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.

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.

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 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.

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.

Evolutionary therapies regulate heterogeneous populations by altering selective pressures through treatment sequences in cancer and infections. This letter develops an invariant-set framework for treatment-induced containment based on positive triangular invariant sets. For periodically switched systems, sufficient conditions are derived for the existence of such invariant regions. Robustness with respect to mutation is established by showing that the invariant simplex persists under small perturbations of the subsystem matrices. In the two-phenotype case, the analysis yields an explicit mutation threshold that separates regimes in which therapy cycling maintains containment from regimes in which mutation can enable evolutionary escape. Simulations illustrate the geometry of the invariant sets and the role of mutation and dwell time in containment robustness.

Planarian fragments can regenerate with normal gross anatomy after a transient bioelectric perturbation yet display altered outcomes upon re-cutting, implying that regeneration can store a persistent hidden state. Here we formulate an open-path version of Tangential Action Spaces (TAS) for this setting. Regeneration after a given cut is represented as a prescribed coarse anatomical trajectory together with multiple physiological lifts in a higher-dimensional state space. A metric on physiological state space defines a baseline lift, an effective excess-cost functional, and a baseline-relative endpoint displacement that serves as written hidden regenerative state. Re-cutting converts this open-path construction into a challenge readout. Locally, the theory yields a cut-dependent memory co-metric that identifies latent directions that are easy, difficult, or inaccessible to rewrite. We show that this geometry is consistent with published observations of cryptic phenotypes, stable re-challenge ratios, and near-absorbing double-headed outcomes. A reduced rank-one latent-threshold realization fitted to published 8-OH immediate and re-challenge counts identifies a challenge-sensitive cryptic interval below the immediate double-headed threshold and predicts out-of-sample re-challenge penetrances near 15% for nigericin- and monensin-treated immediate single-headed survivors using only their immediate phenotype penetrances. As a mechanistic bridge, a local electrodiffusive in-silico example instantiates a local version of the physiological-state effort metric G. This metric defines the baseline lift and excess rewriting cost, in relative biophysical units, and yields explicit example local write geometry. An illustrative semimechanistic readout based on integrated wound-edge gap-junction contrast and Na/K-ATPase load reproduces the treated-family ordering and similar transfer predictions when the untreated baseline is softly anchored near zero. These quantitative layers are intended as proof-of-concept calibratability and mechanistic-grounding checks rather than full validation of the complete open-path model. The framework therefore turns cryptic regenerative memory into a geometric, costed, and experimentally testable object, yielding predictions about temporal-profile dependence, compensatory cancellation, sign-reversing controls, cut dependence, anisotropic rewriting, and multi-round accumulation of hidden regenerative state.

Glassy dynamics in active biological cells remain a subject of debate, as cellular activity rarely slows enough for true glassy features to emerge. In this study, we address this paradox of glassy dynamics in epithelial cells by integrating experimental observations with an active vertex model. We demonstrate that while crowding is essential, it is not sufficient for glassy dynamics to emerge. A mechanochemical feedback loop (MCFL), mediated by cell shape changes through the contractile actomyosin network, is required to drive glass transition in dense epithelial tissues, as revealed via a crosstalk between actin-based cell clustering and dynamic heterogeneity in experiments. Incorporating MCFL into the vertex model reveals contrasting results from those previously predicted by theories- we show that the MCFL can counteract cell division-induced fluidisation and enable glassy dynamics to emerge through active cell-to-cell communication. Furthermore, our analysis reveals, for the first time, the existence of novel collective mechanochemical oscillations that arise from the crosstalk of two MCFLs. Together, we demonstrate that an interplay between crowding and active mechanochemical feedback enables the emergence of glass-like traits and collective biochemical oscillations in epithelial tissues with active cell-cell contacts.

Robust development depends on maintaining correct proportions as overall size varies. What controls and limits this ability to scale remains poorly understood in part due to the complex interplay between mechanical and biochemical factors within developing embryos. Using confinement of dissociated embryonic presomitic mesoderm cells, combined with imaging and chemical perturbations, we identified aggregation as the initial event in de novo anterior-posterior axis patterning. Using a continuum model solely based on cell-cell attraction, we quantitatively map out how the time available for aggregation-driven patterning limits the system size over which scaling can be maintained: Small systems allow for rapid and robust pattern scaling whereas coarsening dynamics substantially de-lay the appearance of a scaled pattern in large systems. Our experiments quantitatively confirm these predicted scaling regimes. Together, our results suggest a developmental time-size tradeoff on the scaling of aggregation-driven patterns.

Receptor-mediated ligand endocytosis is traditionally viewed as a negative feedback mechanism for signal attenuation. Here we show that ligand removal can paradoxically enhance directional information in autonomous cell-cell attraction. Many cell systems migrate toward one another in the absence of externally imposed gradients, implying that secretion, diffusion, and uptake must themselves generate usable directional cues. We develop a surface-resolved theory of a finite-sized detector exposed to a nearby source and derive analytical expressions for the steady-state ligand field. The resulting concentration profiles are governed by a single dimensionless Damkohler number that compares receptor-mediated endocytosis to diffusive ligand transport. Increasing ligand removal lowers extracellular ligand concentrations and reduces absolute concentration differences across the detector surface, but preferentially enhances relative surface anisotropy. Thus, destroying the signal can increase the usable information encoded in relative gradients. Incorporating nonlinear downstream processing reveals a tradeoff between contrast enhancement and signal depletion that yields a well-defined optimal endocytosis rate, in a regime consistent with experimentally measured receptor internalization kinetics. These results recast receptor-mediated endocytosis as an extracellular information-processing mechanism that reshapes self-generated gradients to enhance directional information.

In living systems across developmental and cancer biology, populations of cells on surfaces organize themselves into aggregates that mediate function and disease. Recent experimental studies have identified that such aggregates can have emergent fluid-like properties such as surface tension, yet the physical origin of these properties is not clear. Here, we develop a minimal cell-based model in which cell-cell and cell-substrate interactions are governed by active intermittent attachments. We explain the transition of cells from a dilute population to a dense aggregate, and quantify the emergent material properties underpinning this transition. We use our model to interpret experiments on dewetting of aggregates of MDA-MDB-231 cancer cells and shape fluctuations of surface-associated OVCAR3 cell aggregates. Finally, we show how spatial heterogeneity in attachments governs collective chemotaxis of cell aggregates. Together, these results reveal how active intermittent attachments generate cell aggregates with emergent material properties, with broad implications for development and cancer.

The C. elegans zygote is a powerful model for asymmetric cell division. Its strikingly patterned cortex features thick F-actin bundles and myosin foci, contractile nematic structures that drive characteristic surface ruffles. Early in the cell cycle, symmetry breaks near the sperm-contributed centrosomes, typically at the presumptive posterior pole, and is marked by local downregulation of contractility. This initiates a cortical flow that polarizes the cell and enables PAR proteins to establish anterior and posterior domains. While biochemical mechanisms maintaining these domains are well understood, the mechanical role of cortical architecture in polarization remains unclear. We developed a three-dimensional (3D) mechanical model of C. elegans zygote polarization that represents the actin bundles and myosin foci of the cortex as a network of stiff contractile filaments. We measured cortical flow with high spatiotemporal resolution by tracking myosin foci, and used these data alongside mechanical properties from the literature to parametrize the model. The model simulates the complete polarization process in 3D, from symmetry breaking through domain stabilization, and reproduces key cortical dynamics including flow profiles, surface ruffles, and tension anisotropy. Domain arrest near the embryo midpoint emerges from density-dependent contractility regulation, in which cortical material redistribution during flow creates a mechanical negative feedback that balances anterior and posterior tension.We find that compressive flow aligns actin bundles in the anterior domain and generates anisotropic tension perpendicular to the flow direction. Although this alignment is not essential for polarization when symmetry breaking occurs at the pole, it contributes to this process when symmetry breaking occurs laterally. In such cases, anisotropic tension from aligned bundles drives axis convergence by rotating the posterior domain towards the nearest pole. Nematic cortical structures therefore ensure robust alignment of the polarization axis. AvailabilityAll data and code required to reproduce the results are freely available at https://doi.org/10.5281/zenodo.18135771. The latest version of the software is maintained at https://bitbucket.org/pgmsembryogenesis/polarization.

Mitochondria often form branching membrane networks distributed throughout the cell interior. In many, though not all, cell types, these networks are observed to consist of one large connected component together with many smaller fragments. Why does this pattern arise? Does it reflect a specific biological function, an external biophysical constraint, or something simpler? Using results from extremal graph theory, we prove a new theorem which suggests that, under a sufficiently broad sampling of the space of mitochondria-like graphs, the predominance of three-way junctions makes the appearance of a large component likely. This suggests that, in some settings, a large component may serve as a useful null model for mitochondrial network structure rather than requiring a dedicated explanation. More broadly, our result points towards testable predictions, since systematic deviations from this baseline may help reveal additional constraints or mechanisms shaping mitochondrial morphology.

Understanding why specific metabolic states become stable in cancer has remained a fundamental challenge, as current pathway-centric frameworks lack a unifying physical principle governing global metabolic organization. We introduce the Metabolic Spin-Glass (MSG) model, which recasts cellular metabolism as a frustrated many-body system governed by a Hamiltonian that integrates reaction free energies, cofactor-mediated thermodynamic couplings, and patient-specific transcriptomic fields. The Hamiltonian is formulated as a binary optimization problem and solved using hybrid quantum annealing. Embedding gastric cancer transcriptomes (n=497) reveals that malignant phenotypes correspond to thermodynamically distinct ground states rather than isolated pathway perturbations. The Warburg effect emerges intrinsically as a thermodynamic phase transition, and stem-like tumors occupy the deepest attractor basin reflecting high energetic stability. A thermodynamic order parameter stratifies patients into prognostically distinct subtypes independently of transcriptomic classification, suggesting clinically applicable non-redundant biomarkers. This work establishes a spin-glass energy landscape framework for physically principled, patient-specific cancer metabolic stratification.

The architecture of the genotype-phenotype-fitness map (GPFM) is a key determinant of evolutionary dynamics. One salient feature of biological GPFMs is variational modularity, where each mutation affects only a small subset of functional traits. Variational modularity may constrain the dynamics of trait evolution, but these constraints are not well understood. Here, we use several extensions of the Fishers geometric model with two functional traits to investigate these constrains. We find that on GPFMs with universal pleiotropy, populations evolve along the fitness gradient, which implies that the trait under stronger selection is optimized exponentially faster than the trait under weaker selection. In contrast, on modular GPFMs, populations approach a quasi-steady state that we term a "module-selection balance" where both traits improve at the same rate and their ratio remains constant. We demonstrate that the existence of a module-selection balance is robust with respect to the details of evolutionary dynamics and GPFMs themselves, as long as they are variationally modular. Our theory predicts that variationally modular organisms should exhibit stereotypical bi-phasic dynamics of genome evolution, especially in the strong clonal interference regime, and we find support for this prediction in metagenomic data from Lenskis long-term evolution experiment in bacterium Escherichia coli. We propose that module-selection balance is an inherent feature of variationally modular GPFMs, which imposes an important constraint on long-term trait evolution.

Cells sense and respond to the mechanical properties of their environment, yet the minimal physical principles sufficient to reproduce mechanotransduction and durotaxis remain debated. This work introduces FraCeMM, a physics-first mechanochemical simulation framework coupling stochastic ligand-integrin-talin binding to a deformable soft-body cell model on an elastic substrate. Without imposed polarity, directional cues, or migration rules, the model reproduces hallmark mechanobiological behaviors including stiffness-dependent spreading, traction reinforcement, focal adhesion asymmetry, and directed durotaxis. A finite pool of adhesion molecules, mechanically coupled through elastic linkages, drives emergent force asymmetry and polarization via self-consistent feedback between stochastic binding, molecular availability, and substrate stiffness. Despite minimal assumptions and a coarse-grained molecular representation, resulting traction forces, adhesion loads, and migration speeds fall within experimentally reported ranges. These results support the view that local force balance, limited adhesion resources, and mechanically binding are sufficient to generate adaptive mechanosensing and directed migration, establishing a transparent and extensible foundation for computational mechanobiology.

Beta-band oscillations in primate motor cortex propagate as planar traveling waves whose amplitude attenuates with spatial gradients across the cortical sheet just before movement onset. How local excitatory-inhibitory (E-I) interactions and spatial connectivity jointly generate these waves, their attenuation patterns, and their stereotyped rostro-caudal bias remains unclear. Here we address this question by implementing a spatially structured network of leaky integrate-and-fire neurons with distance-dependent connectivity, conduction delays, and realistic synaptic dynamics. Through linear stability analysis and large-scale simulations validated against macaque electrophysiology, we show that planar beta waves emerge as Turing-Hopf spatiotemporal instabilities, where global beta oscillations coexist with irregular single-neuron firing. When the network operates near the boundary between oscillatory and asynchronous regimes, internally generated fluctuations produce the irregular, transient beta bursts characteristic of single-trial local field potentials. A rapid, spatially homogeneous increase in external drive pushes the circuit into an asynchronous state, reproducing the beta power reduction and spatial attenuation gradients seen at movement onset, alongside the irregular spatiotemporal dynamics of movement execution. By introducing anisotropic excitatory-to-excitatory connectivity, we recover the observed rostro-caudal propagation bias. Our results suggest that motor cortical traveling waves are intrinsic dynamical modes of local E-I circuits, recruited and modulated by behaviorally relevant inputs to organize movement initiation.

The environments inhabited by flying insects demand a balance between flight efficiency and flight manoeuvrability. In structural oscillators such as the insect indirect flight motor, efficiency (arising from resonance) and manoeuvrability (arising from kinematic modulation) are typically quid pro quo, with modulation incurring penalties to efficiency. Band-type resonance is a phenomenon that offers, in theory, a strategy to lessen these penalties via careful navigation through a band of efficient kinematic states. However, identifying this band is challenging: no methods exist to identify the complete band in realistic motor models, involving elasticity distributed across thorax and wing. Nor are the effects of elasticity distribution on the band known. In this work, we address both open topics. We present a suite of numerical methods for identifying the complete resonance band in general systems. Applying them to models of the insect flight motor with distributed elasticity--thoracic and wing flexion--reveals that distributed elasticity is moderate-risk but high-reward morphological feature. Well-tuned distributions expand the resonance band over fourfold whereas poorly-tuned distributions completely extinguish the resonance band. These results indicate that distributing elasticity across the insect flight motor can have adaptive value, and motivate broader work identifying distributions across species.

1The stereotyped internalization of two endodermal precursors during early Caenorhabditis elegans gastrulation enables quantitative dissection of cell ingression mechanics. Experimental work has shown that apical constriction drives Ea and Ep ingression, and several molecular features involved have been identified. Yet, no integrative mechanical analysis has assessed how these elements collectively produce the observed behavior. To address this, we combined biomechanical simulations with a comprehensive dataset of 3D-segmented cell meshes, some with cortical protein distributions, to analyze the mechanics of ingression in its in-vivo context. Our analysis shows the process starts shortly after birth of the ingressing cells. A cortical flow drives the formation of an E-cadherin-rich structure at the apical Ea-Ep interface, which contributes to localizing the buildup of apical tension. Simulations show that medioapical actomyosin contraction can reproduce the observed ingression movements and suggest force transmission to neighboring cells via a friction-based molecular clutch at the apical ring of contact. A series of concurrent cell divisions facilitates ingression, and their stereotyped planar orientation also contributes. Furthermore, we observe an embryo-wide movement of cells during gastrulation. This movement resembles a flow, suggesting that local force generation leads to global rearrangements via internal pressure changes. Finally, at the end of ingression, detailed microscopy shows that neighboring cells actively close the gastrulation cleft by forming a rosette-like configuration and extending actin-rich protrusions. In conclusion, our integrated mechanical description of gastrulation shows that successful ingression is driven by apical constriction and supported by localized friction-based force transmission, coordinated stereotyped cell divisions, and the resulting global tissue flow.

Collective cell migration is a critical process in embryogenesis and cancer invasion. Recent work has shown that uniform tissues can undergo sharp rheological transitions, with collective motion emerging above a critical cell motility. In vivo, however, migration typically involves multiple populations with distinct motile and adhesive properties, and how this heterogeneity shapes collective dynamics remains unclear. Here, using two different vertex model implementations, we show that migration of heterogeneous clusters through tissues is maximized at intermediate adhesion strength: too little and the cluster fragments, too much and cluster cell cohesion suppresses the rearrangements needed for forward motion. We test our model against recent and new data on zebrafish mesendoderm invasion, where graded Nodal signalling regulates both motility and adhesion differences. By mapping measured Nodal levels to mechanical parameters, the model not only reproduces migration outcomes across homogeneous and heterogeneous clusters, but also discriminates between alternative adhesion rules. Strikingly, the inferred parameters place the system near the predicted optimum, where adhesion is strong enough to maintain cohesion yet graded enough to allow selective coupling among heterogeneous neighbors. These results identify an optimal balance between cohesion and interfacial remodeling as a general principle coordinating collective invasion in heterogeneous tissues. Significance statementCells often migrate collectively during embryonic development and cancer invasion, but tissues are rarely uniform and different cells differ both in their adhesion and activity. Using models of tissue mechanics, we show that collective invasion is maximized at an intermediate level of adhesion within the migrating cluster cells: too little and the cluster falls apart, too much and it cannot advance. We test this principle against experiments in zebrafish gastrulation, where a signaling gradient simultaneously controls both cell motility and adhesion. The model reproduces migration outcomes across a range of experiments and identifies the adhesion rule cells use to selectively stick to neighbors. These results reveal a simple mechanical logic for how heterogeneous cell collectives coordinate invasion.