Collective motion in animal groups emerges from local interactions, yet how individuals select and integrate social information remains poorly understood. In schooling fish, inferring which neighbors drive an individual's behavior at any given moment is extremely challenging because the analysis of trajectories alone does not unambiguously reveal the underlying causal interactions that generate the observed motion. Here, we used a closed-loop virtual-reality system in which a single real rummy-nose tetra Hemigrammus rhodostomus interacted in real time with four model-driven virtual conspecifics whose interaction rules could be precisely controlled. By systematically varying the number of influential neighbors of the virtual fish and combining experiments with simulations, we quantified how social information filtering shapes individual and collective dynamics. Collective coordination increased with stronger social coupling, but analyses of kinematics, spatial organization, correlations, and turning dynamics consistently showed that the real fish behaved as if it were responding primarily to the single most influential neighbor, whose identity could change over time. These results demonstrate that selective interaction with the most influential neighbor is sufficient to sustain coordinated group motion and highlight the power of biohybrid closed-loop experiments to reveal causal mechanisms of collective behavior.

The cytoskeleton organizes the cellular interior using cytoskeletal filaments that rely on bundling, usually executed by stable and ordered cross-linking proteins. Bundling often requires protein complexes with at least two defined microtubule binding regions, as present in many molecular motors. Here, we establish a mechanism of microtubule bundling based on capillary forces, analogous to how wet hair sticks together. We show using in vitro experiments and theory that condensates can bundle microtubules through capillary forces, wherein liquidlike capillary bridges form between microtubules and adhere them together through interfacial and wetting forces. We quantify the structure and dynamics of these capillary bundles using total internal reflection fluorescence microscopy, and directly measure the interfacial tensions of condensates on microtubules using atomic force microscopy. Lastly, we show that these capillary bridges provide viscous resistance to motor-driven microtubule sliding that is insensitive to the bulk protein concentration. Taken together, we provide a novel mechanism for how cytoskeletal filaments bundle: through condensate-mediated capillary forces.

Plants employ diverse mating strategies, including self- and cross-fertilization or their combination. Despite the ubiquity of mixed mating—the use of both self- and cross-fertilization within the same population—its evolutionary origins and dynamic stability remain elusive. Here, we study self-incompatibility, in which self-fertilization is disabled by molecular recognition and enabled when mutations disrupt this recognition. Using population-level stochastic simulations, we model the effects of two main parameters on the prevailing mating mode: promiscuity of molecular recognition and inbreeding depression, which penalizes self-compatibility. We reveal a phase diagram with three phases: complete self-incompatibility, complete self-compatibility, and a mixture combining both. The mixed phase exhibits newly described vigorous, nondecaying fluctuations in the proportions of individuals occupying either mating mode. Pollen limitation extends the parameter range over which complete self-incompatibility is favored, contrary to the commonly held view. This study offers new insight into the evolutionary dynamics of plant reproductive systems.

Living cells exhibit a complex organization comprising numerous compartments, among which are RNA- and protein-rich membraneless, liquidlike organelles known as biomolecular condensates. Energy-consuming processes regulate their formation and dissolution, with (de)phosphorylation by specific enzymes being among the most commonly involved reactions. By employing a model system consisting of a phosphorylatable peptide and homopolymeric RNA, and thanks to time-resolved ultra-small-angle x-ray scattering enabling us to probe a wide range of length and time scales, we elucidate how enzymatic activity remodels the growth kinetics and the internal structure of biomolecular condensates. Under a passive condition, we reveal a nucleation-driven coalescence mechanism maintained over four decades in time, similar to the coarsening of simple binary fluid mixtures. Coarse-grained molecular-dynamics simulations show that peptide-decorated RNA chains assembled immediately after mixing constitute the basic subunits. In contrast, actively formed condensates initially display a networklike internal structure, which gradually matures upon dephosphorylation prior to coalescence. Both pathways lead to a steady state, but fluorescence recovery after photobleaching indicates a peptide diffusivity that is twice as high in actively formed condensates consistent with their loosely packed internal structure. This study contributes to linking effectively cellular active processes such as post-translational modifications to the formation and dissolution pathways of membraneless organelles, impacting other fields including synthetic biology.

Chromatin mobility in living interphase nuclei provides a dynamical readout of genome regulation. From time-resolved trajectories of chromatin granules in primary acute lymphoblastic leukemia (ALL) samples (one B-ALL, one T-ALL), leukemic cell lines (U266, Jurkat), and healthy donor T lymphocytes, we resolve rare but reproducible transitions between extreme mobility states. We quantify these events with Brownian-bridge ensembles, i.e., trajectories conditioned on crossings between low- and high-mobility quartiles. Despite their low frequency, crossings reveal strong polarity: activation ($\mathrm{Q1}\to \mathrm{Q4}$) concentrates on a superlinear, ballistic-like ridge, whereas reconfinement ($\mathrm{Q4}\to \mathrm{Q1}$) returns more slowly toward confinement. The scarcity of extreme events is comparable in magnitude to reported bursting fractions, motivating future joint live-imaging tests that directly link mobility bridges to transcriptional state. These asymmetric pathways yield a compact dynamical signature of disrupted confinement and lineage-dependent nuclear deregulation in ALL, and generate testable predictions for cohort-level validation.

Many natural complex systems exhibit synchronization, characterized by temporal coherence among the dynamics of their units. In ant colonies, synchronization manifests through activity bursts: sudden, sharp spikes of motion by a significant portion of workers throughout the nest, followed by durations of quiescence. These activity bursts were discovered three decades ago and studied since then; yet, the mechanisms that lead to their emergence remain elusive. Here, we present a model of ant activity that integrates both the motion of individuals within the nest and the transitions between behavioral states. By balancing microscopic behaviors in the model, we identify a nest-level phase transition from unsynchronized to synchronized activity and draw several connections between model predictions and empirical observations in the literature. Our analysis begets two key insights: (i) activity bursts emerge as a balance between the responsiveness of the colony to the first ant that activates and the ability of the colony to completely deactivate before the onset of the next burst, and (ii) the timescale of the motion of individuals through the nest is faster than that of the burst, suggesting that ants operate in a high-speed interaction regime where new behaviors are near-instantaneously transferred through the nest. We relate these findings to the broader literature on synchronization in complex systems, highlighting the need for periods of quiescence followed by the rapid communication of a new behavior through a population of connected units.

Cilia-driven flows in corals are crucial for exchanging nutrients, removing waste, and maintaining the ambient conditions necessary for corals' survival. While various studies have identified that ciliary activity can influence mass transport, the effect of local ciliary orientation and heterogeneity remains relatively unexplored. In this study, we combine noninvasive experimental measurements of ciliary distribution and alignment, with quantification of flow fields and spatial oxygen distributions among several species of reef-building corals, and develop a mathematical model based on singularity solutions of the Stokes equations to investigate mass transport near the coral's surface. Our findings reveal that counter-rotating vortices robustly emerge in three dimensions as a result of the ciliary arrangement on the coral surface. The rate at which ciliated tissue can exchange nutrients with the surrounding fluid depends strongly on the diffusivity of the dissolved organic matter and is enhanced through the inherent heterogeneity in ciliary orientation—for nutrients with low molecular diffusivity, ciliary heterogeneity increases mass transport by more than 50%. However, the presence of an externally imposed shear flow serves to diminish the overall rate of mass transport across all cases studied. This study highlights the critical role of ciliary orientation in modulating transport processes and exemplifies the role of ciliary transport, particularly in low-flow regimes.

Hair cells actively drive oscillations of their mechanosensitive organelles—the hair bundles that enable hearing and balance sensing in vertebrates. Why and how some hair cells expend energy by sustaining this oscillatory motion in order to fulfill their function as sensors and amplifiers remains unknown. Here, we develop a stochastic thermodynamic theory to describe flows of mechanical energy in a periodically driven hair bundle. Our analysis of thermodynamic fluxes associated with hair-bundle motion and external sinusoidal stimulus reveals that these organelles function as thermodynamic work-to-work machines under different operational modes. One mode allows the cell to harvest energy of the external signal, whereas another channels the power supplied by the cell into the signal. These two regimes might represent thermodynamic signatures of signal sensing and amplification, respectively, which we further connect to the receptor currents through ion channels controlled by the hair bundles. In addition to energy harvesting and work transduction, our model also substantiates the capability of hair cells to operate as heaters and, at the expense of external driving, as active feedback refrigerators. We quantify the performance and robustness of the mechanical work-to-work conversion by hair bundles, whose thermodynamic efficiency in some conditions exceeds 80% of the applied power.

Feedbacks between evolution and ecology are ubiquitous, with ecological interactions determining which mutants are successful, and these mutants in turn modifying community structure. We study the evolutionary dynamics of several ecological models with overlapping niches, including consumer resource and Lotka-Volterra models. Evolution is assumed slow, with ecological dynamics producing a stable community (with some strains going permanently extinct) between successive introductions of an invader or mutant. When new strains are slowly added to the community, either from an external pool or by large-effect mutations of exant strains, an initial evolutionary transient gives way to a diverse eco-evolutionary steady state. In this “Red Queen” phase of continual evolution, the biodiversity continues to turn over without the invasion probability of new variants getting smaller. For resource-mediated interactions, the Red Queen phase obtains for any amount of asymmetry in the interactions between strains, and is robust to “general fitness” differences in the intrinsic growth rates of strains. Via a dynamical mean-field theory framework valid for high-dimensional phenotype space, we analytically characterize the Red Queen eco-evolutionary steady state in a particular limit of model parameters. Scaling arguments enable a more general understanding of the steady state and evolutionary transients toward it. This work therefore establishes simple models of continual evolution in an ecological context without host-pathogen arms races and points to the generality of Red Queen evolution. However, we also find other eco-evolutionary phases in simple models: For generalized Lotka-Volterra models with near-symmetric interactions an “oligarch” phase emerges in which the evolutionary dynamics continually slow down and a substantial fraction of the community's abundance condenses into a handful of slowly turning-over strains.

In bacteria, fluctuations in transcription factor (TF) concentrations and stochastic binding to promoters drive gene expression fluctuations in their target genes. However, how this “noise propagation” from TFs to their targets determines the expression fluctuations in target genes has so far not been quantitatively characterized. Here, we use fluorescence time-lapse microscopy in combination with microfluidics and automated image analysis to quantitatively track the single-cell expression dynamics of target promoters of the SOS response regulator LexA in Escherichia coli under mild DNA damage using the antibiotic Ciprofloxacin (Cipro). We find that all target promoters exhibit short stochastic expression bursts which become more frequent as the Cipro concentration increases, but whose durations are identical for all promoters, with exponentially distributed heights that vary across promoters, but that are independent of Cipro concentration. We show that these observations are inconsistent with popular “equilibrium” models of bacterial gene regulation that assume that binding and unbinding of TFs to promoters is fast relative to TF concentration fluctuations. Instead, all observations are quantitatively fit by a model in which DNA damage events lead to rapid transient dips in LexA concentration, and an expression burst occurs when LexA unbinds from the target promoter during such a short dip. Crucially, because these LexA concentration fluctuations occur on the same timescale as individual TF unbinding events, the responses of different target promoters to individual DNA damage events are complex stochastic and nonequilibrium functions of the strengths of their LexA binding sites and the durations of the events. Our results show that when induction events cause rapid TF concentration changes, i.e., on the timescale of individual TF binding and unbinding events, different target promoters exhibit distinct stochastic responses that read out different kinetics of the induction events. Since many bacterial TFs are activated through phosphorylation or binding to small signaling molecules, such nonequilibrium gene regulation is likely pervasive in bacteria, and may have been exploited by natural selection to encode distinct responses by different targets of the same TF.

When analyzing the individual positional dynamics of an ensemble of moving objects, the extracted parameters that characterize the motion of individual objects, such as the mean-squared instantaneous velocity or the diffusivity, exhibit a spread that is due to the convolution of three different effects: (i) Motion stochasticity, caused by the fluctuating environment and enhanced by limited observation time, (ii) measurement errors that depend on details of the detection technique, and (iii) the intrinsic parameter variance that characterizes actual differences between individual objects, which is the quantity of ultimate interest. We develop the theoretical framework to separate these three effects based on the generalized Langevin equation, which constitutes the most general description of active and passive dynamics, as it derives from the underlying general many-body Hamiltonian for the studied system without approximations. We apply our methodology to determine intrinsic cell-to-cell differences of living and actively moving human breast-cancer cells, algae cells, and, as a benchmark, size differences of passively moving polystyrene beads in water. We find algae and human breast-cancer cells to exhibit significant individual differences, reflected by the spread of the intrinsic mean squared instantaneous velocity over two orders of magnitude, which is remarkable in light of the genetic homogeneity of the investigated breast-cancer cells and highlights their phenotypical diversity. Quantification of the intrinsic variance of single-cell properties is relevant for infection biology, ecology, and medicine, and it opens up new possibilities to estimate population heterogeneity on the single-organism level in a nondestructive manner. Our framework is not limited to motility properties but can be readily applied to other experimental time-series data.

The human large intestine harbors a highly dynamic microbial ecosystem in which growing microbes regularly replenish biomass lost via feces. Elucidating these population dynamics is important as studies increasingly indicate a direct link between microbial density imbalances in the large intestine and host immune responses, colonic cell physiology, as well as disease development. However, given the strong changes in microbial growth, biomass, and intestinal fluid flow throughout the day, gaining a better understanding of these population dynamics remains a major challenge. Leveraging experimental data on fluid turnover, nutrient supply, and microbial growth, we here derive a biophysical model of microbial population dynamics in the proximal large intestine. We show how the digestion of meals in batches triggers strong fluctuations in fluid movement and bacteria growth. Comparing different model scenarios, we further analyze how the expandable nature of the proximal large intestine, the presence of a pouch-like cecum off major flow paths, and the periodic exit of luminal content via “mass movements” are required in combination to maintain the high microbial population observed in the proximal large intestine. As the microbial population undergoes several bottlenecks followed by rapid growth each day, the effective population size in the proximal large intestine is small, $N_e\sim {10}^7-{10}^{11}$, promoting the rapid evolution of microbes. The diurnal fluctuations in flow also hamper the accumulation of slower-growing bacteria and lead to substantial variations in the uptake of fermentation products by the host. Our findings underscore the complex interplay between population dynamics of the gut microbiota, fluid flow, and bacterial growth with important consequences for the microbiome and the host.

A large number of magnetic sensors, like superconducting quantum interference devices, optical pumping, and nitrogen vacancy magnetometers, were shown to satisfy the energy resolution limit. This limit states that the magnetic sensitivity of the sensor, when translated into a product of energy with time, is bounded below by Planck's constant, $\hslash$. This bound implies a fundamental limitation as to what can be achieved in magnetic sensing. Here we explore biological magnetometers, in particular three magnetoreception mechanisms thought to underly animals' geomagnetic field sensing: the radical-pair, the magnetite, and the MagR mechanism. We address the question of how close these mechanisms approach the energy resolution limit. At the quantitative level, the utility of the energy resolution limit is that it informs the workings of magnetic sensing in model-independent ways and thus can provide subtle consistency checks for theoretical models and estimated or measured parameter values, particularly needed in complex biological systems. At the qualitative level, the closer the energy resolution is to $\hslash$, the more “quantum” is the sensor. This offers an alternative route towards understanding the quantum biology of magnetoreception. It also quantifies the room for improvement, illuminating what nature has achieved, and stimulating the engineering of biomimetic sensors exceeding nature's magnetic sensing performance.

The preferred conditions for evolutionary innovation are a fundamental question, but little is known, in part because the question involves rare events. We focus on the potential role of polyploidy in the evolution of novel traits. There are two hypotheses regarding the effects of polyploidy on evolution: Polyploidy reduces the effect of a single mutation and slows evolution. In contrast, the gene redundancy introduced by polyploidy will promote neofunctionalization and accelerate evolution. Does polyploidy speed up or slow down evolution? In this study, we propose a simple model of polyploid cells and show that the evolutionary rate of polyploids is similar to or much slower than that of haploids under neutral selection or during gradual evolution. However, on a fitness landscape where cells should jump over a lethal valley to increase their fitness, the probability of evolution in polyploidy could be drastically increased, and the optimal number of chromosomes is identified. We theoretically discuss the existence of this optimal chromosome number from the large-deviation theory. Furthermore, we propose that the optimization for achieving evolutionary innovation could determine the range of chromosome number in polyploid bacteria.

While two-body fighting behavior occurs throughout the animal kingdom to settle dominance disputes, important questions such as how the dynamics ultimately lead to a winner and loser are unresolved. Here we examine fighting behavior at high resolution in male zebrafish. We combine multiple cameras, a large volume containing a transparent interior cage to avoid reflection artifacts, with computer vision to track multiple body points across multiple organisms while maintaining individual identity in three dimensions. In the body point trajectories we find a spectrum of timescales which we use to build informative joint coordinates consisting of relative orientation and distance. We use the distribution of these coordinates to automatically identify fight epochs, and we demonstrate the postfight emergence of an abrupt asymmetry in relative orientations—a clear and quantitative signal of hierarchy formation. We identify short-time, multi-animal behaviors as clustered transitions between joint configurations, and show that fight epochs are spanned by a subset of these clusters, which we denote as maneuvers. The resulting space of maneuvers is rich but interpretable, including motifs such as “attacks” and “circling.” In the longer-time dynamics of maneuver frequencies we find differential and changing strategies, including that the eventual loser attacks more often towards the end of the contest. Our results suggest a reevaluation of relevant assessment models in zebrafish, while our approach is generally applicable to other animal systems.

Cytoplasmic streaming, the persistent flow of fluid inside a cell, induces intracellular transport, which plays a key role in fundamental biological processes. In meiosis II mouse oocytes (developing egg cells) awaiting fertilization, the spindle, which is the protein structure responsible for dividing genetic material in a cell, must maintain its position near the cell cortex (the thin actin network bound to the cell membrane) for many hours. However, the cytoplasmic streaming that accompanies this stable positioning would intuitively appear to destabilize the spindle position. Here, through a combination of numerical and analytical modeling, we reveal a hydrodynamic mechanism for stable spindle positioning beneath the cortical cap. We show that this stability depends critically on the spindle size and the active driving from the cortex and demonstrate that stable spindle positioning can result purely from a hydrodynamic suction force exerted on the spindle by the cytoplasmic flow. Our findings show that local fluid dynamic forces can be sufficient to stabilize the spindle, explaining robustness against perturbations not only perpendicular but also parallel to the cortex. Our results shed light on the importance of cytoplasmic streaming in mammalian meiosis.

One of the planet's more understudied ecosystems is the deep biosphere, where organisms can experience high hydrostatic pressures (30–110 MPa); yet, by current estimates, these subsurface and deep ocean zones host the majority of the Earth's microbial and animal life. The extent to which terrestrially relevant pressures up to 100 MPa deform most globular proteins—and which kinds—has not been established. Here, we report the invention of an experimental apparatus that enables structural proteomic methods to be carried out at high pressures for the first time. The method, called high-pressure limited proteolysis (Hi-P LiP), involves performing pulse proteolysis on whole cell extracts brought to high pressure. The resulting sites of proteolytic susceptibility induced by pressure are subsequently read out by sequencing the peptide fragments with tandem liquid chromatography–mass spectrometry. The method sensitively detects pressure-induced structural changes with residue resolution and on whole proteomes, providing a deep and broad view of the effect of pressure on protein structure. When applied to a piezosensitive thermophilic bacterium, Thermus thermophilus, we find that approximately 40% of its soluble proteome is structurally perturbed at 100 MPa. Proteins with lower charge density are more resistant to pressure-induced deformation, as expected; however, contrary to expectations, proteins with lower packing density (i.e., more voids) are also more resistant to deformation. Furthermore, high pressure has previously been shown to preferentially alter conformations around active sites. Here, we show this is also observed in Hi-P LiP, suggesting that the method could provide a generic and unbiased modality to detect binding sites on a proteome scale. Hence, data sets of this kind could prove useful for training emerging artificial intelligence models to predict cryptic binding sites with greater accuracy.

Collective cell dynamics play a crucial role in many developmental and physiological contexts. While two-dimensional (2D) cell migration has been widely studied, how three-dimensional (3D) geometry and topology interplay with collective cell behavior to determine dynamics and functions remains an open question. In this work, we elucidate the biophysical mechanism underlying rotation in spherical tissues, a phenomenon widely reported both in vivo and in vitro. Using murine pancreas-derived organoids as a model system, we find that epithelial spheres exhibit persistent rotation, rotational axis drift, and rotation arrest. Using a 3D vertex model, we demonstrate how the combined action of traction force and polarity alignment can account for these distinct rotational dynamics near a solid to flow transition. Furthermore, our analysis shows that the spherical tissue rotates as an active solid occasionally switching to a flowing state and exhibits spontaneous chiral symmetry breaking. Using a continuum model, we demonstrate how the topological defects in the polarity field underlie this symmetry breaking process, which is revealed by asymmetries in the cell elongation pattern. For cell elongation to reveal the chiral asymmetry, shear flow is required in addition to the solid body rotation. Altogether, our work reveals a robust chiral symmetry breaking mechanism with potential implications for left-right symmetry breaking processes in morphogenetic events.

The evolution of resistance remains one of the primary challenges for modern medicine, from infectious diseases to cancers. Many of these resistance-conferring mutations often carry a substantial fitness cost in the absence of treatment. As a result, we would expect these mutants to undergo purifying selection and be rapidly driven to extinction. Nevertheless, preexisting resistance is frequently observed from drug-resistant malaria to targeted cancer therapies in non-small-cell lung cancer (NSCLC) and melanoma. Solutions to this apparent paradox have taken several forms, from spatial rescue to simple mutation supply arguments. Recently, in an evolved resistant NSCLC cell line, we found that frequency-dependent ecological interactions between ancestor and resistant mutant ameliorate the cost of resistance in the absence of treatment. Here, we hypothesize that frequency-dependent ecological interactions in general play a major role in the prevalence of preexisting resistance. We combine numerical simulations with robust analytical approximations to provide a rigorous mathematical framework for studying the effects of frequency-dependent ecological interactions on the evolutionary dynamics of preexisting resistance. First, we find that ecological interactions significantly expand the parameter regime under which we expect to observe preexisting resistance. Next, even when positive ecological interactions between mutants and ancestors are rare, these resistant clones provide the primary mode of evolved resistance because even weak positive interaction leads to significantly longer extinction times. We then find that even in the case where mutation supply alone is sufficient to predict preexisting resistance, frequency-dependent ecological forces still contribute a strong evolutionary pressure that selects for increasingly positive ecological effects (negative frequency-dependent selection). Finally, we genetically engineer several of the most common clinically observed resistance mechanisms to targeted therapies in NSCLC, a treatment notorious for preexisting resistance. We find that each engineered mutant displays a positive ecological interaction with their ancestor. As a whole, these results suggest that frequency-dependent ecological effects can play a crucial role in shaping the evolutionary dynamics of preexisting resistance.

How diversity is maintained in natural ecosystems is a long-standing question in Theoretical Ecology. By studying a system that combines ecological dynamics, heterogeneous interactions, and spatial structure, we uncover a new mechanism for the survival of diversity-rich ecosystems in the presence of demographic fluctuations. For a single species, one finds a continuous phase transition between an extinction and a survival state, that falls into the universality class of Directed Percolation. Here we show that the case of many species with heterogeneous interactions is different and richer. By merging theory and simulations, we demonstrate that with sufficiently strong demographic noise, the system exhibits behavior akin to the single-species case, undergoing a continuous transition. Conversely, at low demographic noise, we observe unique features indicative of the ecosystem's complexity. The combined effects of the heterogeneity in the interaction network and migration enable the community to thrive, even in situations where demographic noise would lead to the extinction of isolated species. The emergence of mutualism induces the development of global bistability, accompanied by sudden tipping points. We present a way to predict the catastrophic shift from high diversity to extinction by probing responses to perturbations as an early warning signal.