Fernando Ramiro-Manzano
The wave is considered a paradigm in dance and connects bodily expression with nature. Although wave concepts such as propagation and phase have proven to be powerful tools for dance analysis, many aspects of bodily expression, including partner dance, have been investigated using numerical approaches and neural networks. Complementarily, compact analytical models have been especially successful for describing human motion, particularly gait. Here, we leverage wave-physics concepts to provide a comprehensive wave-based and oscillatory analytical characterization of expressive motion in partner dance. We apply this framework to Bachata Sensual, a dance style in which the wave is the leitmotif. We analyse three dance couples (Phase I) performing five movement sequences and one composite. The sequences exhibit multiple wave phenomena, from time-dependent interference to the generation-like emergence of harmonics. Within this wave-physics perspective, the formalism can be viewed as a choreographic motion notation. As an illustrative acoustic analogy, harmonic components extracted under boundary conditions can be mapped to audible frequencies, forming musical dyads. Within certain limits and not rigidly constrained by body morphology, modal response can be tuned to underpin fluid motion, adapting across musical timescales and movement patterns. Overall, this wave-physics notation highlights connections between partner-dance expressivity and harmonic nature.
Gustavo G. Cambrainha, Daniel M. Castro, Leonardo L. Gollo, Pedro V. Carelli, Mauro Copelli
Apr 23, 2026·q-bio.NC·PDF The hierarchical organization of the brain is a fundamental structural principle, while brain criticality is a leading hypothesis for its collective dynamics. However, the connection between structure and signatures of criticality remains an open question. Here, we address this issue by applying phenomenological renormalization group approaches to large-scale neuronal spiking activity from the mouse visual cortex and hippocampus. We find that signatures of criticality are not uniform, but instead vary systematically along the known anatomical hierarchy in both brain systems. Strikingly, the direction along this gradient is inconsistent across different criticality exponents, revealing a nontrivial, measure-dependent organization: exponents based on static properties point to a gradient in one direction, while the exponent based on dynamic properties points in the opposite direction. Moreover, the signatures across the visual system are strongly modulated by the engagement in a visual task. We show that the correlations among criticality markers of different brain regions during active engagement are sufficient to reconstruct the anatomical hierarchy from the dynamics. Scaling exponents closely follow a theoretically predicted scaling relation among them, and covary with the hierarchical position. Our findings provide a direct link between the collective dynamics of neurons and the macroscopic architecture of the brain.
Matthias Le Bec, Guillem Pérez Martín, Cameron Boggon, Yiyao Hu, Leonardo Puggioni, Rosa Heydenreich, Alexander Mathys, Luca Giomi, Eleonora Secchi, Lucio Isa
Bacterial colonies composed of elongated cells form active nematic fluids that spontaneously self-organise into ordered domains of aligned cells and exhibit self-generated chaotic flows powered by cell growth. While their dynamics have attracted significant attention, the role of initial conditions remains largely unexplored due to a lack of precise patterning methods. Here, we harness the precision of capillary assembly to pattern Bacillus subtilis endospores into arrays with controlled positions and orientations at single-cell resolution. Upon germination and growth of cell chains, we quantify the dynamics and morphologies of the resulting bacterial films. While orthogonally seeded spores lead to chaotic dynamics, seeding them with parallel orientations yields films with high nematic order across millimetres, which subsequently synchronously buckle upon further growth. Our observations are captured by numerical simulations and a model that describes the buckling dynamics starting from the mechanical properties of individual filaments. By programming local cell orientation with single-cell precision, we finally harness nematic alignment to create macroscopic bacterial films with local optical anisotropy, via structural colouration and light polarisation. Our findings demonstrate that initial conditions play a key role and offer exciting opportunities to control the spatio-temporal organization of bacterial assemblies towards addressing open biological questions and realizing living materials with tailored properties.
Denis S. Grebenkov
Autocatalytic processes underlie diverse systems in which replication is triggered at interfaces, including heterogeneous catalysis on solid substrates, enzyme activity at membranes, viral infections, biofilm growth, and spatially structured ecosystems. In a typical scenario, particles move in a bulk medium and interact with surface regions, where they may either disappear or reproduce through branching, splitting or fission. Here, we develop a general theoretical framework to understand such surface-mediated autocatalytic processes. We show that the interplay between loss and replication at surfaces gives rise to rich population dynamics. For this purpose, we derive a renewal-type nonlinear integral equation for the generating function of the population size, providing access to its full probability distribution and statistical moments. We further establish an equivalent description in terms of a Fokker-Planck equation with nonlinear Robin-type boundary conditions that encode surface reactions. Our results identify distinct dynamical regimes and universal scaling laws, and provide a unified framework to predict when surface activity promotes extinction or explosive growth. These findings offer quantitative insight into catalytic efficiency, metabolic regulation, and population persistence in spatially heterogeneous environments.
Lukas Müllender, Berk Hess, Erik Lindahl
Neural network potentials (NNPs) are rapidly changing the landscape of state-of-the-art molecular dynamics (MD) simulations. To make full use of this development, the community needs flexible, easy-to-use interfaces firmly integrated with existing methodologies. To address this, we here present an interface for hybrid machine learning/molecular mechanics (ML/MM) simulations implemented in the widely used MD code GROMACS. The interface enables NNPs trained in the PyTorch framework to contribute energies and forces during MD simulations, either for selected subsets or entire molecular systems. By defining a flexible set of model inputs and outputs, the interface is agnostic to specific NNP architectures and can accommodate a wide range of descriptor-based and message-passing models. In particular, the design integrates NNP inference seamlessly into the extensive GROMACS molecular simulation ecosystem, providing users with the capability to straightforwardly combine NNPs with existing advanced sampling and free energy workflows. We demonstrate the capabilities of the interface using several representative applications, including enhanced sampling of peptide torsional free energy landscapes, absolute solvation free energy calculations, and protein--ligand simulations. We also run performance benchmarks on water boxes for several different NNP architectures. Our interface is available in recent GROMACS releases, and we believe it will provide a practical foundation for incorporating machine learning potentials into production MD simulations of biomolecular systems.
Chesson Sipling, Yuan-Hang Zhang, Massimiliano Di Ventra
A major unresolved question in Neuroscience is: What is the origin of the observed scale-invariant correlations in neural activity? Many researchers support the ``criticality hypothesis,'' which proposes that the brain operates near a critical point, optimizing various information processing functions. We argue that such a critical point may not exist. Rather, the coupling between neurons and slowly varying resources (acting as ``memory''), may instead generate a robust phase of neural activity with such scale-invariant correlations. This ``memory-induced'' long-range order (MILRO) phase is then stable to perturbations, unlike a critical point. We suggest that this MILRO phase could provide a more natural and consistent explanation of the existing experimental data than the criticality hypothesis.
Chunming Zheng
We study a stochastic model of collective motion in which individuals update their orientation through pairwise aligning or anti-aligning copying interactions. We analyze both annealed dynamics, where interaction types are chosen probabilistically at each update, and quenched dynamics, where individuals are permanently assigned to aligning or anti-aligning subpopulations. Starting from the microscopic master equation on the circle, we derive an exact mesoscopic description via a Fourier-mode expansion and a systematic large $N$ expansion, obtaining closed Fokker-Planck equations and effective stochastic differential equations for the polarization. We show that competing alignment and anti-alignment suppress long-range polar order in the thermodynamic limit in both cases, while finite systems display nontrivial fluctuation-induced structure controlled by the interaction composition. Our results, validated by Gillespie simulations, establish an analytically tractable framework for collective dynamics characterized by competing copying rules and intrinsic noise.
Gastón Avetta, Jose Lobera, Juan José Zárate, Inés Samengo, Damián G. Hernández
Apr 21, 2026·q-bio.NC·PDF Perceptual judgments of sequential stimuli are systematically biased by prior expectations and by the temporal structure of sensory input. In haptic discrimination tasks, these effects often manifest as time-order asymmetries, whereby the perceived difference between two stimuli depends on their presentation order. Here, we introduce a dynamical Bayesian model that accounts for these biases by combining noisy sensory measurements with an evolving internal representation of stimulus intensity. The model formalizes perception as an inference process in which prior expectations are updated by incoming stimuli and propagate in time between observations. We test the model on psychophysical data from vibrotactile discrimination experiments, in which participants compare pairs of sequential stimuli with varying intensities. With a small number of parameters, the model quantitatively reproduces both the direction and magnitude of time-order effects across subjects, as well as the observed inter-individual variability. The inferred parameters provide a compact description of perceptual biases in terms of prior expectations and noise characteristics. Beyond fitting the data, the model induces a transformation of stimulus space, leading to a subject-dependent geometry of perceived stimuli. In this transformed space, perceptual judgments exhibit approximate symmetries that are absent in the physical stimulus coordinates. These results suggest that temporal biases in perception can be understood as a consequence of dynamical inference, and that they impose non-trivial geometric constraints on perceptual representations.
Monika Kish, Suchitra Pradha, Jessica L. Ramsay, Paloma Munguía Salazar, Jonathan Phillips, Daniel R. Kattnig
The light-dependent magnetic compass of night-migratory songbirds is widely hypothesized to rely on the radical pair mechanism within retinal cryptochrome. However, bridging the mechanistic gap between microsecond quantum spin dynamics and the long-lived, global protein conformational changes required for cellular signalling remains a formidable challenge. Here, we apply redox state-resolved hydrogen/deuterium-exchange mass spectrometry (HDX-MS) to map the conformational landscape of European robin cryptochrome 4a (ErCry4a) across its photocycle. We reveal that photochemical reduction drives robust, allosteric structural transitions across key functional nodes, including the phosphate-binding loop (PBL), protrusion loop (PL), FAD-proximal helix α17, and the C-terminal α22/α23 network. Crucially, we isolate the structural fingerprint of the transient semiquinone, the presumed signalling species. Rather than acting as a linear structural stepping-stone, the semiquinone exhibits a distinct, non-monotonic conformational signature characterized by a transient destabilization of the PBL and PL, contrasting sharply with the global rigidification observed in the fully reduced state. These findings establish the semiquinone as a structurally unique and functionally competent biological entity. Our results provide direct biophysical evidence for a dedicated, high-fidelity structural signalling cascade, detailing how localized quantum-level photochemistry is translated into the precise conformational dynamics required for animal navigation.
Damián G. Hernández
Apr 21, 2026·q-bio.QM·PDF We analyze information transmission in a recently proposed coarse-grained model of polymer replication by framing it as a communication channel between templates and copies. By calculating the mutual information in the steady-state limit of long chains, we recover the accurate-random phase diagram and establish that the information per-monomer depends solely on template specificity within the accurate regime. Crucially, even in the accurate region, small error fractions lead to substantial information loss due to the nonlinear relationship between errors and mutual information. Examining the information-to-energy cost ratio reveals non-monotonic behavior as a function of monomer alphabet size, with an optimum determined primarily by the per-monomer assembly free energy. For DNA's four-base alphabet, we find that the observed effective assembly energy (at least $14\,k_B T$) places the system far from the information-transmission optimum, suggesting that biological replication may prioritize the suppression of spontaneous random assembly over information-to-energy efficiency. We also characterize achievable rate-fidelity trade-offs using Shannon bounds, providing a theoretical framework for evaluating future proofreading mechanisms in ensemble models.
Robbert Decruyenaere, Clara Tanghe, Senne Van Wellen, Karel Van Acoleyen
Precise and flexible control of structured light fields is essential for applications ranging from optical trapping and quantum simulation to microscopy and materials processing. Acousto-optical deflectors (AODs) are widely used in these settings due to their high speed, large damage threshold, and ability to generate steerable optical tweezers. Multi-tone driving offers a powerful alternative to slow sequential scanning, enabling the projection of complex patterns with high accuracy as rapid acoustic modulation averages out inter-spot interference. In two dimensions, however, intermodulation between tones in orthogonal AODs can reintroduce coherent artifacts. We present a fast, feedback-free AOD projection scheme based on an incommensurately staggered frequency lattice that intrinsically suppresses such artifacts. For separable two-dimensional target patterns, our method removes the need for scanning entirely, enabling substantially faster and highly accurate projections. We further extend the approach to non-separable images using a minimal scanning strategy that maintains rather high projection speeds. These results demonstrate that appropriately engineered multi-tone AOD driving offers an efficient and robust route to high-speed, high-fidelity generation of arbitrary intensity patterns.
Manas Kumar Dalai, Ankita Mahakhuda, Abinash Prusty
Naturally produced stingless bee hive (NP-SBH) is an intricately produced material by the combination of waxes, resin and other biological materials that offers protection and structural stability to the bee colony. This study explores a detailed analysis of Indian stingless bee hive material using multi-characterization techniques approach to evaluate their morphological, ultrastructural, chemical composition and their crystallinity. FESEM reveal uniformly distributed micro-oval structures along with graphene sheets throughout the observed region. Furthermore, Energy Dispersive X-ray Analysis (EDAX) provides the richness of carbon (C) in graphene as well as in the micro-oval structure. HRTEM gives an insight about the internal ultrastructure and arrangement of atoms in the sample which revealed the presence of multiple graphene sheets. The ring shape electron diffraction pattern and high resolution lattice fringes provide the arrangement of carbon atoms, with interlayer spacing (d) value 3.4 Å, well agreed with that of graphene. Furthermore, X-ray Diffraction (XRD) and Fourier Transform Infrared (FTIR) spectroscopy support the presence of graphene. As a debut, we observe blue emission from PL spectroscopy with decay times 1.18 ns (42 %) and 5.41 ns (58 %).
Mariia Kryvoruchko, Brian A. Camley
Apr 20, 2026·q-bio.CB·PDF When cells collide, they often exhibit "contact inhibition of locomotion" (CIL), a behavior in which cells repolarize and migrate away from the site of contact. Experimental CIL outcomes are highly variable - why? Here, we develop a minimal stochastic model to quantify how intrinsic noise in cell polarity, arising from the finite number of signaling molecules, influences CIL decision-making. We simulate polarization dynamics by tracking individual Rho GTPase proteins that diffuse and switch stochastically between the cell membrane and cytosol. In the absence of cell-cell contact, the polarity axis diffuses rotationally - the cell's orientation wanders - with a diffusion coefficient that decreases as Rho GTPase copy number increases. Assuming that cell-cell contact inhibits Rho GTPase activation, we investigate how contact geometry, duration, and strength affect CIL sensitivity. At low protein copy number, weak, brief, or spatially narrow contacts are masked by molecular noise. In contrast, at high protein copy number, intrinsic polarity noise is negligible, and randomness in CIL response is more likely to reflect the variability from collision to collision in the cell-cell contact properties.
Davey Plugers, Kunihiko Kaneko
Gene expression in cells is stochastic, yet differentiation is robust. We propose a mechanism in which frustrated genes with weakly stable intermediate expression undergo noise-driven switching between basins of attraction, followed by irreversible fate fixation through slow epigenetic feedback. Regulatory interactions amplify effective noise and promote differentiation. We derive analytic expression for the logarithmic dependence of differentiation time on noise strength and input-dependent cell-fate selection, and demonstrate homeorhesis, the dynamical robustness of the epigenetic landscape.
Sabyasachi Mukherjee, Anirban Sain
During cell division active flows occur in the cortex, a thin layer of gel like network of acto myosin filaments, beneath the cell surface. The cortical flow and the associated stresses bring about change in the cell shape, in particular a sharp invagination at the mid cell. Using 3D phase field simulation of an active deformable shell, which captures coupled dynamics of cortical velocity and nematic order, we show how a nematic like actomyosin ring spontaneously emerge at the equator and drive sharp invagination. We further demonstrate how different cortical flow patterns, including counter rotating flows emerge near the division furrow. We show that these flow patterns, often attributed to intrinsic chirality of actomyosin filaments can instead arise from bias in the initial nematic alignment, revealing a memory effect in the system. By analyzing a simpler model of activity gradient driven compressive flow on a flat interface we decipher the main ingredients for surface instability leading to invagination and counter moving flows.
Greta Grassmann, Giancarlo Ruocco, Mattia Miotto
Biomolecular phase separation is typically attributed to the polymer physics of long, disordered chains. However, the underlying chemical grammar, i.e. the specific interactions between protein and RNA building blocks, remains poorly understood. We decouple those effects by screening the phase behavior of the complete dipeptide library in presence and absence of nucleic acids using full-atomistic molecular dynamics simulations. We demonstrate that (i) even these ultrashort units encode the instructions for spontaneous condensation, proving that phase separation is fundamentally rooted at a sub-polymeric level. (ii) Nucleic acids do not act as generic anionic glue but exert instead a base-specific regulatory logic. (iii) Individual nucleobases function as chemical tuners that dissolve, stabilize, or fluidize condensates based on their molecular identity. Overall, our minimal framework reveals that while polymer length enhances assembly, the core properties and regulatory control of condensates may be also governed by a fine-tuned chemical alphabet of peptides and nucleobases.
Kaan Öcal, Syrine Ghrabli, Michael P. H. Stumpf
Apr 17, 2026·q-bio.PE·PDF Statistical physics can describe the behavior of microbial populations consisting of many heterogeneous individuals. A direct consequence is the existence of phase transitions, where the behavior of a population changes discontinuously upon a small perturbation. While such phase transitions have often been proposed in biology, connecting observed behavior to the underlying physics has remained challenging. We show how phase transitions naturally arise in microbial population dynamics and highlight their connection with genealogies. We rigorously demonstrate the existence of a first-order phase transition in a model of bacterial plasmid engineering and find a strict lower bound on the number of plasmids that can be stably maintained in a population.
Michael Massoth
This paper develops a physical framework for the prebiotic emergence of information and meaning. Building on Constructor Theory, we define information as a reproducible physical difference and meaning as a difference with stable functional consequences. Casimir-Lifshitz-coupled protocell clusters serve as a minimal model that exhibits reproducible attractors, ordered transitions, and autonomous task structures. We show that such clusters carry both informational states (e.g., distances, geometries, gradients) and meaningful states that regulate prebiotic tasks such as approach, exchange, or stabilization. This approach integrates physical mechanisms, computational mechanics, and early proto-semantic functions into a coherent account of information formation before biology.
Qianchen Gong, Yingpeng Liu, Yan Zhang, Muhua Zheng, Kesheng Xu
Apr 17, 2026·q-bio.NC·PDF Experimental evidence indicates that intracellular chloride concentration regulates the excitation and inhibition (EI) balance, yet the mechanisms by which activity-dependent chloride dynamics drive seizure evolution and stage transitions remain unclear. We present a conductance-based neuronal network in which EI balance emerges from chloride homeostasis via channel-mediated influx and transporter-mediated extrusion. We show that the fraction of inhibitory synaptic conductance contributing to channel-mediated influx acts as a control parameter that organizes seizure dynamics into distinct stages,pre-ictal, ictal-tonic, and ictal-clonic,distinguished by characteristic amplitude and frequency signatures. Decreasing this fraction shortens ictal activity and suppresses seizure initiation, whereas high fraction promotes the emergence of ictal-tonic and ictal-clonic stages and spiral-wave dynamics, rendering seizure dynamics largely insensitive to inhibition. At intermediate values, seizures bypass the ictal-tonic stage and emerge directly as the icta,clonic stage. Moreover, joint variation of fractions with synaptic strengths reveals that recurrent excitation expands the tonic-clonic seizure, while recurrent inhibition prolongs pre-ictal states and suppresses ictal-clonic activity.
Yanlin Zhang, Yan Zhang, Muhua Zheng, Kesheng Xu
Experimental evidence indicates that intrinsic temporal dynamics operating across multiple time scales are closely associated with the emergence of periodic spatial activity of increasing complexity. However, how information encoded in grid-like firing patterns for path integration is processed across these intrinsic time scales remains unclear. To address this question, we introduce adaptive time scales through a leak term in recurrent neural networks (RNNs), forming leaky RNNs discretized from the continuous attractors of firing rate models. Our results demonstrate that leaky RNNs substantially enhance the emergence of well-defined and highly regular hexagonal firing patterns. Compared with vanilla RNNs lacking a leak term, the trained leaky RNNs produce more accurate position estimates while generating reliable grid-cell-like representations. Furthermore, under identical noise conditions, leaky RNNs consistently exhibit more stable dynamics and better-defined grid structures. The learned dynamics also give rise to stable torus attractors with a clear central hole, supporting robust and regular grid-like activity. Overall, the dynamic leak acts as a low-pass filtering mechanism that protects recurrent neural circuitry from noise, stabilizes network dynamics, and improves path-integration accuracy in recurrent neural networks.