Communications Physics

Electrical signaling in biological systems is generally understood through the lens of single-channel biophysics, yet whether ensembles of ion channels can undergo cooperative opening and closing remains unclear. Here, we show that ensembles of voltage-gated ion channels can undergo bioelectrical order-disorder phase transitions driven by feedback between channel currents and local membrane voltage. When channels open, they carry ion-selective current that redistributes ions near the membrane and perturbs the transmembrane potential, thereby biasing the gating of nearby channels. This emergent nonequilibrium coupling generates a bona fide phase transition in ion channel ensembles. Finite-size analyses of the open-channel fraction, its fluctuations, and the distribution of collective channel states yield a voltage-temperature phase diagram with a first-order line separating collectively open and closed states and terminating at a critical point. The critical temperature is governed by a dimensionless conductance ratio set by ion transport, channel density, and confinement geometry. Applying this framework to measurements from the squid giant axon, the axon initial segment, and the nodes of Ranvier suggests that collective activation may be favored by high sodium-channel densities in large-diameter nerves, whereas the lower densities typical of potassium channels place them in an independent-gating regime.

Transcription factors organize into liquid-like condensates to facilitate gene expression, yet the physical mechanisms governing their formation and properties remain poorly understood. We study the size statistics of transcriptional condensates in human HAP1 cells using widefield and super-resolution microscopy tagging the epigenetic reader BRD4. We find that hubs that appear monolithic in widefield resolve into clusters of smaller droplets that resist coarsening. We link this size control to Active Model B+, a non-equilibrium field theory that captures a regime of reverse Ostwald ripening out of thermal equilibrium. In this regime, chemically driven currents cause larger droplets to transfer mass back to smaller ones, stabilizing a state of microphase segregation. The observed exponential size distribution of BRD4 foci quantitatively matches our numerical simulations, suggesting a universal physical picture for the non-equilibrium self-limitation of cellular condensates.

Bacterial propulsion is powered by torque generated by the flagellar motor and transmitted to the flagellum through the hook, a short deformable structure acting as a flexible joint. Despite the hook being essential for propulsion, the physical mechanism governing torque transmission remains poorly characterized. Here, we show that the torque consists of two components: one parallel to the flagellum, responsible for rapid spin, and another along the motor axis, which induces precession of the former component. Evidence of such a two-component torque also emerge in bead assay experiments, where the attached bead can exhibit complex trajectories rather than simple circular motion. We introduce a minimal mechanical model that captures both the spin/precession dynamics and the commonly observed circular orbits.

A recurring lesson from random ecological models is that coexistence is hard to come by: in the Generalized Lotka-Volterra (GLV) model with pairwise interactions, the probability that randomly sampled parameters admit a positive (feasible) equilibrium - a necessary condition for coexistence - is exactly 1/2n in n species, vanishing rapidly with diversity. This rarity is often read as evidence that coexistence demands specific ecological mechanisms. Real interactions, however, are rarely strictly pairwise: any nonlinear dependence of one species growth rate on anothers abundance, Taylor-expanded, generates higher-order interactions (HOIs) of increasing degree. Treating the interaction order d as a knob that indexes this nonlinearity, we map the random GLV with HOIs onto the Kostlan-Shub-Smale class of random polynomial systems and approximate the probability of feasibility (Pf ) analytically. We find a phase transition at d = 4: below this threshold, Pf decays with diversity as in the pairwise case; above it, the exponential proliferation of equilibria outpaces the probability that any given equilibrium is feasible, and the probability of feasibility increases with n, approaching one. The transition appears to be universal across symmetric coefficient distributions, but vanishes when sign symmetry of the parameter distribution is broken. This work uncovers a route by which feasibility emerges from nonlinearity alone, with no fine-tuning of parameters and no appeal to specific ecological mechanisms.

This Perspective formally proposes Particle Biology as a unifying theoretical framework to address the critical bottleneck in current life science research. Current life science research has reached a critical bottleneck. While the field has advanced to the study of 3D genomic spatial configurations and chromosomal organization, it remains largely descriptive and confined to the macromolecular level. This approach lacks a first-principles understanding of the underlying physical forces that drive biological processes. This Perspective formally proposes Particle Biology as a unifying theoretical framework. We establish an axiomatic system positing that life phenomena are fundamentally emergent spatiotemporal patterns of electromagnetic forces among atoms, electrons, and nuclei operating far from thermodynamic equilibrium. By defining biological states through the Biological Hamiltonian and mapping biochemical pathways to multidimensional Potential Energy Surfaces (PES), we bridge the gap between descriptive biology and predictive physics. We categorize core research technologies into three modalities--seeing, computing, and controlling particles--facilitated by advancements in Cryo-EM, AlphaFold 3, and Boron Neutron Capture Therapy (BNCT). Ultimately, the trajectory of molecular biology has evolved from cells to DNA and onto the 3D spatial genome, yet it cannot go deeper within current paradigms. The next logical evolution is to move beyond the macromolecular bottleneck to focus on the electromagnetic interactions between atoms and ions--the true Particle Biology level--to redefine disease and intervention.

In non-equilibrium (active) systems, increased driving is commonly assumed to amplify energy dissipation. This frames the efficiency of protein-based machines as a fixed or monotonically decreasing function with driving. Using picowatt-sensitive calorimetry and advanced entropy production metrics in reconstituted actomyosin networks, we show that energy dissipation depends non-monotonically on myosin-generated stress (driving). At low driving, dissipation increases proportionally with stress, consistent with near-equilibrium linear response. At high driving, however, dissipation decreases, revealing a far-from-equilibrium regime in which excessive load suppresses motor ATPase activity. This non-monotonicity reflects a transition from spatially localized stress at low driving to delocalized stress at high driving, where force per motor, and thus ATPase suppression, is maximized. Crosslinker mechanics tune this transition as fascin (slip bonds) amplifies stress localization and shifts the dissipation peak to higher driving, whereas -actinin (catch bonds) stabilizes under load, delocalizes stress, and shifts the peak to lower driving. Thus, enhanced mechanochemical coupling causes additional driving to restructure rather than amplify dissipation, revealing how material system organization (bonding), and not driving alone, governs energy flow far from equilibrium.

Single-molecule fluorescence microscopy combined with optical tweezers has enabled the direct observation of diffusing proteins on tethered DNA. These and complementary techniques reveal facilitated one-dimensional diffusion as a common functional mechanism for numerous DNA-binding proteins, with a wide range of heterogeneous diffusive behaviours arising from different DNA-binding modes. However, detailed investigations have been limited by the lack of methods to detect such heterogeneous diffusion. We have developed DeepDiffusion, a physics-informed neural network model for estimating the instantaneous diffusion at each point along a single-molecule trajectory. We show, using synthetic trajectories, that DeepDiffusion can accurately detect subtle changes in diffusion even when challenged with large underlying errors. DeepDiffusion can recapitulate previously characterised heterogeneity in experimental data and reveal mechanistic details of facilitated diffusion that are inaccessible to current methods. We expect DeepDiffusion to become a powerful tool for single-molecule researchers, allowing them to investigate the diffusion of their proteins of interest in unprecedented detail.

Microbial communities obey universal macroecological scaling laws, but which sub-processes generate them remains debated across competing theoretical frameworks. Here we calibrate a modified Yule-Simon model to metagenomic data from 11 distinct microbial environments, revealing that microbial ecosystems occupy a qualitatively distinct region of a two-parameter mechanistic space, characterized by near-neutral recruitment (i.e., linear preferential attachment) and broad diversification strategies, unlike any previously studied complex system, including prokaryotic proteomes and urban economies. This distinctive position, confirmed analytically and validated against sparse-data robustness tests, provides both a mechanistic explanation for observed self-similarities in microbial rank-frequency distributions and a quantitative signature of ecological assembly.

The hierarchical organization of multiphase biomolecular condensates into core-shell architectures is a fundamental problem in soft matter and biophysics. While classical explanations rely on hierarchies of interfacial tension ({gamma}) between coexisting liquids, the ultralow tensions of condensates (0.1-1 {micro}N/m) render such hierarchies potentially fragile. We introduce a robust assembly principle based on Polymer-Assisted Condensation (PAC), in which a single polymer species dictates the entire structure. The polymer nucleates a dense core by recruiting a condensation-incompetent protein (P1). A second incompetent protein (P2), which is repelled or otherwise thermodynamically disfavored from entering the polymer-rich core, is nonetheless recruited to the interface by weak attraction to P1, forming a stable shell. This effective repulsion-driven layering operates across a wide parameter space without requiring{gamma} asymmetries and yields a robust structure that is impervious to concentration fluctuations and environmental perturbations. Phase-field modeling and molecular simulations establish this mechanism and capture key features of nucleolar organization. Our work reveals a general physical pathway for encoding spatial order in soft, multicomponent fluids.

We develop a theoretical proposal linking vacuum stability and brain dynamics through superconductivity-inspired coherence, symmetry reduction, and the thermodynamic stabilization of low-entropy regimes. We take an unbroken SU(3) structure as a candidate stable residue of the low-temperature vacuum. At the neural level, we formulate a coarse-grained analog in which a two-fluid model with dissipative and coherence-supporting components describes brain dynamics. Specifically, the coherence-supporting component is proposed as a possible basis for the efficient binding and integration required to sustain a stable, unified conscious state. The proposal offers a common geometric language for relating physics and neuroscience with falsifiable signatures in coherence and state-dependent transitions. The main technical contribution is a computational algebraic model of conscious-state dynamics, where neural data are mapped to reconstructed state trajectories. Effective generators are inferred from those trajectories, and the two-fluid split is tested as a Cartan-root decomposition of su(3), with a rank-two commuting sector for coherence-preserving balance and six root directions for state transitions. This structure can be tested on neural data and contrasted with alternative dynamical models.

The classical model of bacterial flagellar motility posits a rotary engine driven by proton motive force (pmf), with torque generated by stator-rotor interactions and transmitted through a flexible hook to a helical filament. Despite decades of acceptance, this model faces fundamental challenges in thermodynamics, structural mechanics, evolutionary parsimony, and direct observational evidence. We develop and quantitatively test the murburn model, a new paradigm for bacterial motility in which water, produced as an inevitable byproduct of metabolic redox activity, is ejected via the basal secretory module and channelled along the spiral grooves of the flagellar filament. The ejected flow creates a local shear field that induces a transverse bending wave; the precession of this wave is observed as apparent rotation and generates thrust through anisotropic viscous drag, without any rotary motor, ion gradient, or axial rotation. The principal contribution of this work is a self-contained, first-principles treatment of this mechanism: for a unipolar flagellated cell we derive the governing low-Reynolds-number elastohydrodynamic relations from slender-body theory and show that physiologically realistic rates of metabolic water production reproduce the observed swimming speeds and apparent-rotation frequencies at a small fraction of the cellular energy budget, while direct jet propulsion is quantitatively excluded. Building on this derivation, we provide a force-balance comparison of the competing propulsion mechanisms, obtain a set of falsifiable predictions that distinguish the murburn model from the rotary motor, and report a structural analysis of cryo-EM flagellar-hook architectures that reveals solvent-accessible radial canals consistent with lateral water transport. The same single principle accounts for swimming, tumbling, gliding, spirochete undulation, and archaeal motility, without requiring rotating shafts, ion-gradient coupling, or complex switching mechanisms.

Single-cell growth rates fluctuate across time and generations, but identifying the biological origin of this variability is difficult because growth rate is usually inferred from noisy measurements of cell size or mass rather than measured directly. Here, we develop a simple and interpretable framework that separates measurement noise from biological sources of growth rate variability. We show that the autocovariance of inferred instantaneous growth rates carries robust signatures of the measurement process that are largely independent of the underlying biological growth dynamics, allowing the form and magnitude of measurement noise to be identified directly from data before introducing a model for the biological dynamics. We then use the autocovariance of accumulated growth to distinguish continuous within-cycle fluctuations, division-associated perturbations, and lineage-to-lineage variability. Applying this framework to bacterial and mammalian single-cell datasets, we find evidence for continuous growth rate noise in both systems. In E. coli, division-associated perturbations are large at birth compared with continuous fluctuations, but their contribution to growth accumulated over the full cell cycle is reduced by rapid relaxation. In contrast, mammalian cells show no division kicks, but stronger lineage-to-lineage variability. More broadly, our results provide a direct and interpretable route to identifying the biological origin of growth rate variability in noisy single-cell measurements.

How can simple organisms lacking nervous systems encode and transmit environmental signals to generate complex, adaptive behaviours? Using the unicellular organism Physarum polycephalum as a model, we identify a unifying mechanochemical mechanism that links intracellular calcium oscillations to large-scale behavioural coordination. We first demonstrate experimentally that local perturbation of the actomyosin cortex is sufficient to induce symmetry breaking and directed migration, even in the absence of nutrient cues. Building on evidence linking calcium concentration to actin depolymerization and contractile relaxation, we develop a mechanochemical tubule model in which self-sustained calcium oscillations are coupled to pressure-driven mechanics. We show that environmental cues, encoded through the local modulation of these oscillations, give rise to directed transport and the redistribution of biomass. By extending this framework to a two-dimensional phase-field model, we demonstrate that this mechanism is sufficient to generate a diverse set of slime mould behaviours, including chemotaxis, network formation, and balancing exploration-exploitation trade-offs. In doing so, we provide a single mechanistic framework linking intracellular dynamics to organism-scale behaviour across spatial and temporal scales. Our work shows that these sophisticated behaviours can emerge from the modulation of self-sustained oscillations coupled by diffusion, providing a physically grounded mechanism for information processing in non-neural organisms and offering insight into the evolutionary origins of coordinated behaviour.

Reentrant phase transitions, in which multicomponent systems phase separate at intermediate concentrations but dissolve at experimentally accessible higher concentrations, are ubiquitous in mixtures such as biomolecular condensates. We show that introducing reversible dimerization into a multicomponent Flory-Huggins model and integrating out the dimer state generate an effective three-body repulsion that reshapes phase diagram geometry. This emergent higher-order interaction arises naturally from an interaction competition and mass-action equilibrium, providing a microscopic explanation to the previously phenomenological three-body interaction. We derive a closed-form phase boundary equation capturing phase separation and reentrant dissolution in this minimal model, and predict explicit interdependence between competition strength, emergent many-body interactions, and dissociation constants. We recover and extend the reentrant phase boundary scaling relations through interaction renormalization, with regime of validity. We apply our model to G3BP1-RNA-suramin and explain the underlying mechanisms from the physical parameters inferred from reentrant phase boundaries.

Diffuse gliomas are deadly because the individual tumor cells invade - they travel far from the imageable mass, so it is impossible to remove the tumor completely. On the cellular level, glioma cells seem to be in either a "go" state (in which they do not divide) or a "grow" state (in which they do not migrate). We investigate what this tiny choice has to say about the large-scale speed of the invasion front and whether the implication is sufficiently strong to rule out the classical description of the Fisher-Kolmogorov-Petrovsky-Piskunov (Fisher-KPP) type, in which a single phenotype migrates and proliferates. We derive a two-phenotype reaction-diffusion model with density-dependent switching, and we prove the cooperative (quasi-monotone) structure and the associated comparison principle and study travelling-wave solutions of the model. A leading-edge linearization gives minimal front speed as minimizer of an explicit dispersion relation, and direct simulation verifies the predicted speed. In the experimentally relevant fast switching limit, we find a closed-form expression for the speed, that is, we obtain an effective Fisher-KPP equation with rescaled diffusivity and growth rate, with the fractions of the phenotypes. The "go-or-grow" (GoG) front can move at a maximum speed of half the Fisher speed for the same single-cell motility $D$ and proliferation rate $r$, which occurs only when the cells divide their time equally between the two phenotypes. This bound is directly testable: measurement of the front speed, plus independent determination of $D$ and $r$, discriminates the two hypotheses, and in the GoG case, yields recovery of the phenotype balance. We then extend the result to anisotropic (DTI-informed) invasion along white-matter tracts and discuss implications for understanding clinical measurements of growth rate.

We present a composite, compartmental, multi-clock port-Hamiltonian model of cell dynamics learned by a graph-neural-network surrogate. The state pairs abundance deviation qj, the quantity omics assays measure, with oscillatory phasors derived only for pools a rhythmicity gate certifies as periodic. The storage function decomposes over five functional compartments (core clock, redox, energy, signalling, biosynthesis), so passivity is certified compartment by compartment, and the model carries two mechanistically distinct clocks coupled through a zero-net-power signalling port, with the central dogma hard-wired and conserved moieties held as exact invariants. We evaluate it on a real mouse-liver tri-omic circadian dataset assembled from public repositories and report a deliberately mixed verdict. The trained model is passive ([Formula], no violations over three seeds), forecasts held-out trajectory segments (RMSE 0.324 {+/-} 0.0004), and recovers withheld regulatory edges above a permuted null (AUROC 0.94 {+/-} 0.01). Its central prediction -- that cross-omic phase lags follow {Delta}{varphi} = arctan({omega}/kdeg) -- matches the aggregate transcript-to-protein lag measured independently (5.7 vs 4.9 h) but not the gene-to-gene variation, and the internal two-clock cascade is not scoreable on the available cross-cohort metabolome. The framework thus gives a falsifiable, thermodynamically-grounded account of cell dynamics with explicit limits.

Biomolecular condensates are thought to play a pivotal role in cellular organization by regulating biochemical reactants in space and time. Sustained molecular fluxes across condensate boundaries, together with the participation of phase-separating molecules in active chemical reactions such as ATP hydrolysis, call for a nonequilibrium description. Here, we propose a self-consistent framework in which diffusion-drift dynamics and chemical reactions are coupled through a conditional free energy, defined as the excess contribution to the chemical potential. Self-consistency is achieved by deriving this quantity from the same free-energy functional that governs molecular interactions and phase separation. We apply the framework to a minimal client-scaffold system and investigate how active chemical processes and phase separation interact at steady state. In doing so, our approach recovers the fundamental rules previously identified for the emergence of nonequilibrium steady-state fluxes. The model shows that active reactions involving the scaffold molecules can regulate the phase behavior of the condensate. Moreover, nonequilibrium steady-state fluxes are maximal near the boundary between the phase-separated and homogeneous regimes, suggesting that condensates sustaining molecular transport may operate close to their stability threshold. In the same region, client fluxes are also enhanced, revealing an indirect coupling between scaffold activity and client transport. These results provide a baseline for developing more detailed theories of chemically active condensates.

Biomolecular condensates are dynamic, membrane-free compartments that continuously exchange molecules with their surroundings. The dwell time, defined as the time a molecule remains inside a condensate between entry and exit, determines how extensively the molecule can explore the dense phase and encounter potential binding partners or reaction sites, thereby modulating condensate function. Motivated by our single-molecule measurements of RNA dwell times, we developed an analytical theory to understand dwell-time distributions in biomolecular condensates. Our theory predicts that the dwell-time distributions generally exhibit an early-time power-law regime followed by a late-time exponential tail. The form of the distribution encodes the rate-limiting mechanism of molecular escape: dense-phase diffusion-limited transport feature a -1.5 power law with an exponential tail set by a diffusion timescale, whereas interfacial barrier-crossing-limited transport feature a -0.5 power law with a decay governed by a barrier-crossing timescale. These distinct signatures provide a direct readout of the physical processes that control molecular retention in condensates, with implications for both natural and synthetic condensates.

Balancing cellular loss in tissues requires fine balance of cell proliferation and differentiation. In differentiated tissues consisting of a single cell type, a mechanical regulation of proliferation has been proposed to underlie growth-control and homeostatic steady-states. Yet, how tissues containing different cell types with distinct proliferation rates, mechanical interactions, and spatial self-organization retain robust homeostasis of cell proportions remains poorly understood. Here, we combine particle-based mechanical models of proliferative tissues with a classical hierarchy of stem, progenitor, and differentiated cells, undergoing stochastic fate choices, and show that mechanical feedback alone is sufficient to stabilize populations. We derive analytically and computationally a phase diagram of possible stable states, in particular those maintained either via slow and rare stem cells with short-lived progenitors or no stem cells and long-lived progenitors. Our simulations uncover that mechanical control of growth is sufficient, in the absence of any codes of adhesion or extrinsic niche signals, to cause stable spatial structures, with small stem cell clusters forming and maintaining dynamical renewal units. Our results demonstrate how complex spatial structures can emerge in minimal stochastic and mechanical simulations with impact to understand the homeostasis of multi-cellular systems.

What drove nucleic acids (NA) to associate with proteins (PR) at the Origins of Life? We reason from polymer physics and the Central Dogma (CD) that the fitness value of cooperating through a division of labor - NA for replication fidelity and PR for functional fitness - is much higher than for either polymer alone. Our model shows a Pareto Front, where NA and PR can bootstrap each other to achieve autocatalytic cooperativity towards biology.