PRX Life

A variety of connected systems, ranging from the cytoskeleton to human organizations, dynamically rearrange themselves in order to move through physical or abstract space. However, our understanding of how systems-level behaviors arise from local restructuring actions remains limited, necessitating comparison of real-world data to models that predict network structure and dynamics. To understand these systems, we study an accessible example, the branching slime mold Physarum polycephalum, by imaging the organism as it travels and extracting key fundamental quantities from its continuously remodeling tubular network. By using these quantities as input parameters to a traveling network model, we find that with no further fitting, the model quantitatively matches key emergent properties from P. polycephalum dynamics including path length, relocation time, and search efficiency at different spatial resolutions. These findings demonstrate how a traveling network model can capture P. polycephalum behaviors, highlighting the potential to use traveling networks more broadly for understanding and predicting connected dynamic systems by linking local measurements to emergent, system-wide behaviors.

Mechanical forces modulate receptor-ligand kinetics, sometimes producing the so-called catch bond: a tensile force strengthens the bond, whereas in most cases engagement time is reduced (a slip bond). Multiple phenomenological models reproduce catch-slip behavior between receptors and ligands, primarily by positing how the reaction's free-energy landscape changes under applied forces. In this paper, we show that distinct microscopic mechanisms often collapse into mathematically equivalent multi-exponential forms. This operational equivalence makes them difficult to distinguish (or even falsify) based solely on bond survival curves. We expose this mathematical equivalence and the validity regimes of these reductions and introduce \emph{c}ooperative \emph{f}orce-driven \emph{p}roof\emph{r}eading (CFPR), a network-level mechanism where pulling geometry enables contact-zone reorganization and additional nucleation of residue binding contributions, generating emergent catch-like responses from individually slip-like residue binding contributions. Our model can incorporate data from molecular dynamics simulations to predict, in advance, whether catch-slip will be present in a system and under what conditions, making it a predictive tool that complements experimental data.

Cephalopod skin exhibits a wide range of dynamic visual displays, including traveling waves, light/dark flashes, and flickering, all generated by muscle-regulated expansion and retraction of pigment-filled elastic sacs known as chromatophores. While central neural control is essential for high-level body-wide pattern coordination, experimental evidence from excised and denervated skin reveals that intricate spatiotemporal patterns can also emerge spontaneously in the skin. This raises questions about the respective roles of central versus peripheral control. To determine the simplest control necessary to generate such patterns, we developed ChromatoNet, a biophysical model of chromatophore arrays that includes mechanical interactions on 1- and 2-dimensional lattices with the excitability properties of radial muscles. With minimal external input, our modified Morris--Lecar model reproduces a rich repertoire of skin dynamics, including spontaneous flickering as well as traveling and spiral waves. Both spontaneous and stimulus-evoked waves emerge at an intrinsic resonant frequency, determined by model parameters. Propagation speeds remain stable across conditions and are consistent with the range observed in denervated skin preparations. The model generates diverse output states in response to local couplings and the time-dependence and shape of the input stimulation, with little top-down direction. The results provide a proof-of-principle that chromatophore/muscle dynamics with minimal central neural driving can produce rich dynamic patterns.

Growing experimental and computational evidence suggests that many condensates exhibit heterogeneous internal organization at mesoscopic length scales. How such mesoscale organization influences client selectivity and transport, however, remains poorly understood. Here, we use coarse-grained molecular dynamics simulations to compare homogeneous and heterogeneous sticker-spacer condensates with matched scaffold density, sticker fraction, and sticker-sticker interaction strength. We find that the two condensates exhibit markedly different client partitioning profiles and transport dynamics. In the heterogeneous condensate, clients sample distinct local microenvironments in a size-dependent manner, leading to pronounced differences in their transfer free energy compared with the homogeneous condensate. The heterogeneous internal organization also generates local environments with different mobilities and constraints, giving rise to transient subdiffusion and non-Gaussian displacement distributions in client motion. Our results identify mesoscale organization as an important physical factor controlling both molecular selectivity and transport in biomolecular condensates, with implications for the biochemical functions of both natural and engineered systems.

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.

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.

Allosteric communication in biomacromolecules is fundamentally governed by thermal fluctuation gradients, yet standard Gaussian Network Models (GNMs) treat atomic contacts as uniform, binary couplings without differentiating core constraints from solvent-exposed surface flexibility. Here, we present an analytical matrix framework that incorporates continuous distance-dependent weighting into the Kirchhoff matrix L. This formulation captures the steep steric constraints of hydrophobic core packing versus peripheral surface loops while strictly recovering the classic unweighted GNM as a high-temperature limit (T [->]{infty}). Using Schur complements of partitioned joint covariance matrices, we show that conditional fluctuation variances and higher-order entropy-transfer terms reduce analytically to exact ratios of submatrix determinants (covariance minors), eliminating the need for fitting parameters or molecular dynamics trajectories. Applied to KRAS (PDB: 6GOD), this framework constructs an integrated directional entropy-transfer asymmetry map. Order-1 minors (h(i) = Kii) establish a single-node fluctuation potential gradient, while order-2 minors (Rij) define pairwise channel bandwidths. Higher-order minors show multi-body spatial coupling: order-4 minors identify rigid core residues such as Phe156 as strategic interlobe relay hubs linking Lobe 1 and Lobe 2, and an order-3 triad cooperation index demonstrates that signal transmission from Switch II (Gln61) to Gly60 and Phe156 converges on a single, mechanically integrated allosteric sector. By deriving directional information flow directly from experimental atomic displacement parameters, this approach establishes a rigorous, computationally efficient framework for mapping allosteric networks across structural ensembles.

Inside living cells, many types of biomolecular condensates coexist and interact. Recent experiments have shown that new phases often form at the surface of preexisting condensates. However, the mechanisms for this interface-mediated phase transition remain elusive despite its importance to numerous biological processes. Here, we show that interface-mediated secondary phase separation is a universal pathway for forming a new phase in multicomponent solutions. Using Cahn-Hilliard simulations, we successfully generate puncta of client protein on the surface of scaffold condensates. Based on a quasistatic protocol, we theoretically demonstrate that an interfacial instability triggers new-phase formation and predict the amount of client protein needed for the instability to occur. Remarkably, our quasistatic theory successfully predicts the onset of secondary phase separation even if the client is rapidly added or both the client and scaffold are rapidly added. In the latter case, coarsening of the primary condensates drives secondary phase separation. Our work reveals that cells can exploit existing condensates to lower nucleation barriers of new phases, with important implications for protein aggregation.

Protein language models (PLMs) such as ESM-2 encode protein sequences as embeddings for downstream tasks. PLMs are trained on a masked learning objective that leverages evolutionary constraints. While interpretability studies of ESM-2 have focused on folded proteins, their behavior on intrinsically disordered proteins (IDPs), which constitute a substantial fraction of the human proteome and are implicated in numerous diseases, remains understudied. Because IDPs experience different types of evolutionary constraints on their amino acid sequences, we hypothesized that PLMs would behave differently on disordered versus folded regions. Here we show that ESM-2 exhibits reduced attention on disordered regions, yet still encodes meaningful biological signals. The model assigns heightened attention to disease-relevant residues even at high levels of disorder. Moreover, we show that both the radius of gyration and individual dynamic contact maps, key characteristics of IDPs, can be obtained from the model logits and embeddings. These findings suggest PLMs capture valuable information relevant to IDP biology despite their bias toward structured residues.

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.

We investigate the mechanical principles underlying fruiting body morphogenesis in Dictyostelium discoideum. Quantitative shape analysis based on the Young--Laplace law, together with AFM indentation measurements, indicate surface tension as the dominant tissue-scale force acting on the culminating fruiting body. Based on this observation, we construct a hydrodynamic phase-field model with tunable surface and interfacial tensions, and analyze its behavior numerically. Our results show that, once a stalk begins to form, the elevation of the cell mass arises naturally through a dewetting process. Through quantitative comparisons with experimental measurements, we identify the mechanical conditions required for detachment from the substrate and for establishment of the characteristic morphology of the culminating fruiting body. Together, our model analysis highlights the importance of stalk-tip elongation and tissue-scale surface and interfacial tensions in the construction of large-scale three-dimensional tissues.

Cell monolayers are active, orientationally ordered materials whose mechanics can depart from near-equilibrium behavior. Existing continuum theories often inherit assumptions from equilibrium liquid-crystal physics, motivating explicit nonequilibrium formulations. Here, we develop a minimal continuum model of two-dimensional monolayers based on odd mechanics, accounting for broken symmetries and nonequilibrium dynamics. We show that nematic order can support an odd viscous modulus generated by broken time-reversal symmetry and spatial anisotropy, without chirality. The model reproduces half-integer defect motion and stress profiles in Madin-Darby canine kidney (MDCK) monolayers, as well as defect-associated cell accumulation and depletion in neural progenitor and ovarian mesothelium systems. Finally, we estimate the viscous-moduli tensor from measured stress, velocity, and orientation fields in MDCK monolayers and identify a nonzero odd modulus. Our results show that odd mechanics provides a minimal framework for nonequilibrium cell monolayers, complementing conventional active-nematic theories.

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.

Biological transport networks range from tree-like to highly reticulated architectures, which have been proposed to reflect different balances between viscous dissipation and metabolic cost. This balance cannot currently be inferred from structure: available descriptors either discard edge width entirely or preserve it only as a hierarchical decomposition that has not been mapped to the dissipation--cost trade-off. Here we introduce the tree-to-cycle transition scale, a single number given by the radius of the thickest edge outside the maximum spanning tree, which we interpret as the highest cost a network accepts for redundancy. We compute it for networks adapting to spatially correlated load fluctuations, whose correlation length sets their position on the Pareto front. The transition scale decreases linearly with dissipation and increases linearly with metabolic cost. Its scatter is largest in the regime where some networks end up off the front, and adding a growth term removes the scatter but splits the correlation into two branches. A single quantity read off the static network architecture thus characterizes where a network sits on the Pareto front, and is sensitive to the trajectory the network took through the optimization landscape. This suggests a route to comparing observed networks, for example leaf venations across species or growth conditions, by the trade-off they realize.

Liquid-liquid phase separation (LLPS) widely organizes the cellular interior, yet how third-party macromolecules modulate LLPS remains a puzzle. While one would expect that macromolecules can either promote or suppress phase separation, empirical observations show that a small dosage of macromolecules virtually always promotes phase separation. To resolve this paradox, we study the linear response of phase separation to macromolecule addition. We prove that for macromolecules significantly larger than the solvent molecule, their addition essentially always promotes phase separation, regardless of how they interact with the scaffold protein. Crucially, in the biologically relevant scenario of dilute condensates, the linear responses, including the saturation volume fraction, scale proportionally to [1-v2(1+{chi}12)2], where v2 is the macromolecule size and {chi}12 is the scaffold-macromolecule interaction parameter. Importantly, this universal behavior extends to multicomponent solutions in which the effective interaction parameter is composition-dependent. Our work shifts macromolecular modulation of LLPS from empirical observation to predictable design.

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 [≤] N [≤] 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.

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.

Protein sequences are constrained not only by the need to fold into stable structures, but also by specific functional requirements imposed by natural selection. Yet predictions of how amino-acid changes affect proteins typically collapse these constraints into a single scalar score. Quantitatively separating these effects at scale remains an open challenge, with direct relevance spanning protein design to understanding the molecular mechanisms of disease. Inverse-folding (IF) models have emerged as fast, unsupervised predictors of folding energy changes ({Delta}{Delta}G), but because they learn statistical correspondences between structure and sequence, they can conflate conservation driven by function with conservation driven by stability. Here, we show that blending IF models with a physics-based coarse-grained potential improves global correlation with experimental {Delta}{Delta}G and, crucially, reduces IF model bias at functional sites. Applying the best-performing blend together with an evolutionary language model, we decompose each variants evolutionary cost into folding energy and dark energy, the latter capturing functional constraints beyond folding stability. With this decomposition, and without the need for supervision, we find that disease gain-of-function variants show a distinct functional signature from loss-of-function variants. In particular, we identify oncogenic drivers as largely preserving stability while exhibiting high dark energy, as opposed to tumor suppressors which are predominantly destabilized, paving the way to a mechanistic understanding of driver mutations in cancer. Together, these results provide a scalable framework for accurate {Delta}{Delta}G prediction and mechanistic disentanglement of variant effects.

Graph attention networks (GATs) are emerging as powerful aritficial intelligence (AI) tools for learning biomolecular dynamics, yet extracting mechanistic insight from learned attention remains challenging. Here, we present an interpretable AI approach that combines molecular dynamics prediction with mechanistic interpretation through attention-derived communication networks. We develop three edge-aware GAT models - Edge-Conditioned, Edge-Injected, and Edge-Gated - that differ in how edge information is incorporated during message passing. Relative to a standard GAT, our edge-aware models improve coordinate prediction while recovering complementary aspects of residue communication. Application to Chignolin and HIV-1 protease demonstrates their ability to characterize communication networks underlying protein folding, allostery, and mutation-induced functional remodeling. Analysis of the communication networks using graph-based descriptors of communication throughput, intensity, and relay revealed that the Edge-Conditioned model preferentially emphasizes communication hubs characterized by high communication throughput and intensity, the Edge-Injected model preferentially highlights high-intensity communication within functionally important regions, and the Edge-Gated model most clearly resolves long-range communication relay. Overall, this approach provides an interpretable AI strategy for uncovering the communication mechanisms that underlie folding, allostery, and long-range signal propagation in molecular machines, while also supporting AI-guided modulation of biomolecular function and rational protein engineering.

Mechanical interactions between cells are fundamental to tissue morphogenesis during development and regeneration. Computational methods that infer intercellular stresses from microscopy images of cell shapes offer a non-invasive alternative to experimental perturbation techniques, yet all existing approaches rely on explicit physical models. Here we present StressNET, a Graph Neural Network (GNN) that infers intercellular mechanical stresses directly from tissue geometry, without assuming any underlying physical model. We generated synthetic datasets to train and benchmark StressNET, and demonstrate that its predictions achieve state-of-the-art correlation with experimental stress proxies in zebrafish neuromasts and Xenopus embryos. Analysis of the network's latent space reveals that StressNET learns global organizational principles of mechanical stress distribution, beyond local cell-cell interactions. StressNET is open-source and provides pre-trained models that can be fine-tuned on in vivo data, making it a broadly adaptable tool for studying tissue mechanics across biological systems.