Communications Physics

We show that the p53-Mdm2 gene interaction can be understood as a form of antithetic feedback, but with mixed feedback loops. In particular, we show that this viewpoint sheds light on the emergence and robustness properties of p53's oscillatory behaviour, as observed experimentally.

Collective migration is often attributed to complex cell-cell communication, yet coordinated multicellular dynamics can also emerge in organisms lacking social interactions. Continuum descriptions successfully capture such behaviors but generally rely on phenomenological transport coefficients whose microscopic origin remains elusive. Here we show that these coefficients emerge from cell-cell collisions and can be independently determined from experiments. We investigate the aerotactic dispersal of confined colonies of the solitary amoeba Acanthamoeba castellanii, in which oxygen consumption continuously reshapes the local environment to generate self-organized guidance cues. Combining quantitative videomicroscopy, oxygen mapping with phosphorescent sensing films, and oxygen-controlled microfluidics, we directly measure both the oxygen landscape and the aerotactic response governing colony expansion. We then develop a Patlak-Keller-Segel (PKS) -type drift-diffusion model whose transport coefficients are entirely determined from independent experiments and microscopic-to-macroscopic modeling approach, without adjustable parameters. The model quantitatively reproduces the observed dynamics, including the coexistence of a fast ballistic front and a trailing transient accumulation band. We show that both diffusion and aerotactic transport are modulated by the same collision-controlled mechanism through density-dependent changes in cell persistence. More generally, our results establish transport coefficients as measurable emergent properties bridging microscopic cell dynamics and macroscopic collective behavior, providing a predictive framework for oxygen-guided migration in living active matter.

Cells use ATP-driven chaperones to extract clients from biomolecular condensates selectively, yet no closed-form criterion links molecular parameters to condensate quality control. We derive a minimal non-equilibrium sorting reactor model that reduces the reaction-diffusion dynamics to one algebraic equation. In the idealized low-load limit, a concentration-dependent partition coefficient produces a saddle-node bifurcation between functional clearance and pathological storage. The critical Damk\"ohler number is \(\mathrm{Da}_{0c}=e^{-2}\) and is independent of the feedback strength. Corrections shift the value but preserve the topology. The model predicts a Kramers-type functional lifetime and recovery costs that scale as \(R^3\). Critical slowing down near the bifurcation provides early-warning signatures for FCS and FRAP.

The back-and-forth (bidirectional) motion of Endosomes and Phagosomes along microtubules is fundamentally important for nutrient absorption, receptor recycling, pathogen degradation and many other downstream cellular pathways. Bidirectional motion requires opposing force from the kinesin and dynein motors, causing intermittent Tug-of-Wars (ToWs) followed by runs where winning motors drive their cargo until the next ToW. Dynein displays a counter-intuitive behaviour of catch bonding tightly to the microtubule against opposing force, but why such a response is needed remains unknown. Here we force dynein to catch-bond using an optical trap when dynein-teams are transporting phagosomes in-vitro and inside cells. An unexpected form of collective inactivity emerges, where larger dynein-teams remain inactive for longer after they have been catch-bonded. Kinesin exhibits a high probability of reversing the cargo during such periods of inactivity of dyneins. Therefore, the inactivity of dyneins allows cells to auto-regulate the effect of opposing molecular forces for the purpose of achieving bidirectional motion of endosomes/phagosomes. We propose that an inactivating conformational change is induced when dynein detaches against strain from its catch-bonded state. Collective inactivity follows because each inactive dynein exerts an entropic force on the other to delay the entire team from being activated. We capture the dynamics of this process through a statistical model of Entropically Constrained Reactivation, obtaining predictions in excellent agreement with experimental data. The emergent behaviour observed here possibly prevents a runaway dynein situation inside cells, its simple entropic origin suggesting broader relevance to catch-bonded receptor ligand interactions operating under conditions of cellular crowding.

Behavioral variability reflects differences in internal and external parameters and provides a substrate for natural selection. However, uncovering its structure is challenging because behavior evolves nonlinearly across multiple timescales. Our goal is to reconstruct phenotypic variation from finite behavioral observations without prescribing the scales at which individuals should be compared. To achieve this, we introduce a multiscale dissimilarity that compares dynamics across all statistically resolvable resolutions by constructing reduced transfer operators at progressively finer scales and weighting their differences by finite-sampling uncertainty. This dissimilarity defines a geometry of dynamical phenotypes, which we reconstruct using diffusion maps. We show that the resulting phenotypic coordinates locally follow the parameter directions to which dynamics are most sensitive. We validate our approach in a stochastic model and apply it to bacterial and larval zebrafish behavior. In bacteria, we identify run speed as a major component of inter-individual variability, associated with the chemotaxis protein CheB. In zebrafish, we find that prior prey experience reshapes behavioral dynamics and phenotypic variability, with paramecia experience concentrating fish in a prey-capture-associated region of phenotypic space. Together, these results provide a framework for identifying internal and environmental variables that structure dynamical phenotypes across scales.

Kinetic parameters in engineered biomolecular circuits are typically known only within intervals and are not precisely tunable; controller synthesis is therefore naturally posed over sets of gains rather than at a single nominal design. We formulate robust controller tuning under parametric plant uncertainty as a universally quantified set-inversion problem: given a plant-parameter box, compute the controller gains that stabilize every plant in the box. Robust stability is decided by Hurwitz sign conditions, which are bounded over the joint gain-parameter domain using validated range enclosures, yielding machine-checkable certificates of local exponential stability of the deterministic equilibrium. Numerical experiments show that the certified region depends strongly on the range enclosure. For an antithetic integral feedback (AIF) controller acting on a two-species gene-expression cascade, a tensor-product Bernstein enclosure certifies 83.2% of the design plane at bisection tolerance epsilon=0.05 with 324 terminal boxes, whereas four baseline enclosures certify between 50.9% and 58.9% on the same paving. For a fifth-order loop, the certified fraction increases from 34.4% to 73.8%, and the certified and excluded terminal boxes confine the robustly stabilizing area to a window of about 2.1 percentage points. Finally, for a refining variant of the algorithm in which the plant box is refined alongside the design box, the undecided area satisfies |U(epsilon)|=O(epsilon) under a non-degeneracy assumption on the stability boundary.

We develop a general geometrical framework for growth processes in which the growth rate depends exponentially on the number of active growth-supporting components. The framework separates the fraction of active components from the size of the accessible component space, allowing qualitatively different growth laws to arise from the same underlying construction. Bacterial population growth provides an example of a fixed-size component space controlled primarily by activation and deactivation, whereas examples such as human population growth or speciation provide an expanding component space in which interactions between individuals promote further growth. This motivates a Replicative geometrical law, dX/dt= X exp({beta} X^{gamma}), whose exponent characterizes the scaling of the effective growth-supporting component space and is tested against historical human-population regimes and eukaryotic lineage-through-time data. For innovation indicators such as patents and arXiv manuscript submissions, where an existing item need not itself reproduce, the same construction motivates a Direct Geometrical production law without the prefactor X. The framework therefore provides a common description of fixed and expanding component spaces while distinguishing Replicative from Direct Geometrical growth.

Cell density is a fundamental biophysical property of living cells, yet the dynamical architecture underlying density homeostasis remains unknown. Here, we reconstruct the geometry, dynamics, and control structure governing cellular density regulation from single-cell mass-volume measurements acquired during recovery from osmotic perturbation. We identify a persistent anisotropic attractor in mass-volume state space that permits coordinated density-preserving fluctuations while strongly suppressing density-changing deviations. Optimal transport analysis reveals directed recovery trajectories confined to this attractor, enabling inference of an effective density-restoring control law. Density deviations are associated with coordinated changes in biomass accumulation and volume expansion that together generate an effective density-restoring control law. Moreover, density error contains significant predictive information regarding future corrective behavior, revealing an information-rich component of cellular regulation. Together, these findings establish cellular density homeostasis as an emergent dynamical control process and demonstrate how attractor reconstruction, optimal transport, and information theory can be combined to uncover the organizing principles of biological homeostasis.

Nanopore sensors have transformed single-molecule analysis, enabling real-time detection of biomolecules with unprecedented resolution. Understanding and modelling noise in nanopore sensing is essential to unlocking their full analytical potential. Although mechanistic models of nanopore noise exist, a rigorous statistical framework that characterises noise structure across diverse experimental conditions is still lacking. Here, we present a comprehensive empirical analysis of nanopore noise using twelve diverse experimental conditions acquired with different nanopores, analytes, and electronic recording systems. By employing multiple, mutually reinforcing statistical methods, we establish that nanopore noise is well described by a composite fractional model comprising multiple fractional Gaussian noise and fractional Brownian motion components. This finding is supported by confirmation of Gaussianity, rigorous interpretation of second-order exponents, multifractal analysis, and evidence that transient deviations are attributable to deterministic or experimental artefacts rather than alternative stochastic mechanisms. We further resolve quasi-deterministic structures, such as baseline trends and structural breaks, and demonstrate their separability from the underlying stochastic profile. Collectively, these results provide the first formal statistical validation of a composite fractional model for nanopore noise and establish a principled basis for noise reconstruction, realistic simulation, and statistically grounded algorithm design for nanopore sensing applications. This study provides the foundation for the creation of high-fidelity performance evaluation datasets for signal processing, and the controlled generation and augmentation of data for machine learning while maintaining statistical fidelity to experimental conditions.

A foundational tenet of modern biophysics dictates that protein-solvent interactions and charge-transport dynamics operate strictly within microscopic, sub-nanometer boundaries where long-range coordination is natively suppressed by rapid room-temperature thermal dephasing. Here, we report an unexpected phenomenon that fundamentally challenges this paradigm: the long-distance, quantum-coherent communication between isolated protein samples in aqueous solution across macroscopic spatial separations. By mapping the distance-dependence of a weak, protein-emitted electromagnetic field on the response dynamics of an adjacent complex, we demonstrate that this inter-protein communication establishes a stable, intermolecular vibronic coupling operating across millimeter-scale distances up to at least 40 mm. This field interference induces a robust quantum coherence that lowers the local potential energy surface, transforming the coupled protein-solvent network into an un-attenuated, low-barrier energy transfer conduit. This stabilized pathway launches a topologically protected, non-dispersive soliton that exhibits exceptional phase stability against room-temperature thermal dephasing. These findings provide definitive empirical evidence of a sophisticated, field-driven quantum infrastructure embedded within a biological macromolecule. More explicitly, they reveal that a protein can possess an inherent capacity to preserve coherent wave propagation over macroscopic scales previously deemed impossible in room-temperature aqueous environments.

The emergence of treatment resistant mutants is one of the primary contributors to therapy failure in cancer. Evolution-based adaptive therapies aim to exploit the competition between sensitive and resistant cells to prolong therapeutic control. However, in the densely packed tissues of solid tumors, competition is not only determined by cell-intrinsic factors but also a result of local physical interactions between neighboring cells. The successful development of physics-informed adaptive therapies therefore hinges upon the bottom-up integration of tissue mechanics and experimentally grounded models of tumor evolution. Here, we show how population-level treatment response emerges from mechanical interactions between individual cells. Combining genetically tailored therapy-mimicry experiments in spatially structured 2D melanoma colonies with mechanistic, particle-based tissue simulations as digital twins in a real-to-sim-to-real framework, we reveal how treatment-induced changes in mechanical crowding shape a cascade of resistance expansion, spatial competitive escape, and consequent therapy failure. We then link these underlying physical mechanisms to population-level observables in an abstracted mathematical model for reinforcement-learning-based therapy optimization and a distilled treatment-decision-heuristic, which we test experimentally. Together, our results identify mechanical crowding and escape as governing principles of resistance-mediated therapy failure in solid tumours and provide a basis for mechanistically informed, evolution-based treatment strategies.

We investigate the dynamics of the spread of the infection due to a phage- during the range expansion of an E coli colony. We find a rich panoply of spatio-temporal dynamics at large colony-level length scales, arising due to the interplay of mechanical forces, growth and viral infection dynamics at smaller cellular length scales. Strikingly, the phages "surf" the front of the growing bacterial colony by hitchhiking on E coli that are advected due to the growth of bacteria in the colony, resulting in an anisotropic spread of the phages. We identify microscopic processes - of the phage release during lysis (occurring on short millisecond timescales at length scales close to that of a single bacterium) and local nematic alignment of the rod-like E coli bacteria (occurring on timescales comparable to the bacterial growth rate and the length scales of a few bacterial cells) - that enhance the advective effects driving the hitchhiking behavior. Combining our experiments with a mathematical model, we explain the multiple spatio-temporal dynamical regimes - from the coexistence of uninfected bacteria, resistant (lysogenic) cells and phages, to fixed points where the entire population turns resistant or remains uninfected. Our work broaches a new frontier in the investigation of eco-evolutionary processes arising from the mechanical and ecological forces due to the interactions between viruses and their hosts.

Non-exponential residence times give rise to history-dependent or memory effects in epidemic dynamics, yet their mathematical formulations remain fragmented. Here we develop a unified framework for a history-dependent SEIR model with general latent- and infectious-period distributions. We first establish the equivalence between the stage-age and renewal formulations and show that both admit a unified transition-flow representation. We then introduce the concept of a transition-flow multiplier, which provides a direct link between residence-time distributions and transition flows. When the multiplier is a weighted sum of shifted power functions, the model admits an exact representation in terms of Riemann-Liouville or tempered Riemann-Liouville fractional operators, providing a first-principles foundation for fractional epidemic models. Finally, using Euler-Lotka equations and stochastic-order arguments, we characterize how the latent- and infectious-period distributions shape epidemic growth and decay. A longer latent period slows both early growth and late decay, while, at fixed means, greater variability in the latent period accelerates early growth but slows late decay, whereas greater variability in the infectious period slows both.

Unveiling directional causality in complex, high-dimensional dynamical systems remains a fundamental challenge. Conventional nonparametric approaches, such as Convergent Cross Mapping, require computationally intensive surrogate tests and frequently yield false-positive detections under strong coupling due to generalized synchronization. Here, we present F-CODEC (Forest-weighted Conditional Dependence Coefficient), a forward-looking causal discovery framework integrating generalized random forests with adaptive rank conditioning. Nonparametrically conditioning on the target variable's intrinsic delay coordinates systematically neutralizes shared manifold representations, suppressing false positives without surrogate data. Furthermore, an analytical time-series threshold allows instant, rigorous hypothesis testing directly from raw observations. We demonstrate F-CODEC's resilience against generalized synchronization and high-dimensional noise across canonical chaotic benchmarks and multi-species network motifs. Applying F-CODEC to historical lynx-hare cycles resolves delayed trophic forcing, while in-situ IoT honeybee monitoring captures real-time microclimate-acoustic causal surges preceding colony-level behavioral events. F-CODEC establishes a practical, scalable, and surrogate-free tool for causal discovery in nonlinear dynamical systems.

Enzymes are central to biological function, catalyzing the chemical transformations that sustain metabolism, signalling, molecular transport, and cellular regulation. Understanding their catalytic activity requires connecting molecular structure and conformational dynamics to measurable reaction kinetics. Classical Michaelis--Menten kinetics describes turnover through a dominant catalytic pathway approaching a single saturation limit, whereas conformational-selection, induced-fit, allosteric, and dynamic-disorder models account for additional complexity arising from conformational exchange and heterogeneous catalytic states. Experimental studies further reveal concentration-dependent changes in pathway usage, cooperative or sigmoidal responses, transitions between distinct turnover regimes, and intermittent single-molecule activity. These behaviours are typically treated within separate kinetic descriptions, leaving their mechanistic relationship within a common physical picture incompletely resolved. Here, we formulate a minimal conformational free-energy landscape with interconverting enzyme states and competing catalytic routes to examine whether these apparently distinct kinetic signatures can arise from a shared physical mechanism. In this framework, substrate availability and conformational barriers redistribute catalytic flux between slower and faster pathways, thereby altering both pathway occupancy and turnover times. Stochastic Gillespie simulations together with deterministic mean-first-passage-time analysis show that different parameter regimes generate low- and high-turnover states, concentration-dependent kinetic crossovers, burst--halt intermittency consistent with single-molecule observations, and effectively Michaelis--Menten-like or sigmoidal allosteric-like responses. The same underlying landscape can therefore produce qualitatively different kinetic behaviours depending on how conformational exchange and substrate capture partition catalytic flux. It also explains how finite substrate windows can mask underlying bimodal behaviour. These results provide a physically interpretable framework linking conformational dynamics, pathway selection, and experimentally observed enzyme kinetics.

Dense, well-ordered material states of proteins form the amyloid fibers that are a hallmark of neurodegenerative disease in the brain. Beyond forming amyloid fibers, some of these proteins can also adopt other material states termed condensates which are initially liquid-like but evolve to a soft, glassy phase. Fiber growth requires a large supply of monomers and, thus, it is often speculated that fibers emerge from within a dense condensate as it ages and its microscopic dynamics slow into a glassy state. Here, we use the well-established model system Fused in Sarcoma (FUS) to directly observe, quantify and theoretically describe fiber growth and its interplay with condensates. We report the discovery that fibers grow overwhelmingly in the dilute phase surrounding the condensates while the condensates concurrently evolve to a glassy arrested solid. The resulting protein fibers and glassy condensates are both distinct solid-like phases that coexist but do not directly interconvert. Taken together, these findings reveal that there are two competitive aging pathways in FUS condensation that are linked through phase separation kinetics.

Neuronal dendrites form highly branched morphologies supported by spatially patterned microtubule arrays that are essential for intracellular transport and structural stability, yet how this organization is established and maintained remains unclear. Here we analyze microtubule behavior in Drosophila sensory neurons using live-cell imaging, deep learning-based analysis, and genetic perturbations. We find that microtubules are shorter and fewer in thin distal branches and progressively longer and more abundant in thicker proximal branches. Locally, polymerization slows in narrow branches due to soluble tubulin depletion, while catastrophe frequency increases near branch points upon cortical encounter. A physical model incorporating these morphology-dependent constraints reproduces both local microtubule dynamics and global organization. Inverting the textbook view that microtubule dynamics shape cell morphology, our results demonstrate that, on short timescales, cell morphology instead constrains microtubule dynamics, thereby determining large-scale cytoskeletal organization.

Force spectroscopy enables direct investigation of the interplay between free energy and transition kinetics in proteins and nucleic acids. However, multi-domain proteins often display multiple intermediate structures with diverse stabilities and kinetics, making equilibrium conditions difficult to achieve and out-of-equilibrium experiments challenging to interpret. We introduce a single-molecule force spectroscopy assay using periodic forces and high-resolution magnetic tweezers to bridge different stabilities, selectively enhance intermediate populations, and rigorously analyze multi-domain protein folding kinetics at quasi-equilibrium. By applying well-defined periodic square force functions with tunable steady states, we first validate our framework on the two-state folder {lambda}6-85, demonstrating that periodic force experiments can reproduce equilibrium kinetics. Extending this approach to the multi-domain protein mimic (Protein L), we show that tuning force, frequency, and amplitude enhances populations of distinct intermediate states. Finally, we dissect the entangled states of the hybrid two-domain protein {lambda}-L, where a 100-fold difference in domain kinetics precludes equilibrium analysis, by expanding from a two-state to a three-state periodic force protocol to direct intermediate populations. Our assay and analysis framework open new possibilities for deciphering complex multi-domain folds and their multi-dimensional folding pathways.

Extrachromosomal DNAs (ecDNAs) form highly heterogeneous condensates in cancer cells that drive oncogene overexpression, yet how structural variability coexists with stable gene regulation remains unclear. Here, we develop a minimal polymer physics model of MYC-harbouring COLO320-DM ecDNAs, where BRD4-like complexes bind and bridge cognate sites along ecDNA rings. Above a critical binder concentration, ecDNAs phase separate into condensates exhibiting diverse conformations because of their thermodynamic folding degeneracy. Despite this variability, condensates retain conserved interaction scaffolds that give rise to reproducible contact patterns, including in-trans associated domains (I-TADs), genomic regions enriched in intermolecular regulatory contacts between distinct ecDNAs. We find that condensate 3D architecture follows universal scaling relations with ecDNA copy number, n, remaining robust to model parameter changes. Regulatory contacts within I TADs increase linearly with n, yet they are one order of magnitude stronger than in size matched control regions outside I TADs, whereas their relative fluctuations are markedly suppressed as n increases. This scaling produces enhanced, low-noise regulatory environments for oncogenes embedded within I-TADs, such as PVT1-MYC fusions, whereas the canonical MYC copy, located outside, is less amplified as experimentally observed. Our findings reveal universal polymer physics principles underlying ecDNA condensate organization, offering a mechanistic basis for selective oncogene amplification and potential advantages in cancer progression.

Interpreting single-molecule force spectroscopy (SMFS) data from optical tweezers with steered molecular dynamics (SMD) is hindered by a severe timescale mismatch: SMD trajectories span nanoseconds to microseconds, while SMFS experiments unfold over milliseconds to seconds, forcing SMD to pull six to nine orders of magnitude faster than experiment and systematically distorting rupture forces, molecular pathways, and kinetic intermediates. We introduce force-probe coupled steered molecular dynamics (fp-SMD), which explicitly propagates the force probe (the optically trapped bead) alongside the biomolecule within a single classical Hamiltonian description of the coupled molecule-linker-bead system. A Predict-Correct Trajectory Propagation (PCTP) scheme resolves the resulting computational bottleneck by decoupling the fast molecular relaxation from the slow, macroscopic bead motion, using metadynamics and transition-state theory to identify the kinetically resolved molecular intermediate reached at each macroscopic time step. On the cholesterol-{beta}-cyclodextrin ({beta}-CD) host-guest complex, fp-SMD reproduces the experimental rupture-force histogram directly at matched conditions, without extrapolation from standard SMD. fp-SMD further resolves how applied force reshapes the molecular escape pathway itself--cholesterol dissociates via a high-angle route at low force and a direct, small-angle route at high force--providing, to our knowledge, the first direct structural evidence for force-induced pathway switching within small host-guest systems believed to underlie catch-slip bond behavior. fp-SMD/PCTP thus offers a general route to simulating force-probe-coupled biomolecular mechanics at experimentally relevant timescales.