Communications Physics

The collective spatial phenomenon of complement protein opsonization on nanoparticle surfaces is a key component of the immune response to viruses, engineered nanoparticles, and diseased cells. Recent work showed this opsonization follows a sharp, percolation-like transition versus the spacing d between surface-bound attachment sites, leaving two open questions: 1) whether the transition exhibits hallmarks of true criticality, such as diverging susceptibility, and 2) whether it can be distinguished from an alternative first-order cooperative (Hill-type) process producing an equally sharp threshold without true criticality. Here, we resolve both questions using a hierarchical statistical-mechanics treatment spanning stochastic, mean-field, and spatial reaction-diffusion models. The variance of two order parameters, peak complement activity and activation lifetime, diverges near threshold and sharpens systematically with system size, the defining signature of a critical point rather than a smooth cooperative response. Extending the analysis across site spacing and intrinsic kinetic rate constants traces a two-dimensional locus of critical points with consistent critical exponents throughout, establishing a single, robust universality class. The mean-field dynamic exponent for activation lifetime agrees quantitatively with the exact value predicted for the general epidemic process. Finally, a reaction-diffusion model of the nanoparticle surface shows the critical locus is set by a diffusion-limited length scale, establishing complement percolation as a fundamentally transport-limited surface reaction. These results place complement activation within the percolation universality class and identify the physical parameters, diffusion, catalysis, and decay, that govern its critical threshold, with direct implications for rational design of complement-evading nanomaterials, immunology, and evolutionary biology.

Maintaining a prescribed composition in engineered microbial consortia is difficult because small fitness differences can drive competitive exclusion. We study a two-strain consortium in continuous culture and develop a feedback architecture that regulates composition by selectively slowing the fast strain as a function of the population ratio. At the population level, we derive an idealized ratio-feedback law with a tunable positive coexistence equilibrium. We then propose a biomolecular realization using orthogonal quorum sensing, an sRNA-based ratiometric controller, and a ppGpp-mediated growth actuator. Exploiting the separation between slow population growth and faster intracellular controller dynamics, we use singular perturbation theory to show that, for sufficiently fast controller dynamics, the full implementation model inherits the coexistence equilibrium and its local stability properties from the reduced model. Numerical simulations validate the reduction and show how weaker timescale separation or loss of the assumed molecular regime degrades performance.

Enzymatic reactions in biomolecular condensates are often assumed to be regulated through local enrichment of reactants. However, condensates also reshape molecular transport and reaction kinetics, making it unclear how phase separation controls catalysis in living cells. Here, we develop a quantitative theory of biomolecular catalysis in phase-separated systems and find that liquid condensates can act as tunable catalytic switches, transitioning between regimes of enhanced and suppressed enzymatic activity, exhibiting optimal responses at biologically relevant condensate sizes. We show that condensate-mediated catalysis cannot be understood from reactant enrichment alone, but instead emerges from the coupled interplay of molecular partitioning, diffusive transport, and phase-dependent reaction kinetics. The strongest regulatory effects occur under rapid interphase exchange, where the spatially heterogeneous catalytic network admits a system-level Michaelis-Menten description governed by system-averaged concentrations and reaction kinetics. Our framework predicts that micron-sized condensates can either enhance or suppress enzymatic activity by up to two orders of magnitude, and that optimal catalytic regulation can emerge at condensate sizes comparable to many biomolecular condensates. These results provide experimentally testable predictions for condensate-mediated catalysis and establish quantitative principles for understanding and engineering enzyme-catalysed reactions in biomolecular condensates.

Radiation-induced DNA double-strand breaks (DSBs) drive cellular mortality, mutagenesis, and severe evolutionary bottlenecks. While classical phenomenological models, such as the Linear-Quadratic (LQ) framework, reliably predict macroscopic population survival, they obscure the intrinsic single-cell stochasticity that governs critical rare events like tumor recurrence or the emergence of radioresistant persisters. To bridge this divide, we develop a mathematically exact stochastic differential equation (SDE) framework that models continuous DSB induction and repair as a Feller square-root process. By deriving exact closed-form expressions for the foci moments, we establish a highly efficient Maximum Likelihood Estimation (MLE) pipeline that circumvents computationally exhaustive Monte Carlo simulations, allowing the direct extraction of deterministic repair velocities and intrinsic molecular noise from empirical single-cell $\gamma$-H2AX data. Integrating this kinetic model with a cumulative damage hazard via the Feynman-Kac formalism, our framework seamlessly recovers the classic macroscopic LQ survival topology from microscopic first principles. Furthermore, systematic sensitivity analysis uncovers a fundamental evolutionary duality: while initial physical damage operates additively, ultimate cellular fate is driven by a nonlinear survival response governed by the trade-off between the damage hazard rate and intrinsic molecular noise strength. Crucially, we demonstrate that this molecular noise inherently enhances population survival. Governed by Jensen's inequality, stochastic variance acts as a non-genetic bet-hedging mechanism that buffers the population by favoring cells with transiently low damage loads. Ultimately, this exact stochastic framework bridges microscopic biophysics and macroscopic demographics, offering deep mechanistic insights into the evolutionary roots of radioresistance.

Structural network representations of metastatic dissemination typically focus on static topology without resolving transport dynamics, relaxation timescales, or steady-state behaviour. Here, we formulate a discrete Markovian transport model on a directed higher-order network with transition rates derived from qualitative clinical affinity classes. By constructing a non-Hermitian row-stochastic transfer operator, we characterise the relaxation dynamics through its spectral decomposition. The system exhibits a fast-mixing regime characterised by a spectral gap of {gamma} {approx} 0.67, corresponding to a characteristic relaxation timescale of {tau} {approx} 1.49 discrete steps, with the influence of the primary tumour origin progressively attenuated during dissemination. Convergence towards a non-equilibrium steady state (NESS) is accompanied by a reduction in Shannon entropy, concentrating probability mass within specific topological sinks. This spectral relaxation delineates two distinct dynamical regimes: early transient dissemination (n < {tau}), dominated by local organ-specific transition probabilities (organotropism), and the asymptotic regime (n > {tau}), determined increasingly by the global transport architecture of the network. Comparison with independent clinical and autopsy observations across 21 primary tumours and 23 target organs indicates that the predicted stationary distribution is consistent with the observed hierarchy of metastatic organ involvement.

Protocell communities can support programmable molecular nanonetworks, yet most demonstrations use broadcast diffusion or fixed sender-receiver circuits. We introduce PO_SCPLOWROTOC_SCPLOWNO_SCPLOWETC_SCPLOWSO_SCPLOWTACKC_SCPLOW, a network-layer abstraction in which a logical DNA-encoded packet carries a payload, a processing-address list, and an optional forwarding budget. The list determines where localized molecular services transform the packet, not its bidirectional diffusive trajectory. We formulate a finite-state reaction-transport model whose concentration dynamics and single-copy continuous-time Markov chain use the same generator. Under ideal specificity, positive rates, connected transport, no degradation, and sufficient budget, packet stages advance only in the encoded order and delivery occurs almost surely. All injected concentration is delivered asymptotically. Uniform first-order degradation makes delivery probability the Laplace transform of the lossless delivery-time distribution. A union-bound result separates endpoint delivery from route-faithful delivery under off-target processing. As an application, we develop cancellation-based strict-majority aggregation on rooted protocell trees. Conservation of token imbalance proves asymptotic correctness and yields a finite-time certificate. With one initial token per node, outside-root mass below one guarantees the correct root sign. Direct matrix-exponential calculations show sequential processing, branching addressability, route-length attenuation, and bounded forwarding work. A 16-condition finite-copy benchmark with 20,000 trajectories per condition shows that off-target reactions can increase endpoint arrival while decreasing route-faithful delivery. Adaptive ordinary differential equation simulations on trees up to 511 compartments show decision time increasing approximately with maximum tree depth and quantify bias from asymmetric loss. PO_SCPLOWROTOC_SCPLOWNO_SCPLOWETC_SCPLOWSO_SCPLOWTACKC_SCPLOW is therefore a formally analyzable molecular networking architecture and an experimentally testable blueprint. Sequence-resolved gates and chassis calibration remain future work.

Single-particle tracking is widely used to probe nanoscale dynamics in biological systems, yet most approaches rely exclusively on translational motion, overlooking rotational dynamics that offer complementary information about the local physical environment. Here, we present a simultaneous rotational and translational single-particle tracking approach using a vortex-engineered point spread function in a single detection channel. We validate this approach with static and freely diffusing nanorods, demonstrating accurate orientation recovery and quantitative agreement with theoretical predictions of rotational diffusion. Using a biomimetic lipid bilayer system, we show that translational and rotational diffusion exhibit distinct sensitivities to environmental perturbations, confirming that these two modalities capture complementary local environment information. Applying this framework to living HeLa cells, we show that combined translational and rotational diffusion signatures define distinct biophysical fingerprints of cytoplasmic and endocytic compartments and reveal compartment-specific responses to metabolic perturbation. Finally, time-resolved analysis of individual endocytic compartments uncovers dynamic changes in the local physical environment that are inaccessible to conventional translational tracking. By coupling translational and rotational readouts, this framework opens a new dimension for probing the physical organization and dynamics of living systems at the nanoscale.

Spatial gradients of signaling molecules pattern multicellular tissues with high precision. The canonical synthesis-diffusion-degradation (SDD) framework imposes a tradeoff on these gradients: ligand-receptor interactions that generate downstream signaling activity are also responsible for consuming the ligand. Correspondingly, at a fixed ligand synthesis rate, raising ligand-receptor affinity increases local signal strength at the expense of spatial range, and lowering it extends range at the expense of strength. Recent live-imaging measurements appear to violate this seemingly fundamental tradeoff, with low-affinity ligands of the epidermal growth factor receptor (EGFR) diffusing farther and driving spatially broader signaling activity compared to high-affinity ligands. Here we explain these observations with a model of multi-step ligand processing at the receptor, and show that the activity-range tradeoff is a consequence of receptor architecture rather than a physical necessity. When receptors process ligand through a multi-step phosphorylation cascade with kinetic-proofreading-like resetting, the states that generate activity decouple from those that consume ligand, and signaling activity and range increase together over a finite window of ligand residence time. This lets cells tune how far a signal travels independently of how strongly it acts through tuning signaling parameters. Realistic EGFR parameters place the low-affinity ligands in this window. Because multi-site phosphorylation and preferential degradation of the active receptor recur across multiple receptor families, kinetic proofreading may be a general strategy for controlling signaling range. Significance StatementCells coordinate by releasing molecules that bind receptors on neighboring cells. For a fixed supply, how strongly a signal acts and how far it spreads are locked together: tight binding gives a strong response but the molecule is captured near its source, while weak binding spreads farther but signals feebly. Yet recent imaging of epidermal growth factor receptor ligands shows the opposite: weak binders activate a broader field of cells. We show this limit reflects how receptors read the signal, not physics. A receptor that processes a bound molecule through several steps, and can release it partway, separates the states that signal from those that destroy it. Cells, and engineers, can then set a signals reach independently of its strength.

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.

Contact inhibition of proliferation (CIP) enables epithelial tissues to self-regulate growth and maintain tissue homeostasis. However, how cell-level mechanical contact, tissue-scale structural order, and proliferation kinetics interplay remains a fundamental open question in living matter physics. Here, we present a particle-based model of a confluent epithelial monolayer governed by overdamped dynamics, where individual cells interact via a two-dimensional hard core- soft shoulder potential. By comparing structural evolution during quasistatic densification with previously reported experimental division kinetics, we find that the dynamics of proliferation arrest mimics the onset of direct steric contacts between the hard cores of the shell. Identifying hard core contacts as the physical driver of CIP, we couple our mechanical model with a stochastic Monte Carlo division scheme in which the instantaneous division rate decreases to zero from an intrinsic value as the number of hard core contact increases to six from zero. We demonstrate that for high intrinsic division rates, the cellular densification outpaces mechanical relaxation. This kinetic mismatch drives premature hard-core contact formation, shifts the onset of jamming and contact inhibition to lower packing fractions, and induces increasingly disordered transient configurations before the tissue universally converges to a hexagonal close-packed limit. Our model's predicted division kinetics and structural order evolution are consistent with epithelial monolayer experiments, both reported and our own. This minimal physical framework links single-cell steric contact mechanics directly to tissue-scale growth regulation and structural evolution.

Many biological processes rely on mechanical forces, with protein molecules acting as key mediators. Understanding how proteins respond to mechanical stress is essential for conditions including cardiomyopathy and muscular dystrophy. Natural proteins such as dystrophin and utrophin are composed of heterogeneous folding domains with distinct mechanical properties; deciphering domain-level behavior provides insights into disease mechanisms and informs therapeutic strategies. Single-molecule force spectroscopy (SMFS) enables probing the mechanical properties of entire proteins, yet current approaches struggle to identify heterogeneous folding domains, particularly without prior knowledge. Here, we present the first automated framework to identify heterogeneous folding domains in SMFS data, applying both existing clustering methods and a novel physics-aware deep clustering architecture, LatentUnfold. LatentUnfold learns complementary latent representations from force magnitude and the force-extension physical relationship through dual autoencoders, jointly optimized for clustering assignments. We apply our framework to experimental SMFS data collected from a synthetic two-domain protein (ddFLN4-Titin I27) as well as natural protein constructs of dystrophin and utrophin, with Monte Carlo simulated datasets serving as controlled validation. For the synthetic protein, we recover mechanical properties consistent with previously reported values for each domain. For the natural proteins, we uncover two mechanically distinct domain populations - corresponding to the N-terminal domain and spectrin-like repeats - with differences in both unfolding force and contour length increase, and reveal different unfolding order between them for the first time. This work enables domain-level biological inference, overcoming prior limitations that relied on averaging and overlooked heterogeneity, thus advancing the understanding of mechanical behavior in protein unfolding.

Zero-dimensional chemical master equations, ordinary differential equations, and compartmental population models replace spatial stochastic biological systems by vectors of total counts or densities. This study asks when that projection is exact and whether information retained in spatial correlations can diagnose its practical failure. Exact Markov closure is characterized by an aggregate-rate lumpability condition: for every retained transition, the sum of microscopic transition rates must be constant over all spatial configurations with the same counts. Violations are connected to BBGKY-type correlation hierarchies and to mean-field, pair, and triplet closures. Conditional rate, finite-time predictive, memory, path-space, and correlation Kullback-Leibler risks quantify distinct losses. An exactly solvable two-compartment reaction separates structural non-closure from recovery of a well-mixed law under fast hidden mixing. Copy number and a spatial mixing-interaction ratio connect concentration, volume, diffusion, and reaction parameters to practical screening, including an Escherichia coli-scale example. The same projection logic is evaluated in controlled spatial susceptible-infectious-removed and predator-prey benchmarks. Across mixed and segregated initial conditions and four mobility regimes, pair-correlation risk was strongly associated with the error of the corresponding zero-dimensional ordinary differential equations (Spearman correlations 0.95 and 1.00; pooled 0.99). A nearest-neighbour exchange sensitivity analysis preserved the positive risk-error ranking. These benchmarks do not establish a universal threshold, but support correlation information as a transferable diagnostic for selecting among count, pair, higher-order, and explicit spatial descriptions.

Biological aging is accompanied by systematic changes in epigenetic modifications and chromatin organization. The reversal of the effects of aging, rejuvenation, is experimentally achieved by the transient induction of factors that modify these marks in cells and organisms. Here, we show that key features of rejuvenation experiments emerge from the biophysical interplay between dynamic epigenetic marks and the three-dimensional conformation of chromatin. Using a minimal field theory and molecular dynamics simulations, we show that the system responds in three distinct temporal regimes. The intermediary regime fulfills necessary conditions for successful rejuvenation. In this regime, the system spends time near a separatrix, allowing for high epigenetic plasticity, while memory retained in the chromatin conformation enables restoration of the original epigenetic correlations. Analysis of sequencing data further supports the predicted coupling between chromatin compaction and epigenetic correlations. Our results provide a physical explanation for how rejuvenation may remodel age-associated epigenetic states without irreversibly erasing cellular identity. We identify a general mechanism by which memory stored in a slow structural variable permits reversible remodeling of a faster internal state.

Economic stability and progress in modern technological societies depend on vigorous and independent public funding of science and engineering research. When peer review or funding decisions are perceived as politically directed, scientists, funding agencies, and the public react in coupled and conflicting ways. We describe an evolutionary game-theoretic model to analyze how perceived political interference in science funding affects the interrelated behaviors of scientists, funding agencies, and the public. The model simulates scientists choosing to refuse peer reviews and retaliate, agencies responding by adopting AI-assisted review and altering reviewer pay, and the public accepting or rejecting these AI systems. Through numerical simulations, five principal findings are identified: (1) Operational capacity and institutional legitimacy are governed by separate conditions and can fail independently. (2) Legitimacy of the process is bistable, meaning final states are determined by the publics acceptance of AI. (3) Since the career cost for researchers refusing to review is generally low, resistance/retaliation cascades can readily ignite, leading identical institutions to entirely opposite fates. (4) Increasing reviewer pay only stabilizes participation within a strict budget-solvency frontier, and emergency pay can paradoxically erode the legitimacy it aims to protect. (5) Finally, finite-population simulations reveal that baseline scenarios partition into either legitimacy recovery without capacity or joint failure, confirming that the fundamental separation of capacity and legitimacy outcomes is a dominant structural feature driven primarily by initial scientific resistance and politicization levels. This theoretical work quantifies issues for future work in science policy. SignificanceModern industrial nations rely on public funding of science, certified through expert peer reviews whose authority rest as much on perceived institutional independence as on processing capacity. We model what happens when review or funding decisions are seen as politically directed, and artificial intelligence (AI) substitutes for resistant human reviewers. Treating scientists, the public, and an adaptive funding agency as interacting populations, the model shows that operational capacity and institutional legitimacy obey separate dynamics and can fail independently: automation can sustain a review pipeline while its authority collapses, or legitimacy can recover while backlogs persist. Under finite populations, identical institutions can reach markedly different fates by chance alone, and how often chance favors survival depends on the noise process assumed, not on a fixed institutional probability. This framework identifies which conditions govern whether resistance ignites or whether legitimacy, once threatened, is repaired, as explicit future priorities for empirical calibration and policy design. Lay AbstractModern societies depend on public funding for scientific research, a system that only functions if the public believes the process is fair. Using evolutionary game theory, this mathematical work explores what happens when scientific peer review is viewed as politically compromised, and funding agencies turn to artificial intelligence (AI) to replace human experts who protest the system. The mathematical model reveals several critical warnings for policymakers: O_LIAutomation Does Not Equal Trust: Keeping grant money flowing and maintaining public trust are two separate problems. An agency can successfully use AI to process grants while the public completely loses faith in its decisions, creating a "zombie" institution. C_LIO_LIThe Threat of "Proposal Flooding": When scientists protest, in principle they refuse to undertake peer review, and can overwhelm the funding agency by submitting many proposals. This retaliatory flooding and refusal to review, within a limited budget, can outpace any AI systems ability to keep up, causing the entire pipeline to collapse. C_LIO_LIEmergency Pay Can Backfire: Trying to secure scientists cooperation by raising reviewer pay during a highly politicized crisis can make things worse, as the public may view this emergency pay as a "bribe," which rapidly destroys whatever institutional trust remains. C_LIO_LIEarly Action is Critical: Human behavior is unpredictable, small early differences in how people react can push identical institutions toward completely different fates. This model tracks individual scientists and members of the public to capture their behavior, which appears to be model-dependent, thus producing sensitivity to early demographic fluctuations and chance factors. Under the standard assumption, every simulated agency ended up with an unmanageable backlog; the only thing chance decided was whether public trust recovered. Under a coarser and more commonly used shortcut, roughly half the agencies appeared to recover fully, and a "zombie" agency that kept processing grants while trust collapsed appeared in about one in six. Policymakers therefore must urgently focus on building trust through transparent, explainable AI rollouts before public rejection becomes permanent. C_LI

Spontaneous curvature characterizes the propensity of a membrane to bend in a specific direction. It is therefore crucial in the multitude of cellular processes that involve membrane shape remodelling. Yet, experimentally quantifying the spontaneous curvature remains a significant challenge in complex biological membranes, as their heterogeneity causes ambiguities in spontaneous curvatures physical interpretation. Here, we introduce a general experiment-simulation framework to measure an effective spontaneous curvature using dual-direction tether pulling from cell-attached giant plasma membrane vesicles (GPMVs) and mesoscale simulations. For homogeneous membranes, the force difference between inward and outward pulls yields a tension-independent readout of spontaneous curvature. We show that this continuum observable can be generalized to the mean of the spontaneous curvature in a heterogeneous membrane, independent of the underlying microscopic spontaneous curvature distribution. Applied to HEK-derived GPMVs, a baseline negative spontaneous curvature of the plasma membrane is revealed. Sucrose treatment and extracellular addition of Annexin A5 systematically shift the effective spontaneous curvature, while mucin reporter overexpression does not measurably alter it under the conditions tested. We also measure the curvature imprint of individual fluorescently tagged proteins through a sorting index. Benchmarked with Annexin A5, our scheme recovers curvature imprints very similar to previous atomistic molecular dynamics simulations. Taken together this makes spontaneous curvature accessible as a directly measurable material property of native membranes and membrane-proteins, enabling quantitative studies of membrane remodelling across diverse cellular processes.

Viral diagnostics during early phases of infection are often limited by target scarcity and the deployment tempo. We significantly advance both quantitative accuracy and diagnostic throughput of viral agglutination assays with Immuno-Janus Particle (IJP) aggregation behavior that "flicker" stochastically with size-dependent statistics. By scrutinizing microscale blinking patterns of time series fluorescent videos, we decipher Brownian dynamics of individual IJP-Virus conjugates and IJP aggregates via windowed Ito stochastic analysis (termed the Culsans method). High-frequency rotational fluctuation is deconvolved from corrupting drifts caused by gravitational sedimentation and Brownian translational motion. This methodology enables a non-linear mapping of angular positions of detected IJPs and IJP aggregates to extract rotational diffusivity (Dr) (and subsequently overall construct size) with superior linearity (R2[&ge;]0.85). The aggregation behavior exhibits a maximum when the IJP and viral particle concentrations are equal. The virion-bridged IJP-IJP conjugates significantly shift the detectable hydrodynamic diameter in the Poisson limit of reduced virus concentration with respect to IJPs, pushing the limit of detection (LOD) to 103 - 104 virions per mL in untreated human plasma. This tunable platform offers a rapid, low-volume, and scalable alternative to lab-based RT-PCR, bridging the gap between virion sensitivity and field-readiness.

Biomolecules self-organize into membrane-less organelles known as condensates that compartmentalize essential biochemical processes, such as ribosome biogenesis in the nucleolus1-3. Molecular dynamics within condensates are governed by chemical preferences and interaction networks that can imbue nanoscale structure4-7. Such organization is typically inferred from ensemble-averaged measurements, such as scattering and electron microscopy, which reveal molecular arrangements8-14. However, the complexity of cells obscures the interpretability of these techniques, limiting insight into condensate internal structure and roles in macromolecular assembly and transport. Here, we develop an approach to quantify the average microenvironment surrounding specific proteins within condensates in live cells, using thermodynamic principles to interpret the partitioning of designed protein probes. Using this approach, we find that condensates in cells, including the nucleolus, stress granule, and nuclear pore, exhibit spatial inhomogeneity, aligning with emerging views of condensates as networked fluids5,6,15-18. Within the nucleolus, we link spatial inhomogeneity to ribosome biogenesis, which progressively loosens the average local meshwork, facilitating transport of assembled ribosomal subunits. Within the nuclear pore, we find that transporters experience a weaker local meshwork than nucleoporins, consistent with the selective phase model19,20. Together, our approach uncovers a distinct mode of biomolecular control arising from nanoscale structure, which we term microenvironment coupling, whereby internal interaction landscapes shape transport to enable regulation and proofreading.

Quantitative fluorescence imaging techniques such as fluorescence lifetime imaging microscopy and hyperspectral imaging infer molecular contrast from photons distributed across spatial pixels and temporal or spectral channels. In the few-photon regime, however, conventional pixel-wise analysis discards the spatial relationships imposed across neighboring pixels by the microscope point-spread function (PSF). Here we show that this spatially distributed information can be recovered without prior knowledge of emitter positions, spatial support or component assignments. We introduce SPOOL (Spatially Pooled Optical Observation Likelihood), a training-free Poisson inverse framework that jointly recovers source-space amplitudes and quantitative contrast by combining the PSF with temporal-decay or spectral-response dictionaries. For an isolated source, the attainable precision gain is governed by a dimensionless optical quantity: the PSF width expressed in detector pixels. The predicted gain therefore scales with optical sampling rather than with the physical origin of the contrast. The model predicts that lifetime-precision gain scales approximately linearly with the number of pixels spanning the PSF full width at half maximum, a scaling reproduced by Monte Carlo simulations. At one detected photon per foreground pixel, the reconstruction reduces lifetime dispersion sixfold in fluorescent-bead experiments and decreases the lifetime root-mean-square error relative to a high-photon reference from 1.19 to 0.45 ns in dual-labeled cells. The same framework transfers unchanged to hyperspectral imaging, recovering spectral contrast from generic emission bands without prior fluorophore spectra.

In linear polysomes, excluded-volume interactions among ribosomes can induce dimensional reduction of mRNA. Yet linear architectures allow steric stress to relax at open ends-- limiting how strongly crowding can remodel the mRNA's structure and dynamics. Using coarse-grained molecular-dynamics simulations, we compare circular and linear polysomes over a range of ribosome densities. Circular closure selects a predominantly quasi-planar global conformational ensemble, as indicated by a shape dimensionality dshape {approx} 2 over a range of ribosome densities. Crucially, circular topology and ribosome crowding act cooperatively to suppress structural fluctuations. While closure alone or linear crowding reduces relative global size fluctuations ({Delta}Rg/Rg) only to {approx} 0.16, their combined effect drives this fluctuation down to {approx} 0.07. Within this stabilized architecture, increasing ribosome density drives a distinct in-plane reorganization: the ring becomes more isotropic, global size fluctuations are strongly suppressed, and the scaling exponent increases toward {nu} [~=] 0.74 - 0.77, consistent with two-dimensional self-avoiding walk-like value over the accessible finite-size window, 1000 [&le;] N [&le;] 4969. Closure shortens the radius-of-gyration decorrelation time of circular polysomes by 40-fold relative to matched linear systems, reflecting the topological elimination of free ends. Within this closureselected ensemble, ribosome crowding further reduces the decorrelation time by up to 20% at the highest density. A fluctuation-informed crossover model links the density dependence of the global scaling exponent to inter-ribosomal subchain statistics. These results distinguish the geometric role of circular closure from the density-dependent steric response that it enables, revealing a confined yet dynamically responsive conformational regime for circular polysomes.

The dynamics of gene regulatory networks are governed by intrinsic noise, stemming from the random nature of biochemical reactions, and by extrinsic noise, arising from fluctuations in cellular components and environmental conditions. Together, these sources can compromise the reliability of predictive computational models if not properly accounted for, and capturing both effects within a single framework remains a non-trivial task in computational biology. In this work, we propose an uncertainty quantification framework that addresses these two contributions jointly: intrinsic stochasticity is described through a partial integro-differential equation (PIDE) for the protein probability density function, whereas extrinsic noise is represented as parametric uncertainty in the kinetic parameters. The propagation of the uncertainty is carried out via an intrusive polynomial chaos expansion (PCE), in which the PCE coefficients are obtained from a stochastic Galerkin projection of the PIDE, yielding a coupled deterministic system that is solved with standard numerical methods. We illustrate the approach on a positive autoregulatory gene network with one and two uncertain kinetic parameters. The proposed approach accurately reproduces the mean, variance, and full protein probability density function, including the bimodal distributions, at a substantially lower computational cost.