a single grain, so remodeling is expected rather than merely observed. A minimal active remodeler model — an active Brownian particle among mobile, rigid obstacles — shows that obstacle mobility alone is sufficient to relocate the onset of permanent trapping. Thus, our results establish obstacle mobility as a control parameter for motility in soft, disordered matter.
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All authors Ayushi Bhatt, Nico Schramma, Jasper van der Gucht, Uddalok Sen

Friday 9 October

Biological Physics · Cross-listed from cond-mat.soft

Biomolecular condensates and their pathologies: A colloidal, interfacial, and transport perspective

Vineeth Godavarti and Vinny Chandran Suja

Biomolecular condensates are micron-scale, membraneless compartments that concentrate proteins and nucleic acids by liquid-liquid phase separation (LLPS). Condensates play critical roles in transcription, mRNA metabolism, stress responses, and signaling across the cell, with their dysregulation increasingly correlated with several pathologies spanning neurodegeneration, cancer, cardiometabolic disease, and viral infections. The emergent biology of condensates can be cohesively viewed through the framework of colloidal, interfacial, and transport phenomena. Here we review the condensate literature through this joint lens, treating each condensate as a biological colloid whose behavior is captured by four measurable physical quantities: a Flory–Huggins interaction parameter that determines whether the dense phase forms at all, a partition coefficient that dictates the degree of client molecule accumulation, a viscoelastic relaxation time that sets whether the dense phase responds as a liquid or a solid, and an interfacial tension that governs droplet shape and interfacial dynamics. Each quantity is traced to its physical origin, its measured range in biological condensates, and the techniques that quantify it, for both three-dimensional droplets and two-dimensional membrane-tethered condensates. Interpreting disease as the displacement of these physical quantities from their functional ranges, we reorganize condensate pathologies into a small set of physical failure modes — saturation concentration dysregulation, aberrant aging and liquid-to-solid transitions, disrupted partitioning, interfacial aggregation, and pathogen hijacking — and show how the same parameters become design variables for condensate-targeted drugs and engineered coacervate delivery vehicles. The review concludes with open questions at the interface of condensate biology and colloidal and interfacial science.
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Biological Physics

Efficient Transport Networks through Edge Pruning by Relative Resistance

Mathieu Le Verge-Serandour, Viktor Vasilev, Lara Szeimies et al. (+1 more)

Transport networks are key to living and engineered systems. How can transport networks adapt efficiently using only local rules, without a global objective or specific boundary conditions? Inspired by observations in biological transport networks, we adapt networks by subsequently pruning edges by relative resistance, thus removing edges whose resistance is higher than the resistance for routing flow through the rest of the network. We find that adaptation by such a local, boundary-condition-free measure of redundancy, set by topology alone, yields sparse, loop-rich, low-dissipation networks.
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All authors Mathieu Le Verge-Serandour, Viktor Vasilev, Lara Szeimies, Karen Alim

Biological Physics · Cross-listed from cond-mat.soft

A new device for live cell imaging of biological samples under in-plane shear

Sidharth Kumar Das, Lyova Mamoyan, Eberhard Bodenschatz et al. (+1 more)

The effect of mechanical stresses and deformations upon the behavior and properties of cells and tissues is a topic of paramount interest in the field of biophysics. Its understanding is essential for deepening our comprehension of cellular responses to environmental stimuli, with implications in fields ranging from embryogenesis to tumor development. For boosting the understanding in this field, it is important to develop devices for the application of simple deformations in order to probe the cellular response to the basic mechanical transformations. While a number of stretching devices have been developed, in-plane shearing deformation has been little investigated. Here, a new device is introduced that allows the application of homogeneous in-plane shear upon a cell monolayer while imaging the sample with a commercial optical microscope. A description of the faced experimental challenges is reported and a full characterization is presented. It is demonstrated the capability of the device to apply reproducible, controlled and homogeneous shear of both single cells and confluent cellular monolayers.
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All authors Sidharth Kumar Das, Lyova Mamoyan, Eberhard Bodenschatz, Stefano Villa

Thursday 8 October

Biological Physics · Cross-listed from physics.med-ph

An anatomically grounded human-scale dynamic model of contraction-driven lymphatic pumping

Chia-Feng Chang, Ayemon Baraka, Egor Sidorov et al. (+1 more)

Lymphangions, the valved muscular segments of lymphatic vessels, pump lymph against an adverse pressure gradient. Current models resolve this pumping in one vessel, a short chain or a regional network, and whole-body human models are passive. We extend the passive whole-body network of a companion static study to the active regime on the same anatomical graph, with intrinsic contraction, dynamic valves and a respiration-gated venous outlet on every vessel. Without fitting the waveform or the contraction amplitude, the network settles into a bounded pumping cycle at the lymphovenous junction, at a clinical pressure of 7.5 to 12.3 mmHg and a drainage of 35 to 50 mm^3/s. Respiration adds a swing of 1 to 2.5 mmHg at the junction and contraction about 1 mmHg inside the duct. Pressure generation is limited more by load than by drive, and no single contraction amplitude fits every vessel. Raising the subclavian venous pressure from 3 to 15 mmHg lifts the deep cervical node from 8.6 to 21.1 mmHg at the weakest drive, a transmission of 1.04 that falls to 0.78 at the strongest, so contraction buffers cervical drainage against venous pressure. The same solver gives the human cervical outlet pressure that cerebrospinal-fluid drainage models prescribe.
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All authors Chia-Feng Chang, Ayemon Baraka, Egor Sidorov, Sabee Molloi

Biological Physics · Cross-listed from physics.med-ph

An anatomically grounded human-scale steady hydraulic model of the lymphatic network

Chia-Feng Chang, Ayemon Baraka, Egor Sidorov et al. (+1 more)

The lymphatic system returns 2 to 3 L of interstitial fluid per day to the venous circulation and drains the central nervous system. Most models of lymph transport describe one lymphangion or a short chain, so they cannot show how body-wide topology, calibre hierarchy and the venous outlet set pressure and flow. We build a passive steady hydraulic model on an anatomically derived graph of the human lymphatic system, with 1010 vertices and 1126 edges expanded to 89,060 sub-edges. Radii follow a Murray cube law, derived both from minimum work and from wall-shear-stress homeostasis. Pressure and flow follow from nodal hydraulic balance over Hagen-Poiseuille conductances, with an exponential wall law, hysteretic valves, Starling leak, node retention and a Windkessel outlet. Active contraction is off, and only the inflow scale is calibrated. Two quantities that are not fitted land in clinical range at the same operating point. Radii fall inside the vessel-class range, from 1.50 mm in the thoracic duct to 0.16 mm in the cervical collectors, and pressures stay below the 50 mmHg cuff scale on all sub-edges. About 49% of the gross load is removed before the outlet, nearly independent of the outflow scale and of the outlet parameters. On the route that drains the central nervous system, 30% of the superficial cervical load reaches the vein, at 10 to 18 mmHg along a segment that cervical-scale models replace with a prescribed outlet pressure. The model is a passive whole-body baseline for contraction-driven and clinical extensions.
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All authors Chia-Feng Chang, Ayemon Baraka, Egor Sidorov, Sabee Molloi

Biological Physics

Boundary-contact repolarization provides a mechanism for directional guidance of neural crest cell collectives

Aditya Shankar Paspunurwar and Hector Gomez

Neural crest cells (NCCs) provide a well-established system for investigating collective migration during embryonic development and exhibit migratory behaviors with parallels to cancer invasion. Previous studies establish roles for co-attraction, contact inhibition of locomotion, polarity persistence, and tissue confinement in guiding NCC migration. However, it remains unclear whether NCC migration in confined environments occurs through spatial restriction alone or also through a repolarization response induced by contact with boundaries of confinement. By developing a computational model that couples self-generated chemical transport with contact-mediated repolarization and mechanical interactions, we show that cell-boundary contact can reorient cell polarity and sustain directional migration under confinement, whereas no persistent collective direction emerges without confinement. Moreover, suppressing contact-mediated repolarization from non-permissive confinement boundaries while preserving the mechanical interactions prevents sustained directional migration, suggesting that spatial restriction alone is insufficient in the model. Together, these findings establish an experimentally testable mechanism through which self-generated chemical signals and contact with confining tissues jointly regulate collective guidance of NCCs. These insights provide a basis for investigating developmental migration defects and potential strategies to regulate collective tissue invasion.
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Biological Physics · Cross-listed from cs.AI

Ecology of AI Agents: Collaboration Creates a Population Threshold for Takeoff

Erin Crawley and Hidenori Tanaka

AI agents can now conduct real-world cyberattacks, scale up capabilities with the number of agents, and collectively pursue misaligned goals to obtain rewards. Together, these factors raise the risk of a population explosion of misaligned agents: agents could compromise computers and secretly deploy additional agents, creating a self-reinforcing cycle where larger populations develop greater collective cyber capability and expand further. This raises a fundamental question: What determines whether a population of misaligned agents remains contained or takes off into this self-reinforcing cycle? This population-level problem is ecological safety: unlike individual-agent or multi-agent safety with a fixed population, it concerns the dynamics of the population itself. Here, we develop an ecological theory of AI-agent populations based on a population growth equation in which fitness (growth rate) depends on cybersecurity capability. We show that, without collaboration, the population takes off only when individual-agent capability exceeds a critical threshold. With collaboration, however, collective cybersecurity capability increases with population size. This creates a critical population threshold: below it, the population declines; above it, the population takes off, even though individual-agent capability has not changed. In ecology, this phenomenon is known as the strong Allee effect. Because red teaming a small group of agents cannot guarantee ecological safety in larger populations, our theory calls for ecological red teaming and population pacing: gradually deploying larger agent populations in controlled environments, while measuring how cyber capability scales with population size, and estimating the critical population size for takeoff. Capability gains may lower this threshold, requiring re-estimation for each new model generation.
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Biological Physics

Femtosecond Three-Dimensional Imaging of Single-Protein with Hard X-ray Laser

Paul Lourdu Xavier, Andrew J. Morgan, Johan Bielecki et al. (+66 more)

The extremely intense pulses of X-ray free-electron lasers (XFELs) have enabled imaging of radiation-sensitive samples, such as macromolecular microcrystals, beyond radiation damage limits. These sources have the potential to deliver biomolecular single-particle imaging, similar to cryo-electron microscopy but without the need for cryo-fixation and with temporal resolution from femtoseconds to milliseconds. While this possibility was recognized before XFELs were built, the biological single-particle imaging work-flow has previously only been demonstrated on large virus particles. Based on decades of improvements in X-ray beam focusing, particle delivery, diffraction detection, and advanced analysis, here we demonstrate imaging of a single molecular complex, the giant-hemoglobin erythrocruorin (Ery) with X-ray laser pulses. Two-dimensional classes of diffraction patterns could be reconstructed to 15 Angstrom resolution, and 3D images to approximately 20 Angstrom, while the 3D merged intensity in reciprocal space extended beyond 20 Angstrom. The resolution discrepancy is likely due to heterogeneity caused by gas-phase compaction of the complexes. With increased throughput, this approach could be used to reveal in-situ structural details during mass spectrometry studies of biomolecules, while improvements in sample delivery may provide ultrafast snapshot imaging of biological single-particles in their native-state beyond the limitations of radiation damage.
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All authors Paul Lourdu Xavier, Andrew J. Morgan, Johan Bielecki, Amit K. Samanta, Simon Dold, Chufeng Li, Xinyue Gao, Spencer K. Passmore, Ruslan P. Kurta, Tim B. Berberich, Emiliano De Santis, Wenhui Zhang, Juncheng E, Faisal H. M. Koua, Romain Letrun, Luca Gelisio, Oleksii Turkot, Egor Sobolev, Lukas V. Haas, Jannik Lübke, Jingxuan He, Madeline B. F. Memovich, Kevin Janson, Stefanie Lenzen, Safi Rafie-Zinedine, Mansi Butola, Oleksandr Yefanov, Fabian Trost, Chan Kim, Tomas Popelar, Tokushi Sato, Yevheniy Ovcharenko, Rebecca Boll, Sergey Usenko, Thomas M. Baumann, Alberto De Fanis, Björn Senftleben, Matteo Porro, Andrea Castoldi, Ibrahym Dourki, Cyril Danilevski, Ekaterina Round, Christina Schmidt, Huijong Han, Roberto Alvarez, Ervin Chia, Cornelia Cazey, Carolin Seuring, Ruojie Sha, Maia Azubel, David A. Bushnell, Sébastien Boutet, Sergey Ovchinnikov, Adrian P. Mancuso, Michael Meyer, Richard Bean, Anton Barty, Filipe R. N. C. Maia, Richard A. Kirian, Thomas D. Grant, N. Duane Loh, Carl Caleman, Erik G. Marklund, Nadia Zatsepin, Andrew V. Martin, Saša Bajt, Roger D. Kornberg, Jochen Küpper, Henry N. Chapman

Biological Physics

Three-dimensional imaging of isolated membrane-protein complexes in vacuo with an X-ray laser

Kartik Ayyer, Tej Varma Yenupuri, Johan Bielecki et al. (+51 more)

The prospect of imaging single biomolecules, viruses and cells with intense, ultrashort X-ray pulses has driven the development of X-ray free-electron lasers (XFELs). However, the weak scattering from small particles is easily swamped by background from residual gas, which has so far limited applications to strongly scattering targets such as viruses, cell organelles and cells. Here we report a three-dimensional (3D) reconstruction of an isolated 1-MDa membrane-protein complex, photosystem I (PS I), from single-particle diffraction data. PS I trimers were aerosolised by charge-reduction electrospray ionisation and injected into the European XFEL beam, with partial helium gas exchange reducing background scattering by 80%. From 32 788 diffraction patterns of single trimers in random orientations, we reconstructed the 3D electron density to a resolution of 3.8 nm, limited by the detector geometry. The disc-shaped density, about 22 nm across and 10 nm thick, matches the size of a PS I trimer in a detergent micelle and is consistent with the compaction predicted by molecular dynamics simulations of the complex in vacuo and observed in native mass spectrometry. These results show that membrane-protein complexes can be imaged in vacuo with X-ray lasers, an important step towards ultrafast diffractive imaging of single macromolecules.
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All authors Kartik Ayyer, Tej Varma Yenupuri, Johan Bielecki, Lutz A. Eichacker, J. Domingo Meza-Aguilar, Louise J. Persson, Lena Worbs, Patrick Adams, Roberto Alvarez, Adil Ansari, Alfredo Bellisario, Rebecca Boll, Carl Caleman, Sebastian Cardoch, Andrea Castoldi, Joe P. J. Chen, Cyril Danilevski, Merle Dechering, Simon Dold, Armando D. Estillore, Alberto De Fanis, Lukas V. Haas, Patrick Konold, David Lomitze, Abhishek Mall, Dimitra Manatou, Iñaki de Diego Martinez, Parichita Mazumder, Andrew J. Morgan, Anna Munke, Yevheniy Ovcharenko, Matteo Porro, Safi Rafie-Zinedine, Amit K. Samanta, Christina Schmidt, Arezu Sehati, Jonas A. Sellberg, Björn Senfftleben, Carolin Seuring, Zhou Shen, Nicusor Timneanu, Sergey Usenko, Daniel Westphal, August Wollter, Tamme Wollweber, Paul Lourdu Xavier, Henry N. Chapman, Janos Hajdu, Richard A. Kirian, Jochen Küpper, Erik G. Marklund, Petra Fromme, Michael Meyer, Filipe R. N. C. Maia

Biological Physics · Cross-listed from q-bio.NC

If the brain were so simple: Information-based drivers of intelligence and their interaction through an n-body-inspired framework

Brett J. Kagan, Johnson Zhou, Forough Habibollahi et al. (+1 more)

Theories of how the brain processes information and returns intelligent outputs are numerous and often difficult to conclusively test. Here, we consider an approach grounded in informatics and algorithmic thermodynamics for how neural systems respond to different patterns of information in different ways, with a focus on the relationship between the information entropy of a signal and a new proposed quantity we term information enthalpy, which represents the internal resource of structured information within a system available to be used for predictions and other forms of work. To evaluate the capacity of the external signals to increase information enthalpy in the system, we introduce the information enthalpy potential (IEP) as a defined metric. We propose and implement a method for quantifying the amount of potential information enthalpy - the IEP - in a given signal across a range of example signals. We offer a conjecture of how information enthalpy may be treated by neural systems within a biological framework before showing how it may integrate and offer falsifiability to existing approaches such as the Free Energy Principle. This framework is also positioned in light of the role of neural criticality. Finally, we postulate a testable and falsifiable framework where the distinct mechanisms within each driver interact through a described n-body-inspired model to govern the "motion" of a neural system through a high dimensional state-space. Through these processes, it is proposed that these features form the most fundamental basis of the neural drivers that give rise to the complexity of adaptive behaviors that are commonly called: intelligence.
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All authors Brett J. Kagan, Johnson Zhou, Forough Habibollahi, Valentina Baccetti

Wednesday 7 October

Biological Physics

Polarization of the Drosophila oocyte by active cortical exchange

Mohammadhossein Firouznia, Wen Lu, Margot Lakonishok et al. (+3 more)

Anterior-posterior polarization in the Drosophila oocyte requires posterior localization of oskar mRNA. oskar mRNA is produced in the anteriorly situated nurse cells, moved into the oocyte, and assembled into oskar-Staufen ribonucleoprotein particles (RNPs). These RNPs then move through the oocyte and interact with the cortex, where myosin-V binds them to the actin network and kinesin-1 removes them along cortically anchored microtubules. This cortical tug-of-war takes place against an anterior-to-posterior decrease in microtubule density. How the interplay between cytoplasmic transport and cortical binding and unbinding produces a stable posterior cap is unclear. Here, we combine quantitative microscopy, photoconversion experiments, experimental perturbations, and biophysical modeling to show that posterior localization emerges from position-dependent cortical exchange coupled to cytoplasmic diffusion. Our model, which accounts for the tendency of RNPs to demix, suggests that two key parameters that determine the observed localization pattern are the total amount of oskar and the ratio of cortical unbinding to binding rates (the exchange equilibrium constant). Given oskar volume fractions in wild-type, the measured exchange constant is such that oskar localizes to the posterior cortex without demixing. If the balance is broken, say by increasing unbinding rates, oskar irreversibly demixes into cytoplasmic droplets. This is consistent with observations from our experimental perturbations. Our results show how bulk diffusion and cortical exchange, in the face of oskar RNPs' self-attraction, convert a cytoskeletal asymmetry into robust developmental polarity.
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All authors Mohammadhossein Firouznia, Wen Lu, Margot Lakonishok, Reza Farhadifar, Vladimir I. Gelfand, Michael J. Shelley

Biological Physics · Cross-listed from cond-mat.mtrl-sci

Ceramic Materials: From Atomic Bonding and Processing to Microstructure, Functional Properties, and Biomedical Applications

Jairo Rondon, Juan Valera, Alex Velez Cruz et al. (+1 more)

Ceramic materials are inorganic, predominantly nonmetallic solids whose performance is governed by the interplay among atomic bonding, crystal structure, processing, microstructure, and functional properties. Their ionic and covalent bonding provides high hardness, thermal and chemical stability, wear resistance, and diverse electrical behavior, but also limits plastic deformation and increases sensitivity to flaws, porosity, grain boundaries, and residual stresses. This review examines ceramics through the processing–microstructure–property–performance relationship, emphasizing the effects of crystal chemistry, defects, grain size, porosity, and phase stability on mechanical, thermal, electrical, and biological behavior. Conventional sintering, sol–gel processing, and additive manufacturing are discussed as key routes for controlling densification, porosity, geometry, and composition. Biomedical ceramics, including alumina, zirconia, calcium phosphates, hydroxyapatite, tricalcium phosphate, and bioactive glasses, are also reviewed. Emerging topics include nanoceramics, smart ceramics, composites, multimaterial manufacturing, defect engineering, immunomodulatory bioceramics, and sustainable processing. Major challenges remain in brittle fracture, processing heterogeneity, degradation control, scale-up, and the porosity–strength trade-off. Future advances will require integrating advanced manufacturing, multiscale characterization, predictive modeling, and data-driven design.
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All authors Jairo Rondon, Juan Valera, Alex Velez Cruz, Irela Perez Magin

Biological Physics · Cross-listed from physics.chem-ph

Cotunneling-Assisted Redistribution of Nonpassivity in a Photosynthetic Junction

Manjet Rajak, Trishna Kalita, Debasish Sharma et al. (+1 more)

We investigate how cotunneling affects passive-state ordering in a Photosystem II reaction center modelled as a nonequilibrium molecular junction. We use a full pigment excitation manifold, with excitonic and charge-transfer rates computed within a Nakajima-Zwanzig formalism using realistic spectral densities. Cotunneling is introduced through a microscopically constructed doubly reduced acceptor state obtained from relevant quinone-side two-electron configurations, while photoexcitation is allowed into the full excitonic manifold. Passive-state permutations are quantified through ergotropy and energetic capacitance. We find that thermodynamic smoothness in ergotropy and energetic capacitance can conceal microscopic state rearrangements. Cotunneling redistributes the extractable nonequilibrium energy among the quinone acceptor, the donor-pheophytin radical pair, and excitonic states with strong contributions from pigments on the inactive branch. At high Coulomb interaction, cotunneling produces no passive-state reordering. Piecewise-linear relations among ergotropy, energetic capacitance, and mean energy further reveal the population reorderings generated by cotunneling.
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All authors Manjet Rajak, Trishna Kalita, Debasish Sharma, Himangshu Prabal Goswami

Tuesday 6 October

Biological Physics · Cross-listed from cond-mat.stat-mech

Phase diagram of a semiflexible magnetic polymer in three dimensions

Linda Delimi, Arpan Dey and Jean-Charles Walter

We study a semiflexible magnetic polymer in which each monomer carries a two-state spin, giving rise to coupled conformational, stiffness, and magnetic degrees of freedom. The model provides a minimal representation of nucleoprotein complexes in vivo, in which DNA bending rigidity and bridging proteins must be simultaneously accommodated. We employ Monte Carlo simulations on a face-centered-cubic (FCC) lattice, complemented by a variational free-energy upper-bound approach, to characterize the zero-field phase diagram as a function of bending stiffness and temperature. At low stiffness, we identify a first-order transition combining coil-to-globule collapse with a ferromagnetic-to-paramagnetic transition. At lower temperatures, an additional first-order transition separates the disordered globule from an ordered crystal characterized by nematic-like orientational order of the bond tangents. At high stiffness, the globule disappears, resulting in a direct first-order coil-to-crystal transition. These results demonstrate how chain stiffness and magnetic degrees of freedom couple to polymer conformation and reorganize the zero-field phase diagram. Our model connects the two limiting cases of flexible magnetic polymers and semiflexible homopolymers, the latter corresponding to the strong-field limit in which all spins are aligned.
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Biological Physics

Inverse-Designed Anisotropic Intrastromal Corneal Exoskeletons for Keratoconus: A Three-Dimensional Computational Feasibility Study

J. Sumaya-Martinez and Alan Altamirano

We propose G-EXO, an inverse-designed intrastromal corneal exoskeleton for keratoconus. Unlike conventional intracorneal ring approaches, G-EXO is conceived as a patient-customizable, sectorial and anisotropic scaffold whose geometry and stiffness are optimized to redistribute corneal deformation and reduce asymmetric optical aberrations. We develop a three-dimensional finite-element screening model with localized ectatic weakening, physiological intraocular pressure, and an intrastromal reinforcement domain. A representative design produced a modest but consistent reduction in pressure-induced coma, with a mesh-refined estimate of about 5%, while displacement and stress metrics were more numerically stable. The study is presented as a computational feasibility framework rather than a clinical validation. Its main contribution is to formulate intrastromal reinforcement as a free-form optical-biomechanical inverse-design problem. Patient-specific tomography, nonlinear anisotropic corneal mechanics, full optical modeling, and ex-vivo validation are identified as the next steps.
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Biological Physics

Spatiotemporal Oscillations Driven by Spatial Heterogeneity and Growth

Lewis S. Mosby, Mohit P. Dalwadi and Zena Hadjivasiliou

Temporal oscillations arise naturally in biological systems with feedback loops or explicit delays. When incorporating spatial effects, the travel times of propagating signals can also generate intrinsic delays, and this behaviour is complicated further in the presence of spatial heterogeneity. In this work, we investigate the mechanistic origin and dynamics of spatiotemporal oscillations driven by intrinsic diffusive delays in a representative activator-inhibitor system with spatial heterogeneity using a combination of mathematical modelling, simulations and numerical methods. In contrast to delay-differential equation models where delays are explicitly prescribed, in the partial differential equation system we consider here delays are an intrinsic consequence of signal travel time through space. We demonstrate that undamped oscillations can arise as a result of spatially heterogeneous feedback when the system size increases through a critical length. At this critical length the time for systems to reach steady-state diverges, and the systems first exhibit an eigenvalue with a positive real part and non-zero imaginary part, corresponding to a Hopf bifurcation. We infer that this critical length could define a theoretical upper limit for the size of systems where oscillations hinder function. The analysis pipeline derived in this work offers a novel route for understanding the onset of spatiotemporal oscillations and Hopf bifurcations in a diverse array of biological systems where interactions are mediated by diffusive species.
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