Biological Physics Papers

ALT Dengue epidemics exhibit strong stochastic variability, temporal memory and complex temporal organization that are difficult to capture using conventional deterministic epidemic models. However, the dependence of epidemic fluctuations on the underlying dynamical state and their influence on reconstruction across temporal scales remain insufficiently understood. In this work, we develop a data-driven stochastic framework to investigate dengue epidemic dynamics using daily infection data from Imphal West District, Manipur, India, for 2023-2025. We use Singular Spectrum Analysis (SSA) to separate deterministic and stochastic epidemic components. Logarithmic regression and autocorrelation analyses reveal empirically state-dependent noise amplitudes and persistent temporal correlations, supporting a phenomenological description involving multiplicative, temporally structures stochasticity. Polynomial and logistic-type drift functions are subsequently fitted to the deterministic components a

ALT Rapid evolutionary adaptation may be necessary for organisms facing strong environmental pressures. Although selection often acts continuously, in the case of B cells evolving high affinity antibodies in germinal centers, selection can take place after multiple rounds of division. To study how the number of divisions between selection events affects adaptation, we develop a minimal computational model of cyclic selection. We find that the optimal average mutation rate per division and the corresponding adaptation velocity are nearly invariant across number of divisions between selections, and we quantitatively explain this behavior with a theory based on the dynamics of the "nose", i.e. the fittest individuals in the population. We also find that when the number of divisions between selection events is large, then mutating every division results in slower adaptation than mutating only at the last division before selection (as is the case for B cells). Our model provides a framework for

ALT Purpose: Finite element (FE) models are indispensable for studying the biomechanics of traumatic brain injury (TBI), and modern head FE models typically comprise thousands to millions of elements. The resulting brain strain is therefore high-dimensional, and when combined with dense temporal sampling and large numbers of head impact cases, this produces datasets of substantial data volume. Accordingly, this study investigates the effective dimensionality required to accurately represent brain strain and proposes a data-driven framework to reduce the dimensionality of FE simulation data while maintaining high spatiotemporal fidelity. Methods: We developed a series of "clustering templates" by applying K-means clustering to feature vectors derived from the maximum principal strain (MPS) time-history curves of 118,840 brain elements over 3,693 impact simulations using the THUMS head model. The performance of these templates, with cluster counts (K) ranging from 8 to 8192, was rigorously e

ALT Classical chemotaxis models consider the directed motion of cells in response to a chemical gradient. Recent experiments suggest that biological microswimmers such as bacteria can perform chemotaxis toward multiple chemicals, which dynamically evolve depending on the distribution of the microswimmers. In this Letter, we consider a mass-conserving chemotaxis model in which pattern formation is driven by two competing chemicals, autoinducers and nutrients, where the former acts as an activator and the latter as an inhibitor for coarsening. We show that this can give rise to diverse patterns including long wavelength uninterrupted coarsening, Turing-like arrested coarsening, traveling waves as well as a novel class of mixed multi-scale oscillatory coarsening patterns, where a dense aggregate coexist with a dynamically spreading part of the population. We analytically derive the conditions for the onset of such patterns and discuss their physical implications, which significantly broadens

ALT The co-condensation of DNA and proteins plays an important role in genome compaction, including heterochromatin formation and chromosome segregation. Meanwhile, transcription and replication continuously generate DNA supercoiling, yet how the resulting torsional stress affects co-condensation remains poorly understood. Here, by coupling DNA elasticity to a protein concentration field, we show that supercoiling acts as a topological catalyst for co-condensation through a mutually reinforcing interplay with protein wetting. Specifically, protein wetting facilitates twist-to-writhe conversion, generating highly writhed DNA conformations that in turn promote capillary collapse into a protein-rich droplet. In the absence of supercoiling, DNA remains kinetically trapped in metastable extended states within our model, whereas sufficiently strong supercoiling removes the nucleation barrier and enables spontaneous co-condensation. These results establish topology as a distinct control parameter

ALT Enhanced-sampling methods enable molecular simulations to access configurational regions that are difficult to explore with conventional dynamics, but their efficiency often deteriorates as the number of collective variables (CVs) increases. Here, we introduce an enhanced-sampling framework that couples Stein variational gradient descent (SVGD) with parallel restrained molecular dynamics simulations to facilitate exploration of higher-dimensional CV spaces. SVGD collectively transports an ensemble of interacting particles toward a target probability distribution by combining mean forces that guide particles toward low-free-energy regions with kernel-mediated repulsive interactions that maintain particle diversity. We validate the approach using an analytically solvable double-well model, followed by aqueous alanine dipeptide and tripeptide in two- and four-dimensional CV spaces, respectively. In each case, the particle ensemble reproduces the corresponding target distribution, and the

ALT The gut microbiota shapes host metabolism, development and immunity, yet how the host regulates its composition in the presence of complex ecological interactions remains poorly understood. Recent experiments that measured T cell reactivity to individual strains in germ-free mice inoculated with a defined microbial community found that T cell recognition across microbial strains is sparse but broad, with a few polyspecific clones recognizing antigens shared across taxonomically diverse strains. Reanalyzing these experiments, we show that these T cell responses are associated with abundance changes of the strains they target. To interpret these findings, we develop a model linking the T cell repertoire to microbial abundances, in which a repertoire is allocated to minimize microbe-driven harm at the cost of maintaining an immune response. The model rationalizes the observed sparsity of gut T cell reactivity and shows that this allocation can emerge from competition between T cells for m

ALT Hippocampal neurons not only encode the animal's current position, but also generate sequences representing potential future states and past locations. During goal-directed navigation, the represented location sweeps ahead of the animal's location (online forward replay). After such tasks, during rest, neuronal sequences occurring during sharp-wave ripples replay previously explored trajectories (offline forward replay). Following random foraging, however, neuronal sequences resemble Brownian motion, which does not correlate with behavior (diffusive replay). The mechanisms producing such diverse sequences, and their distinct effects on spatial coding and navigation, remain unclear. Here, we address these questions using a closed-loop model of navigation in a two-dimensional open-field environment based on a spiking neuronal network. In our framework, neuronal sequences are generated by a common mechanism driven by background noise and the balance of excitation and inhibition. Different

ALT The desert-dwelling sandfish (Scincus scincus) moves within dry sand, a material that displays solid and fluidlike behavior. High-speed x-ray imaging shows that below the surface, the lizard no longer uses limbs for propulsion but generates thrust to overcome drag by propagating an undulatory traveling wave down the body. Although viscous hydrodynamics can predict swimming speed in fluids such as water, an equivalent theory for granular drag is not available. To predict sandfish swimming speed, we developed an empirical model by measuring granular drag force on a small cylinder oriented at different angles relative to the displacement direction and summing these forces over the animal movement profile. The agreement between model and experiment implies that the noninertial swimming occurs in a frictional fluid.

ALT Red blood cells carrying intracellular vacuoles, called pocked cells, are a sensitive marker of splenic function: about 0.5% of cells in healthy people, 20 to 70% after splenectomy. Vacuoles too small to see are common, and visible ones are thought to grow when small ones merge. We tested that idea with a particle model that resolves the cell's membrane, its contents and every vacuole, and with an inventory of the vacuole volume a cell can make. In tube, shear and vascular flows, a cell's vacuoles met within a second, and, allowed to fuse, ten merged into one to five. Meeting is not what limits growth; material is. At the rate measured after splenectomy, all the vacuoles an average cell makes in its 120-day life fuse into one body 0.6 μm across, the size healthy cells carry, but 16 times smaller in volume than the 1.5-μm bodies of patients without a spleen. Taking vacuole size as set at formation, a population model in which vacuoles are made mainly in young cells, and far faster in a

ALT Estimating mutual information in large neural populations is limited by the exponential growth of possible neural response patterns. Here we apply a Monte Carlo framework to make this problem tractable by sampling from a conditionally independent population model fit from single-neuron responses. The estimator accurately recovers analytical ground truth, corrects finite-data bias, and scales to empirical recordings containing 100 to 10,000 neurons from visual cortex, hippocampus, and retina. Across systems, stimulus uncertainty decays exponentially with population size, revealing a simple scaling law for high-dimensional neural codes.

ALT Platelet traction contracts fibrin networks, yet whether contraction promotes or hinders the escape of suspended particles is unclear. We coupled an overdamped Langevin model of platelet–fibrin mechanics to Brownian transport of finite-size tracers through frozen network states. Nine mechanical replicas were evolved at four filopodial traction baselines (0–8.8 nN), stacked into 5-μm-thick domains of 50 layers and probed at 0–15 s with 0.5-μm tracers (29,952 trajectories, 100 s observation). Benchmarked against experimental clot-contraction kinetics, the simulated contraction is post-lag, within one order of magnitude of experimental rates and corresponds to the onset of the contraction phase. Relative to the matched zero-baseline control, every active-baseline state showed greater projected contraction and a larger tracer-accessible volume (+9.3% to +27.6%; exact sign test p=0.0039 per condition). At 15 s, mean survival at 100 s fell from 27.3% to 14.3% and the restricted mean exit tim

ALT AI-enabled computational drug discovery is advancing rapidly in predicting molecular structures, binding poses and affinities, but ligand dissociation kinetics remain harder to access. Bound-state residence time, important for biological response and kinetic selectivity, is not fixed by equilibrium affinity but depends on the dissociation free-energy landscape and dissipative friction effects. Here, we show that restrained umbrella-sampling trajectories can be used to calculate residence times on the minutes timescale. Time-resolved coordinate fluctuations yield friction profiles that incorporate non-Markovian effects on dissociation kinetics. Combining them with the free-energy profile, we predict a residence time of τᵣₑₛ = 275 s for the adenosine A2A receptor with ZM241385 (comparable to the experimental value of 125 s) and reproduce the experimental ranking of wild type and two receptor mutants. For HSP90 with compound 18 and RNase S with the S15 peptide, we predict τᵣₑₛ = 2,169 s a

ALT Epithelial dynamics are often studied using two-dimensional models that omit cell height. Here, we investigate how this third dimension influences tissue fluidization driven by cell self-propulsion. We identify three effects absent from conventional 2D models. First, cell height controls fluidization through a reentrant transition: squamous and columnar tissues are more resistant to fluidization than cuboidal ones. Second, neighbor exchanges through transient scutoid states substantially enhance cell motility, compared to classic T1 transitions. Third, self-propelling cells develop pronounced apicobasal shape asymmetry, with the experimentally less accessible basal surface providing a more accurate indicator of cells' target shape index and motility.

ALT Quantifying the biological effects of radiation is essential for radiotherapy and radiation protection. While research has centered on statistical analyses of experimental data, this approach often fails to elucidate underlying mechanisms, limiting the development of generalizable models. Describing time dependent phenomena, such as sub-lethal damage (SLD) recovery during fractionated irradiation, presents a particularly significant challenge for existing static models. To address this, we developed a mechanistic mathematical model that extends the classical multi-target, single-hit model by introducing explicit recovery transitions among damage states, allowing the simultaneous description of radiation-induced damage accumulation and biological recovery. We applied this model to Elkind and Sutton's classical split-dose experimental data to determine its parameters. The model reproduced the major features of split-dose survival curves for treatments with 0-, 2.5-, and 23-hour recovery

ALT Observables in complex biological systems are often reduced in their inherent complexity, resulting in abstract, a priori mathematical representations based on simple concepts such as points in spaces or interconnected nodes in lattices or networks. This reduction is relevant not only for the formulation of multiscale mathematical models and field theories but also for experimental planning and data collection to gain systematic evidence for the nature of such systems, thus extracting robust causal relationships that would lead to better control of the system. However, mathematical abstraction remains challenging and problematic, especially given the tightly intertwined elements between objects and their environment, the system's affordance, and the fact that all these aspects limit fundamental mathematical concepts such as set theory, thus hindering the definition of phase spaces in extreme cases. In this work, I introduce a potential axiomatic representing a nonreducible representati

ALT In nature, animals interact with complex terrains and often generate forces, deformations, and vibrations across the body and environment. Such interaction-induced mechanical information may guide behavior, yet its generation and use are difficult to resolve because movement rapidly and continually reconfigures the interactions that produce it. Sensory neurophysiology and biomechanics have revealed how mechanical information is transformed within sensory, neural, and musculoskeletal subsystems, whereas studies in complex terrains reveal rich whole-body interactions and behavioral outcomes. Connecting these levels remains a challenge. Here, I present a focused perspective on how biologically guided robotic physical models can help identify candidate mechanical information generated through whole-body interactions, test its physical mechanisms and behavioral benefits, and generate biological hypotheses. Three case studies illustrate this approach, multi-legged locomotor transitions to tr

ALT Transport networks are key to living and engineered systems. How can transport networks adapt efficiently using only local rules, without a global objective or specific boundary conditions? Inspired by observations in biological transport networks, we adapt networks by subsequently pruning edges by relative resistance, thus removing edges whose resistance is higher than the resistance for routing flow through the rest of the network. We find that adaptation by such a local, boundary-condition-free measure of redundancy, set by topology alone, yields sparse, loop-rich, low-dissipation networks.

ALT Neural crest cells (NCCs) provide a well-established system for investigating collective migration during embryonic development and exhibit migratory behaviors with parallels to cancer invasion. Previous studies establish roles for co-attraction, contact inhibition of locomotion, polarity persistence, and tissue confinement in guiding NCC migration. However, it remains unclear whether NCC migration in confined environments occurs through spatial restriction alone or also through a repolarization response induced by contact with boundaries of confinement. By developing a computational model that couples self-generated chemical transport with contact-mediated repolarization and mechanical interactions, we show that cell-boundary contact can reorient cell polarity and sustain directional migration under confinement, whereas no persistent collective direction emerges without confinement. Moreover, suppressing contact-mediated repolarization from non-permissive confinement boundaries while

ALT The extremely intense pulses of X-ray free-electron lasers (XFELs) have enabled imaging of radiation-sensitive samples, such as macromolecular microcrystals, beyond radiation damage limits. These sources have the potential to deliver biomolecular single-particle imaging, similar to cryo-electron microscopy but without the need for cryo-fixation and with temporal resolution from femtoseconds to milliseconds. While this possibility was recognized before XFELs were built, the biological single-particle imaging work-flow has previously only been demonstrated on large virus particles. Based on decades of improvements in X-ray beam focusing, particle delivery, diffraction detection, and advanced analysis, here we demonstrate imaging of a single molecular complex, the giant-hemoglobin erythrocruorin (Ery) with X-ray laser pulses. Two-dimensional classes of diffraction patterns could be reconstructed to 15 Angstrom resolution, and 3D images to approximately 20 Angstrom, while the 3D merged int