Emergent self-organization is a hallmark of natural bacterial communities, whose spatial structures and dynamic gradients are shaped by the complex interplay among bacterial diversity, oxygen and its consumption, and motility and by hydrodynamic flows. Key aspects of the interplay between oxygen gradients and bacterial community spatial structure remain obscure. Here we aim to elucidate the role that oxygen plays in the self-organization of multispecies bacteria in the water column, focusing on oxytactic bioconvection suspensions of naturally coexisting aerobic bacteria and on self-organization near air-water interfaces. Combining microscopy, mapping of the oxygen field and controlled external oxygen levels, we show that species-specific oxygen affinities and consumption rates induce the formation of distinct bacterial layers near air-water interfaces, resulting in dynamic segregation during multispecies bioconvection, and play a key role in determining the nature of bioconvective patterns in single species suspensions. We further find that oxygen and bacterial fields are tightly coupled and fluctuate with similar spatiotemporal scales, giving rise to oxygen advection and a well-defined oxic-anoxic boundary. Together, our results illuminate the fundamental role that oxygen gradients play in multispecies bacterial active matter and its spatial self-organization, with implications for the formation and stability of ecological niches in aquatic and sedimentary environments.
Condensed biomolecular phases are fundamental compartments in cells and play intricate roles for cellular organization. Although often referred to as “membraneless organelles,” evidence has emerged that condensates associate with membranes through wetting interactions, which are physiologically important but not well understood. Here we report a novel method that provides a detailed description of how condensates interact with surfaces. Combining technically advantageous flat membranes and reconstituted condensates, we developed an accurate method that automatically analyzes the three-dimensional geometry of single, μ m -sized condensates acquired as multicondensate confocal image stacks using 96-well plates. We validated our computational routine by quantifying wetting of and condensates for precisely controlled solution and supported lipid membrane biochemistries. Further, we applied our method to analyze the wetting of condensates formed from the Alzheimer disease-associated protein tau in its phosphorylated and nonphosphorylated forms. Our results demonstrate that both membrane charge and tau posttranslational modifications affect wetting and that our method allows building a systematic, quantitative wetting database, which catalogues conditions controlling wetting in multiple dimensions. We anticipate that upscaled in a high-throughput pipeline, our screening approach will efficiently support efforts to construct a comprehensive wetosome database, which will unravel the mechanisms underlying condensate-membrane interactions and, thus, decipher the molecular grammar governing wetting.
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.
We present a methodology that leverages the zero-crossing time gaps of electric-field molecular fingerprints from liquid biopsies to extract information about their molecular composition. By analyzing zero-crossing timings, we demonstrate that these temporal features encode all medically relevant information contained in the original time traces. A clinical study targeting lung cancer detection using human blood samples reveals that aberrations in zero-crossing time gaps are associated with the presence of the disease and its progression. The proposed approach to discretizing continuous molecular signals through zero-crossing analysis enables compact representation of physiological signatures and can facilitate integration with other omics technologies, opening pathways for comprehensive, multidimensional biological insights.
Quantifying the impact of hereditary transmission within lineage trees remains a fundamental challenge universal to a wide array of biological domains. Here we introduce the new concept of inheritance entropy, a quantity designed to gauge the hereditary structure of inactive cells across a lineage. We measure this entropy in 32 human stem cell clonal colonies, obtained from high-definition single-cell lineage tracing experiments, and show that in the greatest majority of clones the entropy is decisively smaller than that of the corresponding nonhereditary ensemble, hence proving that variations in the proliferative power of stem cell lineages are determined by hereditary epigenetic factors that regulate cell-cycle exit. The method can also be employed to locate the specific node of the tree where a mutation in the probability of inactivity occurs, together with a determination of the lag between the mutation ultimately leading to inactivity and its actual expression. This framework can be used to assess in a robust, simple, and model-agnostic way the hereditary origin of differential growth in any type of lineage trees.
The E. coli chemosensory lattice, consisting of receptors, kinases, and adaptor proteins, is an important test case for biochemical signal processing. Kinase output is characterized by precise adaptation to a wide range of background ligand levels and large gain in response to small relative changes in concentration. Existing models of this lattice achieve their gain through allosteric interactions between either receptors or core units of receptors and kinases. Here we introduce a model which operates through an entirely different mechanism in which receptors gate inherently far from equilibrium enzymatic reactions between neighboring kinases. Our lattice model achieves gain through a mechanism more closely related to zero-order ultrasensitivity than to allostery. Thus, we call it lattice ultrasensitivity (LU). Unlike other lattice models with critical points, the LU model can achieve arbitrarily high gain through timescale separation, rather than through finely tuned allosteric interactions. The model also captures qualitative experimental results which are difficult to reconcile with existing models. We discuss possible implementations in the lattice's baseplate where long flexible linkers could potentially mediate interactions between neighboring core units.
Biomolecular condensates formed by phase separation are key players in cellular organization, yet their interfacial mechanics remain poorly understood. Here we show that both synthetic and endogenous nuclear condensates exhibit critical-like interfacial behaviors near the phase boundary, including enhanced capillary fluctuations and reduced surface tension. By combining optogenetic control with submicron-resolution fluctuation spectroscopy, we quantitatively estimate surface tension, bending rigidity, and effective viscosity. Surface tension diminishes as the system approaches the critical composition, consistent with classical theories of phase separation. Notably, bending elasticity emerges as an unexpected feature of these nuclear liquidlike structures, suggesting the formation of structured interfacial layers that progressively weaken near criticality. Among these condensates, the nucleolus displays exceptionally high viscosity, which may arise in part from viscoelastic coupling to the surrounding perinucleolar heterochromatin, effectively increasing the apparent viscosity in the long-time fluctuation regime. This noninvasive approach enables probing condensate mechanics in living cells and may provide a basis for diagnosing or modulating condensates in biomedical contexts.
Identifying the network of species interactions is a fundamental step toward understanding ecosystem stability and biodiversity. However, the interpretability of empirical interaction measures remains a major challenge. Experimental estimates frequently exhibit puzzling temporal fluctuations, including sign shifts typically interpreted as transitions between competition and facilitation. Here, we analyze the temporal behavior of pairwise interaction measures to demonstrate that these fluctuations—and apparent shifts in ecological roles—can emerge intrinsically from standard population dynamics, without any underlying change in the actual ecological relationships. We show that inferred interactions are heavily distorted by experimental protocol choices, particularly the duration of observation and microbial growth constraints. By systematically evaluating interactions across timescales, we uncover a principled mechanism to mitigate these biases: short-term measurements reliably isolate direct, pairwise species couplings, whereas longer-term observations inevitably absorb indirect community feedbacks and systemic experimental constraints. By disentangling direct couplings from indirect network effects, our framework provides a robust, timescale-aware approach to interpreting empirical interaction matrices, offering critical quantitative guidance for experimental design and predictive ecosystem modeling.
Golgins are coiled-coil proteins that decorate Golgi with highly specific spatial distribution acting as vesicle tethers through their extended, semiflexible architectures. Among them, GM130 is also known to undergo phase separation, yet the molecular basis of this behavior remains poorly understood. Here, we use atomic force microscopy (AFM) to directly visualize individual GM130 molecules adsorbed onto attractive surfaces. We find that GM130 is highly flexible and undergoes conformational transitions from extended chains to collapsed coils—globular structures that serve as building blocks for the submicrometric two-dimensional and dynamic domains when anchored to lipid bilayers. Molecular-dynamics simulations predict a supramolecular scaffold for GM130 domains composed of coiled-coil intermolecular nodes surrounded by a dispersed phase of intrinsically disordered regions with electrostatic interactions acting as chief drivers of phase-separation. These assemblies are experimentally validated under high-force AFM measurements. Our findings establish the structural basis linking GM130 conformational polymorphism to its phase separation capacity.
How biological brains become operational from development and onwards is an unresolved issue. Here we explored the emerging effects in a brain circuit model that received simulated touches through a biological skin model, which featured the potentially critical aspect of sensory dependencies resulting from mechanical couplings across a tissue patch. Our skin model system was connected to a generic, primitive cortical-like subnetwork that was composed of fully connected excitatory and inhibitory neurons, where each individual synapse was subject to continuous, independent, Hebbian-like learning. We used continuous random mechanical activations of the skin model, in this regard mimicking behavioral patterns of early brain development observed in infants, and let the neuron-independent learning define the function that emerged in the network. Remarkably, we found that the network could rapidly learn to separate various naive dynamic skin inputs and solve a kinematics task it had never encountered, even when substantial parts of the sensor population or even network connections were removed post-training. We propose that autonomous learning from a sensor population with intrinsic, time-evolving dependencies could cause the extensively recursive cortical network to gradually adapt its intrinsic dynamics to better mirror the various dynamics of whatever body it is connected to, which results in many biologically useful features for the early acquisition of brain circuitry function.
Living matter consumes energy to build and actuate dynamic protein machines, but the way energy enters these systems is often assumed to be externally imposed and independent of state. This review advances a unifying perspective, that across diverse cytoskeletal active matter, energy injection itself is state dependent, regulated by the mechanical and organizational configuration of the system, rather than acting as a fixed external bath. We synthesize recent experimental evidence, from excitable cortical waves to reconstituted actomyosin networks , showing that feedback between system state and energy input leads to nonmonotonic dissipation, selective energy routing, and relaxation-driven contractility. By organizing these findings within a common framework, we highlight how state-dependent energy injection challenges classical nonequilibrium assumptions and opens new questions for the design and control of active matter.
The day–night cycle drives the largest biomass migration on Earth: the diel vertical migration (DVM) of aquatic organisms. Here, we present a three-dimensional agent-based model that incorporates photokinesis, gyrotaxis, and stochastic reorientation to explore how individual-level swimming behaviors give rise to population-scale DVM patterns. By solving Langevin equations for swarms of swimmers, we identify four distinct regimes—, , , and —governed by two key dimensionless parameters: the Péclet number ( Pe ), representing motility persistence, and the vertical swimming asymmetry ratio ( W = w down / w up ), encoding photokinetic bias. These regimes emerge from nonlinear interactions between light-driven navigation and active noise, diagnosed through topological and statistical features of vertical distributions. A critical feedback is uncovered: upward-biased swimming ( W < 1 ) promotes surface aggregation, while excessive downward bias ( W > 1 ) leads to irreversible sinking. Analytical estimates link regime boundaries to gyrotactic alignment and velocity reversals. Together, our results provide a mechanistic framework to interpret DVM diversity and emphasize the central role of light gradients—beyond absolute intensity—in shaping ecological self-organization.
Evolutionary change is shaped not only by genetic variation and selection across generations but also by phenotypic plasticity, niche construction, and nongenetic inheritance. Here we develop a quantitative, falsifiable framework that models individual organisms as learners that infer hidden environmental states from noisy cues and update phenotype strategies within a lifetime. We introduce Markovian, Bayesian Agents (MBAs), which combine regret-gated stochastic exploration with genetic assimilation, and compare them to nonlearning Blind Agents (BAs). Agent-based simulations reveal that MBAs outperform BAs when cue-stress correlations are high, but lose their advantage above a critical environmental stochasticity threshold ( ɛ ≈ 0.2 ). We then test a central prediction in the unicellular holozoan using a smart-incubator conditioning assay: cultures trained with predictive cue-stress pairing show significantly reduced mortality relative to nonpredictive controls (Mann-Whitney U , p = 1.07 × 10 − 8 ). Together, model and experiment link within-lifetime inference to cross-generational adaptation in a single-celled system.
Many living and artificial systems improve their fitness or performance by adapting to changing environments or diverse training data. However, it remains unclear how environmental variation shapes adaptation, what is learned, and when memory of past conditions is retained. Here we show how cyclic environmental change can produce robust memory. Using a model athermal disordered solid trained by inverse design to attain target elastic properties over a prescribed range, we find that the system evolves toward a marginally absorbing manifold (MAM), meaning that training is reversible within the training range but not beyond it, which encodes a memory of that range. We further propose a general mechanism for MAM formation and memory encoding based on discontinuities in the gradient of the trained quantity. These results provide a simple, broadly applicable physical framework for how adaptive systems learn under changing environments and retain memory of past conditions.
The positioning of nucleosomes, the most abundant nucleoprotein complex in eukaryotes, and their physical properties are strongly influenced by sequence-dependent geometric and elastic properties of the wrapped DNA. At present, the theoretical study of this system is largely limited to numerical computation, and access to entropic contributions has been challenging. In particular, the toolset for the systematic introduction of binding-site-mediated constraints that retains analytical tractability—beyond uniform superhelical wrapping—is currently lacking. Here, we present a mathematical framework for introducing such constraints in the context of the rigid-base-pair model that permits local DNA relaxation and yields closed-form expressions for elastic binding free energies. Importantly, the methodology enables the evaluation of individual contributions to the free energy, including entropy. Benchmarking against numerical solutions obtained via Monte Carlo sampling and validation with experimental data ranging from competitive nucleosome reconstitution to spontaneous nucleosome breathing and force-induced unwrapping demonstrates quantitative fidelity. The approach enables genome-wide evaluation of nucleosome binding affinities and can be readily extended to noncanonical and epigenetically modified DNA or histone cores. While the present study is based on the rigid-base-pair model, the same ideas extend to any rigid-body-based model, opening a route to a broad class of problems in biomolecular modeling.
Biological systems maintain long-term memories that guide future behavior but face the challenge of retaining beneficial memories while eliminating harmful ones. This challenge is exemplified in gene silencing mechanisms, which must suppress deleterious genetic elements without affecting essential genes. These systems face a fundamental credit assignment problem, as the fitness consequences of individual memory units are revealed only through aggregate physiological outcomes such as cellular growth or DNA damage. We propose that eukaryotic gene silencing mechanisms address this challenge through fluctuation-driven feedback. Silenced genomic regions are memory units whose stability depends on fluctuating residual transcription, and different genomic regions are coupled through shared epigenetic modifiers that respond to global stress signals. Our analysis shows that this feedback loop preferentially stabilizes the silencing of harmful elements, enabling adaptive refinement of the memory repertoire over time. The model explains the paradoxical reliance of stable silencing on residual transcription and the adaptive role of stress-induced desilencing, makes specific testable predictions, and illustrates how fluctuation-driven feedback can shape memory repertoires in biological systems.
In broad terms, the goal of a sensory system is to allow an organism to gain information about the external world, taking into account the physical processes that intervene between the sources of the signals and the organism's receptors. In olfaction, these transformations may be particularly complex, as they include the fluid mechanics of odorant transport, which is often turbulent. Here we focus on this transformation, viewing it as an inescapable signal processing stage that occurs before sensory transduction. The typically passive nature of an odorant (i.e., that it is carried by the flow, but does not affect the flow) allows for a concise characterization of how flow transforms the temporal characteristics of odorant concentration at the source into its temporal characteristics downstream. Specifically, the power spectrum (but not the odor concentration time series itself) is transformed in a linear fashion: Spectral components at the source are filtered and mapped to other frequencies at a downstream sensor. We characterize the dominant processes in the mapping as (1) frequency filtering acting as a low-pass filter on the source signal, (2) frequency spreading that redistributes power about the source frequency, and (3) frequency production of an underlying spectrum regardless of input frequency. Each of these processes arises naturally from the multiscale nature of turbulent flow environments. This machinery provides a framework for comparison with active sensation, viewed as another form of signal processing that occurs prior to sensory transduction.
Temperature dependence is a central constraint on biological timing, particularly in ectothermic development where reaction rates directly set developmental pace. A widely used framework for describing temperature dependence is the Arrhenius equation, which predicts an exponential increase in rates with temperature. However, biological rates often deviate from this prediction when measured across broader temperature ranges. While negative apparent activation energies are often attributed to protein denaturation, this cannot explain the similar behavior observed at temperatures where enzymes remain stable. These broader scaling patterns remain mechanistically unexplained. Here we present a general Markov chain framework for modeling biological timing as cascades of reversible, temperature-dependent steps. Across datasets, we identify a three-regime scaling structure: Arrhenius-like behavior at low and high temperatures, separated by a quadratic-exponential regime at intermediate temperatures. We show that this pattern arises naturally from differences in activation energies across steps in the network. The quadratic exponential regime is an emergent feature of averaging across many steps and is robust to variation across network realizations. In contrast, Arrhenius-like scaling at the extremes tends to be more variable and originates from smaller subnetworks. Apparent negative activation energies arise from network topology and reversibility in multi-step processes, even without protein denaturation. Our framework provides a general mechanistic framework for diverse temperature-scaling behaviors in biology and may help predict how developmental and physiological processes respond to environmental change. Although we focus on development, the model is broadly applicable to biological systems governed by multi-step reaction networks.
Neuronal cultures exhibit a complex activity, bursts, or avalanches, characterized by the coexistence of scale invariance and synchronization, quite stable with the percentage of inhibitory neurons. While this bistable behavior has been already observed in the past, the characterization of the statistical properties of avalanche activity and their temporal organization is still lacking, as well as a model able to reproduce these dynamics. Here we analyze experimental data of human neuronal cultures with controlled percentage of inhibitory neurons and characterize their statistical properties and dynamical organization. In order to model the experimental data, we propose a novel version of the Kuramoto model for two populations of oscillators, excitatory and inhibitory, implementing successfully the inhibition dynamics. The model can fully reproduce the experimental results, confirming the existence of correlations in the temporal organization of avalanche activity and the presence of an amplification-attenuation regime, as found in the human brain.
To reliably transmit information, cells exploit nonequilibrium drives to reduce errors. Kinetic proofreading is a classic mechanism that sharpens ligand discrimination by T lymphocytes, yet it remains unclear whether adaptive immunity relies on kinetic proofreading alone to achieve high fidelity. Here, we propose an alternative: an enhanced form of mechanical proofreading (MPR), in which adaptive force generation during dynamic cell-cell contact enables faithful selection of high-affinity B lymphocytes. Using a coarse-grained model validated by experiments, we show that adaptive MPR, characterized by mechanical feedback between force exertion and contact formation, supports robust discrimination of receptor quality regardless of ligand quantity. While MPR generally balances tradeoffs between speed and fidelity, a negative scaling of contact duration with ligand abundance reveals the presence of feedback. By modulating interactions among distinct ligands that share mechanical load at membrane contacts, adaptive MPR may help mitigate autoimmunity or enhance multivalent vaccines. Overall, this work generalizes proofreading to include cellular designs that operate across scales to reconcile competing functional demands at the systems level.
