PRX Life

Foraging is a central decision-making behavior performed by all animals, essential to garnishing enough energy for an organism to survive. Similarly, mating is crucial for evolutionary continuity and offspring production. Mate choice is one of the central tenets of sexual selection, driving major evolutionary processes, and can be regarded as a decision-making process between potential mating partners. Often researchers have used coarse-grained models to describe macroscopic phenomenology pertaining to mate choice without detailed quantitative mechanisms of how animals use individual and environmental signals to guide their mating decisions. In this letter, we show that mate choice can be cast as a foraging problem, and we present an analytically tractable optimal foraging-inspired mechanistic theory of decision-making underlying mate choice. We begin from the premise that deciding upon which partner with which to mate is at its core a stochastic decision-making process. Agents adopt a variety of decision strategies, tuned by decision thresholds for leaving or committing to a mate. We find that sensitive leaving thresholds are favored independently of signal availability in the population. By contrast, optimal thresholds for committing to a mate depend upon signal availability in the population, with signal-rich populations generally favoring less eager strategies compared to signal-poor populations.

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.

While the regulation of bacterial cell size is widely studied across generations, the stochastic nature of cell volume growth remains elusive within a cell cycle. Here, we investigate the fluctuations of cell volume growth and report a deviation from standard white-noise models: the random growth rate exhibits subdiffusive dynamics. Specifically, the mean square displacement of the growth-rate noise scales as {Delta}t with an anomalous exponent {approx} 0.27. This low exponent implies strong negative temporal correlations in growth rate noise on timescales of minutes, which are significantly faster than those of gene expression dynamics. We attribute this phenomenon to the physical mechanics of the cell wall. By modeling the peptidoglycan network as a complex viscoelastic material with power-law-distributed relaxation times, we successfully recapitulate the observed subdiffusive behavior. Our results suggest that the heterogeneous mechanical constraints of the peptidoglycan network, rather than biological regulatory programs,govern the short-timescale fluctuations of bacterial growth.

The dynamic assembly of actin filaments underlies diverse cellular morphologies such as lamellipodia, filopodia, and reticulated networks. However, how filament-scale interactions among actin-binding proteins produce distinct actin architectures remains unclear. We developed a filament-resolved computational model of actin self-organization regulated by the Arp2/3 complex and fascin. Individual F-actin filaments are represented as elastic chains, and their stochastic polymerization, Arp2/3-mediated branching, and fascin-mediated crosslinking and bundling are explicitly modeled. The simulations reproduce three actin architectures observed in minimal reconstitution experiments, including lamellipodia-like branched networks, filopodia-like bundled protrusions, and reticulated meshworks, as a function of Arp2/3 and fascin concentrations. We quantify these regimes using actin density, orientational order, and spikiness, which robustly separate the three morphologies across conditions. To connect filament organization to shape change, we further couple the actin network to membrane deformation using a phase-field formulation. This coupling shows how localized remodeling concentrates load to drive pseudopodial protrusions, whereas highly branched networks distribute stresses and stabilize rounded shapes. The model links molecular interactions to emergent architecture and cell-scale morphodynamics.

Cells invariably encounter unpredictable changes in their microenvironment and adapt by orchestrating substantial alterations in their molecular states, often resulting in appreciable phenotypic changes. The timescale of molecular adaptation depends on how quickly a cell loses its molecular memory of past environmental encounters through the degradation rate of proteins unfavorable to the current environment. Concurrently, de novo synthesis of favorable biomolecules during adaptation imposes an energetic cost that impacts cellular fitness. Here, by developing a phenomenological model of intracellular processing of environmental signals and associated cell-state switching, we study the dynamical implications of cellular memory and adaptation cost on cellular responses to a changing environment. We find that while increasing cellular memory reduces cell fitness in periodic environments, counterintuitively, increasing adaptation cost can improve growth by minimizing mismatches between the environment and the cell state. Similarly, we observed a variable role of memory capacity and adaptation cost for stochastic correlated environments, with increasing memory and cost improving cellular fitness in negatively but not positively correlated environments. Lastly, we show that cellular memory and population heterogeneity in adaptation cost explained reported experimental observations in melanoma: increased population survival during drug treatment when the population was either enriched for rare cells expressing resistance marker genes or primed with low-dose drug treatment before exposure to high-dose treatment. Overall, this work establishes a foundational model for studying how cellular memory dynamics and adaptation cost drive cellular adaptation under different environmental conditions and explain complex cellular behaviors.

We investigate the target search process by proteins locating specific target sites along DNA - a phenomenon fundamental to biological functions such as gene regulation, transcription, replication, recombination, and gene-editing technologies. This process proceeds through a repetitive sequence of stochastic motions: consisting of one-dimensional (1D) sliding along the DNA contour interspersed with detachment and three-dimensional (3D) excursions in the bulk, and then reattachment to a random location on DNA. Recognizing this sequence of random events as analogous to the resetting processes widely studied in statistical physics, we employ a first-passage-renewal framework and derive general expressions for both the mean and fluctuations of the total search time. Our results are completely generic and do not depend on the detailed microscopic dynamics of either the 1D or 3D phases. Quite interestingly, we find that intermittent detachment can not only accelerate the mean search but can also regulate fluctuations around it. Our analysis reveals a universal fluctuation inequality that links the variability and mean of the sliding time to the mean excursion time, thereby identifying the fundamental conditions under which target search process becomes efficient. Notably, we find that broad distributions of sliding times emerge as a universal characteristic for optimal search efficiency--a feature emanating from the slow dynamics along the DNA. Using the facilitated diffusion mechanism as a representative example, we validate the generality of our results. These findings provide a unified theoretical framework connecting stochastic search, resetting dynamics, and biological efficiency, while also highlighting the crucial role of DNA structure such as its contour length in modulating search performance.

The primary morphological transition in Hydra regeneration, from an initially quasi-spherical tissue fragment to an elongated body, the hallmark of a mature Hydra, is preceded by a prolonged period of modest shape changes. Here, we ask whether this early stage already contains signatures of morphogenetic organization consistent with canalization toward the main morphological transition. We analyzed shape fluctuations during this period in tissue fragments with different initial and physiological conditions. Using principal component analysis, we quantified the effective dimension of the dynamical morphological fluctuation modes. We find that this effective dimension decreases progressively during the preparatory stage, well before the onset of significant elongation, indicating a progressive restriction of the accessible fluctuation manifold. This decrease is not explained by a single global measure of shape and persists when early and late states are compared at approximately matched shapes. We further show that calcium activity is associated with both the visible morphological changes and this hidden dynamical state. Tissues retaining positional cues from the parent Hydra exhibit lower effective dimensions, whereas tissues lacking such cues or subjected to mechanochemical perturbation maintain higher effective dimensions. These results identify an early, hidden dynamical phase of canalization in Hydra regeneration.

Cellular monolayers often exhibit orientational order, with nematic alignment of cell shape and cytoskeletal structures governing tissue-scale collective dynamics. Despite extensive studies, a unified analysis framework for characterizing active nematics in living systems remains partial, and key discrepancies with theory persist. Here, we present a systematic and comparative analysis of nematic order and tissue flow dynamics across twelve distinct cell types. We quantify the impact of analysis parameters and provide data-driven guidelines to improve reproducibility and cross-study comparability. Across all nematic systems, we uncover remarkably consistent static properties, supporting the universality of nematic behavior in living tissues. By combining orientation-field analysis with velocity-field measurements and numerical simulations, we show that all examined systems display contractile active nematic signatures, with characteristic flow structures around topological defects. However, direct tracking of individual defects reveals subdiffusive dynamics, in stark contrast with the superdiffusive, self-propelled motion predicted by the hydrodynamic theory of active nematics. Our results establish a standardized framework for nematic analysis in biological systems and highlight fundamental limitations of current active nematic models in describing defect dynamics in living tissues.

The interactions of droplets and filaments can lead to mutual deformations and complex combined behavior. Such interactions also occur within the cell, where biomolecular condensates, distinct liquid phases often composed of proteins, have been observed to structure and affect the organization of the cytoskeleton. In particular, biomolecular condensates have been shown to undergo characteristic deformations when cytoskeletal filaments are fully embedded within them. However, a full understanding of the underlying physical mechanisms is still missing. Here, we combine experiments with coarse-grained molecular dynamics simulations and analytical models to uncover the physical mechanisms that define emerging shapes of droplets containing filaments. We find that the surface tension of the liquid phase and the bending energy of the filament(s) suffice to accurately capture emerging shapes if the length of the filament is small compared to the liquid volume. As the volume fraction of filament(s) increases, wetting effects become increasingly important, setting physical constraints within which surface and bending energies compete to define the droplet shapes. We find that mutual deformations of condensate and filament extend accessible shapes beyond classical stability considerations, leading to structuring and entrapment of contained filaments. Shape deformations may further affect ripening dynamics that favor certain geometries. Our findings provide a physical framework for a better understanding of the possible roles of biomolecular condensates in cytoskeletal organization.

AO_SCPLOWBSTRACTC_SCPLOWTransitions between stable states are a defining feature of nonlinear dynamical systems, yet the factors controlling their probabilities and timescales remain poorly understood in non-conservative settings. In many theoretical frameworks, such control is commonly interpreted in terms of potential depth, a concept that becomes ambiguous outside equilibrium. By analyzing a gene regulatory network associated with breast cancer subtypes, we uncover a geometric framework in which the phase-space distance between stationary states, together with the bifurcation structure organizing multistability, provides a robust and well-defined determinant of transition probabilities, times, and variability. Our results show that the coexistence of stable states is only a necessary condition for transitions, while their accessibility is constrained by geometric features of the underlying state space. Within this framework, we find that the HER2+ regime exhibits dynamical robustness to intrinsic parameter variations, whereas the TNBC regime displays strong sensitivity and amplified variability. These differences emerge naturally from the geometric organization of the bistable region and offer a dynamical explanation for the pronounced heterogeneity observed in TNBC. Together, our results establish a general geometric perspective on transitions in non-conservative regulatory networks, with implications for understanding phenotypic plasticity in complex biological systems.

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.

How genetically identical cells spontaneously break symmetry to assume divergent fates is a fundamental problem in developmental biology. While modern genomics has mapped the vast molecular repertoire involved in gene regulation, understanding the mechanism of cell state transitions that drive differentiation remains a formidable challenge. To address this, we use a reaction-kinetic framework to analyze recurring motifs of two and three competing master regulators. While typically such circuits are studied numerically, we show that assuming symmetry in nodes and interactions provides exact analytical description of the bifurcations governing cell fate transitions. We find that the possible cell fates across all considered topologies are dictated by a single dimensionless quantity, {beta}--the ratio of protein degradation to production rates. In the binary Toggle Switch (TS), decreasing {beta} destabilizes the symmetric (stem cell) state, giving rise to two asymmetric (differentiated) fates via a supercritical pitchfork bifurcation. In the three-component Toggle Triad (TT), low values of {beta} yield three asymmetric fates through subcritical pitchfork bifurcation, creating an intermediate range of {beta} where both symmetric and asymmetric fates are simultaneously stable. For the Self-Activating Toggle Switch (SATS), we identify a new parameter for the self-activation threshold ({theta}) and show that decreasing{theta} progressively stabilizes the uncommitted state, leading to a regime of tristability. Building on these temporal bifurcations, we next address the feasibility of spatial structure formation: can these multistable fates stably coexist within a spatial domain? Through a minimal model of cell-cell communication via free diffusion, we extend these motifs into reaction-diffusion systems, which reveals a direct role of network topology on spatial organization. We prove that any heterogeneous pattern in two-node circuits is inherently transient and unstable. In contrast, the three-node repressive network supports the stable spatial coexistence of differentiated phenotypes through pure diffusion, a phenomenon we analyze by studying heteroclinic interface solutions as building blocks. By reducing complex regulatory dynamics to tractable models with physically meaningful parameters, we establish a minimal framework which relates topology to cell fate. Finally, the effects of temporal multistability on pattern formation provide an excellent studying ground for morphogenesis, synthetic biology, and the overarching problem of spatiotemporal self-organization.

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.

The epithelial-mesenchymal transition (EMT) alters cell-cell interactions to facilitate collective or individual migration during embryonic development, wound repair, or tumor invasion. Epithelial cells are typically cohesive and stationary while mesenchymal cells are individually dispersed and motile. Additional "partial" EMT states are thought to occur with distinct adhesive and migratory behaviors, but these functional phenotypes are poorly understood. Here, we show that cells treated with moderate TGF-{beta} concentration exhibit collective migration that is fast and directionally persistent despite heterogeneity in epithelial, partial, and mesenchymal states. We find cells coordinate their motility by reorienting in similar directions after transient contacts, a distinct "collision guidance" mechanism that differs from epithelial arrest or mesenchymal repulsion. Moreover, partial EMT cells sustain collision guidance when interacting with epithelial or mesenchymal cells, which otherwise have increased tendency to repel. We corroborate these experimental observations with a computational model using self-propelled interacting particles that align their motion or repel upon contact. Finally, we show that partial EMT enables tissue monolayer fronts to overwhelm and displace monolayers of other cell types after collision. Overall, these results reveal that partial EMT promotes coherent and emergent behaviors that bridge from cell to tissue length scales, with potential implications for shaping epithelial tissue formation, regeneration, or disorganization.

The nuclear lamina, composed of supramolecular structures of lamin proteins, is a two-dimensional protein meshwork that preserves the structural integrity, elasticity, and morphology of the nucleus. Lamins--A/C-type and B-type--assemble into dynamic, individual but interacting networks with distinct structural properties. Lamina meshwork assembly can be disrupted by lamin mutations in diseases known as laminopathies. Despite extensive experimental insights, the biophysical mechanisms that alter the lamina meshwork topology in health and disease remain relatively poorly understood. In this study, we develop a coarse-grained molecular dynamics (MD) model of lamina self-assembly, where lamin dimers are modeled as semiflexible polymers confined within an elastic nuclear shell. By systematically interrogating inter-lamin and lamin-shell association affinities, our simulations reproduce a plethora of experimentally observed lamina architectures, from lattice-like to fibrous meshwork topologies. This elucidates how the interplay between inter-lamin and lamin-nuclear envelope interactions can shape the nuclear lamina. Importantly, inter-lamin interactions can cause a heterogeneous distribution of lamins on the surface and result in large, lamin-free surface domains at sufficiently low lamin-shell affinities. Furthermore, paracrystalline lamin sheets form with increasing propensity for parallel lamin alignment, in addition to the canonical, sticky terminal groups. Overall, our integrative MD and network analysis provide the first explicit polymer physics model of the lamina and demonstrate how lamin interactions may affect the mesoscale architecture of the lamina in disease.

Quantifying task difficulty remains an open theoretical problem in neuroscience and artificial intelligence. While difficulty is often treated as a scalar property of stimuli or optimization landscapes, neural computation unfolds as a transient reconfiguration of high-dimensional dynamical systems. Here we propose a dynamical manifold theory of difficulty based on heterogeneous, modular FitzHugh-Nagumo networks subjected to structured task demand. Task difficulty is modeled as a conflict-driven control parameter that perturbs competing neural submodules. We define four dynamical metrics: (i) transition action (energetic cost), (ii) peak dispersion entropy, (iii) coherence recovery deficit, and (iv) mean-field trajectory curvature. Across systematic sweeps of task demand, we demonstrate that difficulty does not collapse to a single axis but instead emerges as a multidimensional manifold. Energetic cost and dispersion entropy form a dominant axis, while geometric curvature and integration recovery exhibit partial independence and nontrivial correlations. These results suggest that cognitive difficulty corresponds to structured reorganization in neural state space rather than mere increases in activation amplitude. The proposed framework provides a biophysically interpretable foundation for linking neural dynamics, cognitive effort, and difficulty estimation in artificial systems.

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.

Chromosome segregation is a tightly-regulated process that normally occurs with high fidelity. Errors in chromosome segregation are associated with aging, cancer, and infertility. Initially erroneously attached chromosomes are corrected over the course of mitosis, with the spindle assembly checkpoint preventing entry into anaphase until this error correction is complete. Despite extensive work on the molecular basis of error correction and the spindle assembly checkpoint, it is still unclear how disruption of these processes contribute to chromosome segregation errors. Here, we develop and experimentally test a coarse-grained model of error correction in the presence of a faulty spindle assembly checkpoint. We use the resulting model to disentangle the impact of various small molecule and genetic perturbations on both error correction and the spindle assembly checkpoint, and to compare chromosomally stable hTERT-RPE-1 cells and chromosomally unstable U2-OS cells. We find that the probability of error-free chromosome segregation is determined by the ratio of the checkpoint failure rate to the error correction rate, and validate a simple heuristic for understanding the source of chromosome segregation errors: perturbations which cause errors by disrupting the spindle assembly checkpoint decrease anaphase times, while those that disrupt error correction increase anaphase times. Taken together, this work provides a quantitative framework for understanding how error correction and the spindle assembly checkpoint contribute to mitotic timing and fidelity.