Physical Review Letters

Phototaxis of many species of green algae relies upon directional sensitivity of their membrane-bound photoreceptors, which arises from the presence of a pigmented "eyespot" behind them that blocks light passing through the cell body from reaching the photoreceptor. A decade ago it was discovered that the spherical cell body of the alga Chlamydomonas reinhardtii acts as a lens to concentrate incoming light, and that in "eyeless" mutants of Chlamydomonas the consequence of that focused light reaching the photoreceptor from behind is a reversal in the sign of phototaxis relative to the wild type behavior. We present a quantitative theory of this sign reversal by completing a recent simplified analysis of lensing [Yang, et al., Phys. Rev. E 113, 022401 (2026)] and incorporating it into an adaptive model for Chlamydomonas phototaxis. This model shows that phototactic dynamics in the presence of lensing is subtle because of the existence of internal light caustics when the cellular index of refraction exceeds that of water. During each period of cellular rotation about its body-fixed axis, the photoreceptor receives two competing signals: a relatively long, slowly-varying signal from the direct illumination, and a stronger, shorter, rapidly-varying lensed signal. The reversal of the sign of phototaxis is then a consequence of the dominance of the flagellar photoresponse to the signal with the higher time derivative. These features lead to a quantitative understanding of phototaxis sign reversal, including bistability in the direction choice, a prediction that can be tested in single-cell tracking studies of mutant phototaxis.

We investigate the target search process by proteins locating specific target sites along DNA - a phenomenon fundamental to biological functions such as gene regulation, transcription, replication, recombination, and gene-editing technologies. This process proceeds through a repetitive sequence of stochastic motions: consisting of one-dimensional (1D) sliding along the DNA contour interspersed with detachment and three-dimensional (3D) excursions in the bulk, and then reattachment to a random location on DNA. Recognizing this sequence of random events as analogous to the resetting processes widely studied in statistical physics, we employ a first-passage-renewal framework and derive general expressions for both the mean and fluctuations of the total search time. Our results are completely generic and do not depend on the detailed microscopic dynamics of either the 1D or 3D phases. Quite interestingly, we find that intermittent detachment can not only accelerate the mean search but can also regulate fluctuations around it. Our analysis reveals a universal fluctuation inequality that links the variability and mean of the sliding time to the mean excursion time, thereby identifying the fundamental conditions under which target search process becomes efficient. Notably, we find that broad distributions of sliding times emerge as a universal characteristic for optimal search efficiency--a feature emanating from the slow dynamics along the DNA. Using the facilitated diffusion mechanism as a representative example, we validate the generality of our results. These findings provide a unified theoretical framework connecting stochastic search, resetting dynamics, and biological efficiency, while also highlighting the crucial role of DNA structure such as its contour length in modulating search performance.

Biomolecular condensates are domains within cells with distinct compositions, held together by intermolecular cohesion. They are implicated in a variety of cellular processes, and in vitro studies have revealed the molecular driving forces that underly their condensation. However, in vitro condensates do not capture essential features of cellular condensates. In particular, enrichment of proteins, quantified by partition coefficients, is often exaggerated in these simplified systems. We show that the addition of free amino acids and other small molecules to model condensates can bring their partition coefficients within physiological range. In this limit, where there is low biochemical contrast between condensates and their surroundings, we observe striking changes to condensate behavior. Such low-contrast condensates exhibit large fluctuations in shape and composition and show enhanced sensitivity to changes in their environment. These behaviors reflect dramatic shifts to their material properties, including interfacial tension, rheology, and chemical susceptibilities. We note remarkable similarities in these effects across seemingly unrelated two-phase fluid systems. To explain these trends, we reformulate classic models of critical phenomena in terms of partition coefficients. This framework simplifies application of theory to experiments with near-critical fluids and suggests new experimental approaches for assessing condensate physiology in live cells.

Adhesion of spherical nanoparticles or virus-like particles to membranes can lead to membrane tubules in which linear chains of adhering particles are cooperatively wrapped by the membrane. This cooperative wrapping of spherical particles in tubules is energetically favourable compared to the individual wrapping of the particles because of a favourable interplay of bending and adhesion energies in the contact regions in which the membrane detaches from the particles, and because a particle in a tubule has two such contact regions in the membrane necks that connect the particle to the neighbouring particles, whereas an individually wrapped particle has only one contact region to the surrounding membrane. The energetic gain of cooperative wrapping strongly depends on the range of the particle-membrane adhesion potential, which determines the size of the contact regions. At sufficiently large adhesion energies for wrapping, the energy gain {Delta}E per particle is only weakly affected by the membrane tension{tau} as long as the characteristic length [Formula] of the membranes with bending rigidity{kappa} is clearly larger than the contact regions. For large particle adhesion energies at which the particles are fully wrapped, however, {Delta}E can be limited by the minimum possible radius of the membrane necks, depending on the adhesion potential range.

We introduce a mechanistic, nonlocal tumour-growth model designed specifically to capture explosive dynamics that are not adequately explained by standard logistic reaction-diffusion descriptions. The motivation is empirical: the universal scaling law reported in [1] provides compelling cross-sectional evidence of superlinear tumour activity versus tumour burden, but as a phenomenological relationship it does not by itself supply a dynamical mechanism, nor does it rigorously describe how explosive growth emerges, how fast it develops, or how spatial interactions and tissue boundaries influence it. Our model addresses this gap by incorporating nonlocal proliferative feedback--cells respond to a spatially aggregated neighbourhood signal--and a singular, Kawarada-type acceleration that produces "quenching": tumour density stays bounded while the proliferative drive becomes unbounded as the aggregated signal approaches a critical threshold. This offers a concrete mechanistic route to explosive escalation consistent with physical boundedness. We analyse the model under no-flux (Neumann) boundary conditions, appropriate for reflecting tissue interfaces. In the spatially homogeneous setting we prove finite-time onset of the explosive regime and obtain explicit rates for how rapidly it is approached. For spatially heterogeneous perturbations we derive a transparent spectral stability theory showing how the interaction kernel selects spatial scales and how the singular acceleration tightens stability margins as the explosive threshold is approached. These results provide interpretable links between nonlocal interaction structure, boundary effects, and the emergence of rapid growth. Finally, to connect mechanism to data in the spirit of [1], we embed the model in a Bayesian inference framework that treats the interaction kernel and the acceleration strength as unknown and learned from tumour-growth observations. This enables uncertainty-aware estimation of explosive onset times, escalation rates, and stability margins, while positioning the scaling law of [1] as an observable signature that our mechanistic model can explain and quantify rather than merely fit.

While the regulation of bacterial cell size is widely studied across generations, the stochastic nature of cell volume growth remains elusive within a cell cycle. Here, we investigate the fluctuations of cell volume growth and report a deviation from standard white-noise models: the random growth rate exhibits subdiffusive dynamics. Specifically, the mean square displacement of the growth-rate noise scales as {Delta}t with an anomalous exponent {approx} 0.27. This low exponent implies strong negative temporal correlations in growth rate noise on timescales of minutes, which are significantly faster than those of gene expression dynamics. We attribute this phenomenon to the physical mechanics of the cell wall. By modeling the peptidoglycan network as a complex viscoelastic material with power-law-distributed relaxation times, we successfully recapitulate the observed subdiffusive behavior. Our results suggest that the heterogeneous mechanical constraints of the peptidoglycan network, rather than biological regulatory programs,govern the short-timescale fluctuations of bacterial growth.

The interactions of droplets and filaments can lead to mutual deformations and complex combined behavior. Such interactions also occur within the cell, where biomolecular condensates, distinct liquid phases often composed of proteins, have been observed to structure and affect the organization of the cytoskeleton. In particular, biomolecular condensates have been shown to undergo characteristic deformations when cytoskeletal filaments are fully embedded within them. However, a full understanding of the underlying physical mechanisms is still missing. Here, we combine experiments with coarse-grained molecular dynamics simulations and analytical models to uncover the physical mechanisms that define emerging shapes of droplets containing filaments. We find that the surface tension of the liquid phase and the bending energy of the filament(s) suffice to accurately capture emerging shapes if the length of the filament is small compared to the liquid volume. As the volume fraction of filament(s) increases, wetting effects become increasingly important, setting physical constraints within which surface and bending energies compete to define the droplet shapes. We find that mutual deformations of condensate and filament extend accessible shapes beyond classical stability considerations, leading to structuring and entrapment of contained filaments. Shape deformations may further affect ripening dynamics that favor certain geometries. Our findings provide a physical framework for a better understanding of the possible roles of biomolecular condensates in cytoskeletal organization.

Humans and animals are equipped with a rich innate repertoire of cognitive and behavioral skills [1-3]. Yet, the developmental programs that establish the underlying neural structures are unknown. During early development, neural connectivity is shaped by molecular axon guidance and cell adhesion programs that connect neurons based on the affinity between presynaptic receptors and postsynaptic ligands [4, 5]. Here, we show how such chemoaffinity-based connectivity rules can also establish innate cognitive functions and motor programs by structuring recurrent neuronal networks prior to experience. Different networks develop depending on the statistics of receptor and ligand expression. We illustrate this mechanism in computational models of chemoaffinity-based development that establish i) continuous attractor networks for path integration [6] with a toroidal grid cell topology [7], ii) networks with an exponentially large number of discrete attractors and sequences [8] as categorical, hierarchical, or temporal priors [9, 10], and iii) networks for arbitrary innate motor trajectories. Hence, chemoaffinity may shape not only the anatomical organization of the brain but also its innate cognitive and motor functions.

Mechanical adaptation underlies mechanical homeostasis by allowing living systems to restore characteristic mechanical variables under sustained perturbations. Across biological scales, turnover-mediated remodeling enables mechanical adaptation by continuously renewing internal structures under load. Despite extensive progress in this field, it remains to be established what closed-loop mathematical structure of mechanics-turnover coupling is sufficient to guarantee homeostasis and how the characteristic adaptation timescale emerges from this coupling. Here, we identify the minimal mathematical structure of closed-loop mechanics-turnover coupling, providing a unifying description of mechanically adaptive remodeling across scales. We derive an analytical expression for the adaptation timescale as a function of the coupling between internal mechanical parameters and turnover kinetics, enabling direct cross-system comparison. To isolate this structure, we formulate a dynamical model linking mechanics and turnover, and establish conditions under which the closed-loop dynamics exhibit integral action. Specifically, our model describes how deviations in the mechanical state modulate the turnover of an internal structural state, and the renewed structure feeds back onto mechanics in a negative-feedback direction, driving recovery toward a reference state. We define systems satisfying this structure as Feedback Adaptive Turnover-mediated Environment-Dependent (FATED) systems. As an experimental example, we formulate mechanical adaptation in terms of mechanically regulated actin turnover. With the generalization of this architecture, we evaluate cross-system consistency by comparing reported adaptation and turnover timescales across representative remodeling systems.

A key component of intraneuronal communication is the modulation of postsynaptic firing frequencies by stochastic transmitter release from presynaptic neurons. The time interval between successive postsynaptic firings is called the inter-spike interval (ISI), and understanding its statistics is integral to neural information processing. We start with a model of an excitatory chemical synapse with postsynaptic neuron firing governed as per a classical integrate-and-fire model. Using a first-passage time framework, we derive exact analytical results for the ISI statistical moments, revealing parameter regimes driving precision in postsynaptic action potential timing. Next, we extended this analysis to include both an excitatory and an inhibitory presynaptic connection onto the same postsynaptic neuron. We consider both a fixed postsynaptic-firing threshold and a threshold that adapts based on the postsynaptic membrane potential history. Our analysis shows that the latter adaptive threshold can result in scenarios where increasing the inhibitory input frequency increases the postsynaptic firing frequency. Moreover, we characterize parameter regimes where ISI noise is hypo-exponential or hyperexponential based on its coefficient of variation being less than or higher than one, respectively.

Micro-calorimetry offers significant potential as a quantitative method for studying the structure and function of biological systems, for instance, by probing the excess heat released by cellular or sub-cellular structures, isothermal or not, when external parameters change. We present the conceptual framework of nonequilibrium calorimetry, and as illustrations, we compute the heat capacity of biophysical models with few degrees of freedom related to ciliar motion (rowing model) and molecular motor motion (flashing ratchets). Our quantitative predictions reveal intriguing dependencies of the (nonequilibrium) heat capacity as a function of relevant biophysical parameters, which can even take negative values as a result of biological activity.

We propose a novel three-compartment heuristic model that recasts gastric cancer metastasis into a framework of non-equilibrium thermodynamics and nonlinear dynamics. The system, encompassing primary, hepatic, and peritoneal tumor populations, exhibits a well-defined route to chaos: as immune surveillance weakens, the dynamics undergo a supercritical Andronov-Hopf bifurcation, giving rise to a limit cycle, followed by a Shilnikov-type saddle-foci bifurcation cascade leading to chaotic attractors. Our central finding is the introduction of a dissipation function, {Psi}, constructed via a sensitivity-weighted, two-factor ansatz that integrates metabolic flux and dynamical influence. This spatially coarse-grained measure captures the systems thermodynamic robustness. The analysis reveals a dynamical phase transition: while tumor aggressiveness peaks in the pre-metastatic limit-cycle regime, {Psi} emerges as the definitive marker of the chaotic, treatment-resistant metastatic state, quantifying a sharp increase in systemic robustness that correlates decisively with advanced clinical stages (TNM III-IV). Consequently, this work provides a predictive framework grounded in the physics of metastasis, demonstrating that {Psi} not only diagnoses but also defines the primary therapeutic target: the underlying thermodynamic robustness of the metastatic system. Thus, effective intervention must shift from merely reducing tumor mass to strategically destabilizing this robust dissipative structure, thereby preventing recurrence. PACS: 05.45.-a; 87.18.-h; 87.19.xj; 05.70.Ln HighlightsO_LIA novel three-compartment heuristic model reveals phase transitions and chaotic dynamics in gastric cancer metastasis. C_LIO_LIThe dissipation function {Psi} emerges as a quantitative thermodynamic metric of systemic robustness in the metastatic regime. C_LIO_LIDecreasing immune surveillance triggers biological phase transitions towards metastatic disease. C_LIO_LIThe framework integrates nonlinear dynamics with TNM staging, identifying the dissipation function {Psi} as a therapeutic target to overcome metastatic recurrence. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/702339v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@1a605c7org.highwire.dtl.DTLVardef@c56e83org.highwire.dtl.DTLVardef@1da7fc8org.highwire.dtl.DTLVardef@1fb30ba_HPS_FORMAT_FIGEXP M_FIG C_FIG

We present a new formulation of the low-field effect (LFE) in spin-correlated radical pairs based on a zero-field singlet-triplet basis for the isotropic spin Hamiltonian. The aim is to provide a description that is both formally rigorous and mechanistically transparent, especially in the regime of weak magnetic fields such as the geomagnetic field. For the standard model radical pair containing a single spin [Formula] nucleus, we show that the conventional singlet-triplet basis obscures the distinct dynamical roles of the hyperfine and Zeeman interactions. In the zero-field S-T basis, by contrast, the mechanism separates cleanly: isotropic hyperfine coupling mixes singlet-doublet and triplet-doublet states, whereas the weak-field Zeeman interaction mixes triplet-quartet and triplet-doublet states without directly introducing an additional singlet-triplet coupling. The LFE is therefore revealed as a sequential process in which a weak field unlocks access from a triplet-only manifold to a singlet-accessible triplet manifold, from which hyperfine-driven singlet-triplet interconversion can occur. We then generalize this picture to radical pairs with arbitrary isotropic hyperfine structures by identifying maximal, interior, and, when present, minimal triplet-only manifolds in the zero-field spectrum. Finally, we introduce a practical blockwise dark-state recruitment measure for the triplet-only zero-field state space made singlet-accessible by a weak field, and show how this quantity depends on hyperfine symmetry, including the effects of equivalent nuclei. The resulting framework provides both a simple physical picture of the LFE and a general route to estimating its structural upper bound for arbitrary radical pairs.

Cerebrospinal fluid (CSF) circulates around and through the brain, supporting neural homeostasis by regulating the extracellular chemical environment. Yet the physical mechanisms governing CSF-driven solute transport remain poorly understood, limiting the design of diagnostic and therapeutic strategies targeting brain clearance and drug delivery. Pulsatile CSF flow in the cranial subarachnoid space (cSAS), is driven by cardiac, respiratory, and sleep-related vasomotion. Over longer timescales weaker steady flows, such as inertial steady streaming, Stokes drift, and production-drainage flow, may contribute to solute transport, but their role and relative importance remain unclear. Here, we develop a simplified two-dimensional model of CSF flow and solute transport in the cSAS using lubrication theory. Through multiple-timescale and asymptotic analyses, we derive a reduced long-time transport equation in which advection is governed by the Lagrangian mean velocity, incorporating steady streaming, production-drainage flow, and Stokes drift. Analysing three physiologically relevant case studies, we show that steady flows can substantially reshape concentration profiles, enhance dispersion, and alter clearance efficiency. Our results clarify the mechanisms underlying CSF-mediated transport, predict distinct regimes in humans and mice, and highlight the importance of subject-specific physiological parameters when interpreting contrast-agent and intrathecal drug-delivery studies.

Receptor-mediated ligand endocytosis is traditionally viewed as a negative feedback mechanism for signal attenuation. Here we show that ligand removal can paradoxically enhance directional information in autonomous cell-cell attraction. Many cell systems migrate toward one another in the absence of externally imposed gradients, implying that secretion, diffusion, and uptake must themselves generate usable directional cues. We develop a surface-resolved theory of a finite-sized detector exposed to a nearby source and derive analytical expressions for the steady-state ligand field. The resulting concentration profiles are governed by a single dimensionless Damkohler number that compares receptor-mediated endocytosis to diffusive ligand transport. Increasing ligand removal lowers extracellular ligand concentrations and reduces absolute concentration differences across the detector surface, but preferentially enhances relative surface anisotropy. Thus, destroying the signal can increase the usable information encoded in relative gradients. Incorporating nonlinear downstream processing reveals a tradeoff between contrast enhancement and signal depletion that yields a well-defined optimal endocytosis rate, in a regime consistent with experimentally measured receptor internalization kinetics. These results recast receptor-mediated endocytosis as an extracellular information-processing mechanism that reshapes self-generated gradients to enhance directional information.

Whether qualitative aspects of consciousness, or qualia in short, are equivalent across individuals is a foundational scientific question. Testing this is challenging because one cannot assume a shared mapping between stimuli and private experience (my "red" may be your "green") [1-3]. Previously, we proposed a structural characterization of qualia [4, 5] and the quantitative assessment of structural correspondences through an unsupervised alignment method [4, 6], which does not presuppose such correspondence. Using this approach, our previous work focused on identifying optimal mappings between relational structures of color qualia at the group level [4]. Given known perceptual diversities [7], however, it remained unknown whether any two individuals structures could be empirically aligned. Here, we resolve this by collecting 4,371 pairwise similarity ratings for 93 colors-from 11 individuals, enabling direct individual-to-individual alignment. We reveal two fundamental, coexisting features. First, we identified two clusters of individuals showing robust within-cluster alignment, corresponding to color-neurotypicals and atypicals. Second, we uncovered a continuous spectrum of diversity: some participants who showed normal color discrimination ability in terms of the Total Error Score (TES) on Farnsworth-Munsell 100 hue test nevertheless failed to align with either cluster, revealing idiosyncratic structures that defy simple categorization. Together, these findings suggest a novel structure-based taxonomy of divergent color qualia that complements conventional performance-based classification. Our method is generalizable to other sensory modalities, and opens a path to the scientific investigation of both shared and idiosyncratic qualitative aspects of consciousness.

Cell-cell junctions (CCJs) are dynamic biopolymeric systems essential for adherence of biological cells organizing into a tissue or organ. In vertebrate organisms, CCJs present in stable epithelial tissues are maintained primarily by cell-to-cell protein bridges made of an adhesion receptor E-cadherin. CCJs are destabilized during Epithelial (E) to mesenchymal (M) transformation (EMT), an essential step in cancer metastasis, where cells switch states and acquire migratory features. An essential trigger for EMT is a cadherin switch process from E-cadherins with N-cadherin, another cadherin isoform. EMT proceeds through several intermediate states referred to as hybrid E/M cells. These states are characterized by mixed levels of E- and N-cadherins at the junctions and exhibit versatile cancerous traits that are more aggressive than cancerous fully mesenchymal cells. As a result, many such states have emerged as key targets in cancer therapy. However, development of a therapeutic design to counter the hybrid E/M cells has been limited by the absence of a comprehensive understanding of the mechanics and dynamics of hybrid E/M states. Here, we develop a physical model of CCJs as a non-equilibrium system composed of a variable ratio of E- and N-cadherins, considered as coarse-grained molecules driven by ATP-powered machinery observed at CCJs. Our model predicts a robust measure of strength of junctions that captures previous experimental observations, and reveals a minimal mechanochemical landscape of hybrid E/M states. We show the emergence of several groups of CCJ states in this landscape with variable adhesion strengths, many of which resemble different hybrid E/M-characteristics observed experimentally. Finally, we identify that a difference in mechanosenstivity of the two cadherin isoforms towards cytoskeletal forces could be why the hybrid E/M states come into existence.

We develop a self-consistent free-energy framework in which membrane shape and osmotic pressure are determined simultaneously in a finite reservoir by minimizing bending elasticity and solute entropy. Solute conservation makes osmotic pressure a thermodynamic variable rather than an externally prescribed parameter, producing a nonlinear coupling between membrane mechanics and solvent entropy. This coupling modifies the classical stability condition for spherical vesicles: instability emerges from global free-energy competition rather than the linear Helfrich stability criterion. The resulting critical pressures differ by orders of magnitude from Helfrich predictions and agree with simulations for small and large unilamellar vesicles. The framework is relevant to cellular environments involving biomolecular condensate confinement as well as synthetic vesicles and the development of osmotic-pressure-driven encapsulation platforms.

In multi-cellular eukaryotic organisms, cell type and specific functional identity are defined by the epigenetic patterning of chemical modifications to DNA and chromatin that modulate the expression and silencing of specific genes. When a cell divides, histones containing important epigenetic marks are distributed between the two daughter strands leading to a temporary dilution of epigenetic information and cell identity. In this work we introduce a physics-based model of epigenetic memory that explains how cells restore and maintain H3K9me3 and H3K27me3 histone methylation patterning after cell division. We demonstrate that emergence and maintenance of the epigenetic program is driven by an evolved mechanism that makes use of the biophysics of polymers, phase condensates and enzymatic activity. We validate our model via genome-wide epigenetic time-course simulation and comparison to experimental epigenetic data from multiple donors, multiple cell types, and for multiple epigenetic marks. Finally, we use our model as a conceptual framework to understand cellular reprogramming by hypothesizing that these processes first contend with and later utilize somatic epigenetic maintenance programs.

Cellular adaptation is inherently nonstationary processes with complex stochastic dynamics1-5. Despite remarkable progress in quantitative biology6-11, a quantitative understanding of the cell adaptation dynamics in terms of the underlying cellular network remains elusive. Here, we present the next-generation chemical dynamics model and theory for cellular networks, providing an effective, quantitative description of the adaptive gene expression dynamics in living cells responding to external stimuli. Unlike conventional kinetics, chemical dynamics of cellular network modules are characterized by their reaction-time distributions, rather than by rate coefficients12. For a general model of cell signal transduction and adaptive gene expression, we derive exact analytical expressions for the time-dependent mean and variance of protein numbers produced in response to external stimuli, validated by accurate stochastic simulations. These results provide a unified, quantitative explanation of the stochastic responses of diverse E. coli genes to antibiotic stress and transcriptional induction. Our analysis reveals existence of a general quadratic relationship between the mean and variance of activation times across diverse genes. The gene activation process influences transient dynamics of downstream protein levels, but not their steady-state levels. In contrast, post-translational maturation process affects both transient dynamics and steady-state variability of mature protein levels. This finding indicates that the gene expression variability measured by fluorescent reporter proteins depends on the maturation time of the reporters. This work suggests a new direction for the development of digital twins of living cells.