PRX Life

During vertebrate development, the flat surface of the gut epithelium undergoes a dramatic transformation into densely packed arrays of finger-like projections called intestinal villi. Recent studies show that the villus formation relies on a tissue dewetting process, in which mesenchymal tissues buckle the overlying epithelial layer into periodic folds. However, the mechanisms driving subsequent elongation of these folds into finger-like villi remain largely unexplored. Here, we propose a simple mechanism for villus elongation that couples tissue dewetting to cell differentiation, which emerged as a repeated outcome of multiple independent simulations of an evolutionary-developmental Cellular Potts Model. In this mechanism, a liquid-like mesenchymal tissue continuously differentiates into a solid-like mesenchymal tissue at the interface between them. This differentiation drives the liquid-like tissue to continuously retract from the solid-like tissue in the opposite direction of the interface through dewetting, ultimately creating a finger-like projection. A merit of our proposed mechanism is that it only requires two tissues with different viscosities, high surface tension, and cell differentiation. We develop a simplified phase-field model to determine exactly how villus morphology depends on these three requirements. Since these requirements are satisfied not only in intestinal villi but also in many other developing tissues, we propose that the same mechanism could also drive the elongation of other tissues.

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.

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.

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.

Cells lying in a curved environment can respond to the surface curvature by reorienting their shape. However, whether cells respond to the mean curvature and/or the Gaussian curvature remains largely unexplored. Here, inspired by experimental observations of how ovarian theca cells (TCs) orient themselves on substrates with different curvatures, we propose a theoretical framework for active nematic layers on curved surfaces. In this model, we assume that the nematic directors of the cells respond to both the mean curvature and the Gaussian curvature of the underlying substrate surface. Our theory predicts specific cell orientation patterns on hemicylindrical, hourglass- and dome-like substrates, consistent with experimental observations. In addition, by incorporating curvature-induced active traction, our model successfully recapitulates the experimental observation of TC accumulation at convex regions of hemicylindrical substrates as well as saddle-shaped regions of more complex geometries. Overall, our work reveals the unexpected role of cell curvature sensing in driving collective migration and pattern formation on various substrate curvature. SIGNIFICANCESubstrate surface curvature is a critical environmental cue that can influence multicellular organization and functions. Yet how cells collectively align and migrate on complex curved surfaces remains unclear. Here, we proposed a hydrodynamic theory of active nematic layers over curved surfaces for contractile theca cells (TCs), where we assume that the nematic directors of cells can respond to both the mean curvature and the Gaussian curvature of the underlying substrates. Our theory predicts distinct cell orientation patterns on hemicylindrical, hourglass- and dome-like substrates, consistent with experimental observations. Furthermore, by introducing curvature-induced active traction, our model recapitulates experimentally observed accumulation of TCs at the convex regions of hemicylindrical substrates as well as saddle-shaped regions of more complex geometries. Together, our study provides a simple theoretical framework to unify our understanding of curvature sensing across complex topology, providing insights into geometric control of tissue pattern formation.

Transcription factors organize into liquid-like condensates to facilitate gene expression, yet the physical mechanisms governing their formation and properties remain poorly understood. We study the size statistics of transcriptional condensates in human HAP1 cells using widefield and super-resolution microscopy tagging the epigenetic reader BRD4. We find that hubs that appear monolithic in widefield resolve into clusters of smaller droplets that resist coarsening. We link this size control to Active Model B+, a non-equilibrium field theory that captures a regime of reverse Ostwald ripening out of thermal equilibrium. In this regime, chemically driven currents cause larger droplets to transfer mass back to smaller ones, stabilizing a state of microphase segregation. The observed exponential size distribution of BRD4 foci quantitatively matches our numerical simulations, suggesting a universal physical picture for the non-equilibrium self-limitation of cellular condensates.

How can simple organisms lacking nervous systems encode and transmit environmental signals to generate complex, adaptive behaviours? Using the unicellular organism Physarum polycephalum as a model, we identify a unifying mechanochemical mechanism that links intracellular calcium oscillations to large-scale behavioural coordination. We first demonstrate experimentally that local perturbation of the actomyosin cortex is sufficient to induce symmetry breaking and directed migration, even in the absence of nutrient cues. Building on evidence linking calcium concentration to actin depolymerization and contractile relaxation, we develop a mechanochemical tubule model in which self-sustained calcium oscillations are coupled to pressure-driven mechanics. We show that environmental cues, encoded through the local modulation of these oscillations, give rise to directed transport and the redistribution of biomass. By extending this framework to a two-dimensional phase-field model, we demonstrate that this mechanism is sufficient to generate a diverse set of slime mould behaviours, including chemotaxis, network formation, and balancing exploration-exploitation trade-offs. In doing so, we provide a single mechanistic framework linking intracellular dynamics to organism-scale behaviour across spatial and temporal scales. Our work shows that these sophisticated behaviours can emerge from the modulation of self-sustained oscillations coupled by diffusion, providing a physically grounded mechanism for information processing in non-neural organisms and offering insight into the evolutionary origins of coordinated behaviour.

Neuronal computation depends on the balance between excitation and inhibition, yet how this balance is implemented across the dendritic tree remains unclear. Classical views predict that inhibition should be most effective near the soma or along the path from excitation to output, but many interneuron subtypes preferentially target remote dendritic compartments. This apparent paradox is sharpened by active dendrites, where local NMDA spikes, calcium plateaus and backpropagating action potentials can make distal branches powerful contributors to somatic firing. Here we develop an analytical framework that extracts general principles of inhibition from biophysically detailed multi-compartment simulations. By reformulating the implicit voltage update of detailed neuron models as a matrix recursion, we derive exact voltage sensitivities to inhibitory synaptic perturbations. This leads to a unified {Phi}-a law: the somatic impact of inhibition factorizes into a global dendritic susceptibility term and a local synaptic perturbation term. Using this law to map inhibitory leverage and identify optimal inhibitory interventions, we show that active dendritic excitation can shift inhibitory hot zones from perisomatic regions toward distal or intermediate compartments. Across neocortical, hippocampal and striatal neuron models, the same response law explains convergent inhibitory strategies despite distinct cellular mechanisms. Our framework turns detailed numerical simulation into analytical theory, providing a general principle for how diverse dendritic inhibition controls active neurons.

Structural Maintenance Complexes (SMC) are energy consuming motors that are important in folding the genome by loop extrusion (LE) in all stages of the cell cycle. Single molecule magnetic tweezer pulling experiments have revealed that condensin, a member of the SMC family involved in mitosis, takes occasional backward steps, thus coughing up the gains in the length of the extruded loop. To reveal the mechanism of the forward and backward steps simultaneously, we developed a theory using the stochastic kinetic model and the scrunching mechanism for LE. The calculations quantitatively account for the measured force-dependent step size and dwell time distributions in both the directions. By postulating the existence of an intermediate state in the ATP-driven cycle that is poised to take a forward or a backward step, we predict that its lifetime increases as the external mechanical force increases till a critical value and subsequently decreases at higher forces. The surprising finding of lifetime increase in an active motor, at sub-piconewton forces, is the characteristic of catch bonds, known in force-induced rupture of several passive protein complexes. The identification of catch bond-like states in condensin not only expands our understanding of LE but also highlights the significance of mechanical forces in regulating genome organization.

Biological tissues require branched cellular architectures to maximize spatial coverage while minimizing redundancy. Yet, how cells decode local spatial information to collectively tile territories without a global blueprint remains a key open question. Here, we develop a biophysical theory of interacting branched cells, and show that coupling their growth to short-range repulsion drives efficient tiling with minimal territorial overlap. Our model predicts that the same local mechanism simultaneously suppresses long-range density fluctuations, driving the cellular collective toward hyperuniformity. We confirm these theoretical predictions with experiments on microglial patterning in the developing retina, and show that perturbations resulting in limited cell growth disrupt both tiling and fluctuation suppression. Our results reveal that two seemingly distinct optimization principles of biological patterning, large-scale regularity and efficient tiling, are intimately linked and can arise from a single growth-repulsion feedback, suggesting a general principle for self-organized tissue coverage.

A key question in cell biology is how cell-scale organization emerges from a given set of molecular players and rules of interaction. Given its multiscale nature, addressing this question requires a combination of experimental perturbation, mathematical modeling, and parameter inference. We leverage recent advances in each of these fields, focusing in particular on neural-network methods for simulation-based inference, to study how cell-scale patterns of Rho GTPase activity are defined by molecular-scale activator-inhibitor interactions with filamentous actin. We show that variations in F-actin assembly dynamics can be inferred directly from experimental data by combining a mathematical model with a neural network trained to associate parameter sets with data. Our neural approach differentiates data sets more precisely than traditional summary statistics, and yields a complete and robust likelihood function for each data set. Utilizing the trained network, we demonstrate how RhoGAP tunes RhoA waves via interaction with F-actin. After showing that the known functions of RhoGAP are insufficient to explain experimentally-observed dynamics, we use neural methods to infer that RhoGAP must, at a minimum, also decrease filament nucleation rates to sustain waves. Our work yields specific, experimentally-testable predictions and illustrates how a combination of traditional forward models and modern inference tools can aid in unraveling mechanisms of self-organization.

Bacterial communities often spend long periods under starvation, where survival depends not only on their intrinsic ability to withstand stress but also on nutrients released by dying neighbors. This creates a distinct form of competition: cells compete for recycled necromass, and the outcome should depend on physiological traits that determine nutrient uptake and maintenance demand. Here, we develop a quantitative framework for this competition using Escherichia coli populations whose starvation physiology is tuned by prior growth history. Fast-grown populations have higher maintenance demands and die slightly faster in monoculture, whereas slow-grown populations are better adapted to starvation. In co-culture, these physiological differences are strongly amplified in a frequency-dependent manner: less-adapted populations die several-fold faster than in monoculture, whereas well-adapted populations can reduce their death rate below that of stationary-phase adapted monocultures. We explain these dynamics with a shared-energy-pool model in which death releases recyclable nutrients, surviving cells consume them for maintenance, and intracellular energy sets death rate. Using independently measured parameters, the model makes parameter-free predictions for competitive survival. The predicted instantaneous death rates collapse onto a universal function of the population ratio over four orders of magnitude. Our results establish necromass recycling as a quantitative basis for bacterial competition during starvation and lay the foundation for modeling communities during starvation.

The hierarchical organization of multiphase biomolecular condensates into core-shell architectures is a fundamental problem in soft matter and biophysics. While classical explanations rely on hierarchies of interfacial tension ({gamma}) between coexisting liquids, the ultralow tensions of condensates (0.1-1 {micro}N/m) render such hierarchies potentially fragile. We introduce a robust assembly principle based on Polymer-Assisted Condensation (PAC), in which a single polymer species dictates the entire structure. The polymer nucleates a dense core by recruiting a condensation-incompetent protein (P1). A second incompetent protein (P2), which is repelled or otherwise thermodynamically disfavored from entering the polymer-rich core, is nonetheless recruited to the interface by weak attraction to P1, forming a stable shell. This effective repulsion-driven layering operates across a wide parameter space without requiring{gamma} asymmetries and yields a robust structure that is impervious to concentration fluctuations and environmental perturbations. Phase-field modeling and molecular simulations establish this mechanism and capture key features of nucleolar organization. Our work reveals a general physical pathway for encoding spatial order in soft, multicomponent fluids.

Neural oscillations are thought to play a central role in encoding and transmitting cognitive information across large-scale brain networks, yet the relative contributions of phase synchrony and amplitude co-modulations to distributed coding remain unclear. A key obstacle is the absence of tools that can simultaneously quantify task-relevant information in the frequency domain and disentangle its phase and amplitude components across pairwise and higher-order interactions. Here, we introduce a spectral partial information decomposition framework (named NeOPID) for quantifying information about cognitive variables in power and phase contributions, and to quantify redundant and synergistic information in brain relations, from pairwise to higher-order interactions. We validated the approach on Kuramoto and Stuart-Landau oscillator networks, including a whole-brain model constrained by macaque anatomical connectivity. NeOPID accurately recovers ground-truth encoding schemes and reveals that phase relations and amplitude co-modulations act as complementary coding channels with both redundant and synergistic components. NeOPID further extends this decomposition to higher-order functional interactions enabling the characterization of how cognitive information is collectively distributed across multiple oscillatory edges via redundant and synergistic encoding. To illustrate biological applicability, we applied NeOPID to local field potentials (LFPs) recorded from the macaque fronto-parietal network during a working memory task. In this dataset, NeOPID identified beta-band amplitude co-modulations as the primary carrier of stimulus information, and revealed that higher-order phase interactions exhibit both redundant and synergistic structure during the memory delay. These results establish NeOPID as a principled tool for dissecting the informational architecture about cognitive processes of oscillatory brain networks.

Hippocampal adult neurogenesis (HANG) is a highly regulated process where neural stem cells progress through distinct stages--from Type 1 radial glia-like cells to mature neurons--via a complex series of proliferative and differentiative divisions. While recent in vivo imaging has provided valuable insights to cellular processes, the exact relationship between individual cell-fate decisions and long-term population stability remains difficult to quantify empirically. In this study, we utilized an agent-based (AB) model to simulate the stochastic dynamics of the hippocampal neurogenic niche. Our results demonstrate that while individual progenitor lineages exhibit high variability and probabilistic division symmetries (proliferative symmetric, asymmetric, and differentiative symmetric), the system achieves deterministic stability as the initial progenitor density increases. We found that the T1 progenitor pool follows a negative exponential decay profile, with its longevity primarily dictated by the differentiation rate (d,0). Critically, the terminal output of immature neurons (CIN,t) was non-linearly coupled to the proliferative capacity of transit-amplifying cells (pp,0); even marginal increases in symmetric proliferative divisions resulted in an exponential expansion of the neuronal pool. These findings suggest that the homeostatic maintenance of the hippocampal niche is governed by a kinetic tuning of division probabilities, providing a theoretical bridge between single-cell stochasticity and robust tissue-level output.

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.

Cooperative interactions often unfold in environments that are shaped by collective behavior, yet how knowledge about such changing environments feeds back into evolutionary dynamics remains poorly understood. While network reciprocity explains how spatial structure enables clusters of cooperators to emerge and grow under certain conditions, it typically ignores how individuals respond to environmental change. Here, we integrate stochastic environmental feedback with network reciprocity to examine how knowledge about environmental state shapes the evolution of cooperation in structured populations. We compare regimes in which individuals either condition their behavior on the current state or remain unaware of it. Under weak selection, we derive a simple condition showing that cooperation is favored when the benefit-to-cost ratio exceeds a modified classic reciprocity threshold accounting for the effect of environmental transitions and state knowledge. Environmental shifts can either promote or hinder cooperation depending on accessibility and fidelity of state knowledge. Counterintuitively, greater knowledge does not universally enhance cooperation: for certain transition rules, state awareness raises the critical threshold for cooperation, a phenomenon we term a "knowledge curse". Our results reveal that, in an ever-changing environment, cooperation in structured populations emerges from a subtle interplay between environmental feedback and information availability.

The acquisition of sensorimotor skills critically depends on basal ganglia (BG)-thalamo-cortical circuits. Prevailing theories propose that the BG optimize motor output through reinforcement learning (RL), using internal performance evaluations to approximate stochastic gradient ascent. However, this framework struggles in non-convex performance landscapes, where local optima hinder efficient learning. Songbirds provide a compelling biological example of robust sensorimotor learning, mastering complex vocalizations through trial-and-error within a specialized BG-thalamo-cortical architecture. Here, we present a computational model constrained by the anatomy, physiology, and developmental trajectory of the zebra finch song system. The model combines a BG-driven RL pathway with a parallel cortical motor pathway that progressively consolidates successful motor patterns via Hebbian plasticity. In addition, we incorporate synaptic volatility within the BG pathway, introducing structured variability across learning. Through simulations of vocal learning using both a biophysical syrinx model and synthetic performance landscapes, we demonstrate that this dual-pathway architecture reliably converges to global optima and outperforms standard and noise-annealed RL approaches. The model reproduces key experimental features of song learning, including non-monotonic learning trajectories, a gradual reduction in motor variability, and the developmental transfer of motor control from subcortical to cortical circuits. Mechanistically, delayed maturation of the cortical pathway provides an implicit regulation of the exploration-exploitation trade-off, while synaptic volatility enables escape from local optima. These results highlight the importance of neural circuit architecture and dynamics in efficient learning, and suggest biologically inspired design principles for improving the robustness and sample efficiency of artificial RL systems in complex sensorimotor domains.

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.

Embryonically, the vertebrate brain begins as an approximately uniform, fluid-filled epithelial tube that undergoes rapid volumetric expansion and regionalization to form the morphologically distinct primary brain vesicles. Hydrostatic pressure from fluid secretion into the inner lumen generates tension in the neural tube that has been implicated as a potential driver of cell proliferation during these early stages of brain development. However, a quantitatively rigorous view of 3D morphology and cellular proliferation has remained elusive. Here, we provide a standardized mapping for the mechanical and biological landscape of the developing neuroepithelium along anatomical axes. Using this 3D morphometric framework in chicken embryos, we show that localized curvature characterizes compartmental boundaries. While rapid inflation would typically be expected to stretch and thin the epithelium, we find the opposite: global expansion is coupled with significant tissue thickening, identifying the early brain as an active shell. Moreover, spatial patterns of thickness remain invariant to local curvature. Our results demonstrate a decoupling of geometry and growth, showing that spatially stable distributions of tissue thickness and mitotic activity are maintained throughout massive volumetric expansion, independent of the dramatic geometric reorganization driven by luminal pressure. We conclude that, while tension in the neuroepithelium may contribute to proliferative growth at some level, biological pre-pattern likely plays a driving role in the regionalized expansion of the early embryonic brain. Why it mattersThe embryonic brain begins as a simple fluid-filled tube that undergoes rapid and heterogeneous expansion to set up the basic organizational plan of the adult brain. Errors in this process are linked to severe neurological and congenital disorders. This work investigates the biophysical basis of expansion and regionalization of the early brain, a complex three-dimensional process driven by inflation from internal fluid pressure together with active cell behaviors that ultimately produce regionally distinct growth and curvature profiles amid a complex mechanical landscape. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/726048v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@17c442forg.highwire.dtl.DTLVardef@1609374org.highwire.dtl.DTLVardef@170c00corg.highwire.dtl.DTLVardef@15080ad_HPS_FORMAT_FIGEXP M_FIG C_FIG