The actin cortex is a dynamic biopolymer network that, despite constant architectural changes through the assembly and disassembly of filaments and crosslinkers, sustains a rigid homeostatic state, i.e., a steady state of positive elastic moduli, and exhibits an elastic memory that far outlasts the structural turnover timescale. We still lack simple models that maintain collective rigidity and elastic memory at long times compared to the turnover time. To address this, we develop two complementary elastic network models in which rigidity homeostasis and long-lived elastic memory with complete turnover emerge as a result of mechanosensitive dynamics of filaments (edges) and crosslinkers (nodes), respectively. Both models require the following minimal ingredients: (1) preferential disassembly of edges or nodes under small tension or force, (2) a small but nonzero rate of random disassembly, and (3) energy injection upon assembly. Our models are robust to variations in random disassembly rates, can recover from drastic structural disruption, and exhibit diffusion of nodes and edges in steady state, displaying representational drift similar to that found in neuronal activities and physical learning circuits. We propose that the cortex is an example of “tunable matter,” i.e., its mechanosensitive remodeling dynamics tune properties of its edges and nodes so that the cortex as a whole can maintain robust but flexible rigidity in fluctuating mechanical environments.

Living cells exhibit a complex organization comprising numerous compartments, among which are RNA- and protein-rich membraneless, liquidlike organelles known as biomolecular condensates. Energy-consuming processes regulate their formation and dissolution, with (de)phosphorylation by specific enzymes being among the most commonly involved reactions. By employing a model system consisting of a phosphorylatable peptide and homopolymeric RNA, and thanks to time-resolved ultra-small-angle x-ray scattering enabling us to probe a wide range of length and time scales, we elucidate how enzymatic activity remodels the growth kinetics and the internal structure of biomolecular condensates. Under a passive condition, we reveal a nucleation-driven coalescence mechanism maintained over four decades in time, similar to the coarsening of simple binary fluid mixtures. Coarse-grained molecular-dynamics simulations show that peptide-decorated RNA chains assembled immediately after mixing constitute the basic subunits. In contrast, actively formed condensates initially display a networklike internal structure, which gradually matures upon dephosphorylation prior to coalescence. Both pathways lead to a steady state, but fluorescence recovery after photobleaching indicates a peptide diffusivity that is twice as high in actively formed condensates consistent with their loosely packed internal structure. This study contributes to linking effectively cellular active processes such as post-translational modifications to the formation and dissolution pathways of membraneless organelles, impacting other fields including synthetic biology.

Affinity maturation is a stochastic evolutionary process allowing the adaptive immune system to produce B cells capable of recognizing antigenic molecules. One of the main factors influencing the quality of the maturation outcome, quantified by the affinity of the produced antibodies to the antigen, is the time course of antigen availability. In this paper, we develop a stochastic model for affinity maturation, calibrated against in vivo lineage dynamics and deep mutational scanning data, and address the following question: What is the best antigen-concentration protocol maximizing the probability of reaching a target affinity value at the end of the process? We introduce a path-integral formalism to identify the maturation trajectories of the rare clones ending at the desired target affinity, estimate their probabilities, and maximize them over the antigen-concentration temporal profile. The theoretical optimal concentration protocol is approximated by a discrete three-injection schedule; we show that such temporal modulation of selection pressure outperforms constant-dosage regimes.

The statistical physics view of microbial ecosystems often invokes random unstructured interactions, consistent with bacterial coexistence in very high-dimensional niche spaces. While this approach successfully reproduces broad community-level statistics, its central assumption of high-dimensional coexistence remains untested due to a lack of direct empirical estimates. Here we evaluate this assumption by inferring the effective niche dimensionality directly from abundance profile data using a joint species distribution model, a nonlinear low-rank matrix factorization approach. The inferred niche dimensionality, ${\eta }_D$, tracks ecological expectations: It correlates significantly with the dimensionality of the metabolic environment across paired 16S-metabolomics datasets, supporting the abundance-only inference. Applying this framework across nearly 200 human gut microbiome studies, we find that the inferred dimensionality is consistently low (median ${\eta }_D\approx 4$), markedly lower than median ${\eta }_D\approx 13$ predicted by diversity-matched disordered consumer-resource models, directly contradicting the high-dimensional assumption underlying current theoretical models. Low dimensionality was associated with stronger interspecies competition and reduced biodiversity, consistent with the long-standing macroecological predictions. Consumer-resource simulations reproduced these empirical relationships only when extrinsic resource availability trade-offs were imposed, with species-intrinsic metabolic trade-offs contributing an additional reduction in the effective dimensionality. Together, our results challenge the high-dimensional assumption underlying current physics-based descriptions of microbiomes and identify low-dimensional coexistence as an essential feature of host associated microbiomes.

It often is emphasized that gene expression is noisy. A seemingly contradictory view is that control mechanisms have been optimized to squeeze as much information as possible out of a limited number of molecules. Here we revisit these issues in a simple model where a single transcription factor (TF) controls a large number of target genes. We include only the physically required noise sources—random arrival of TFs at their targets and counting noise in the synthesis and degradation of messenger RNA (mRNA). If the cell has a limited total number of mRNA molecules, then the capacity to transmit information about TF concentration is maximized when these resources are distributed across the largest possible number of target genes. To realize this capacity the distribution of TF concentrations must be biased toward smaller values. Thus, in some limits, information transmission is optimized when individual expression levels are noisy. In addition, the dependence of information transmission on the parameters of this multigene system has a “sloppy” spectrum, so that optimal performance can coexist with substantial variability.

Cellular metabolism is globally regulated by various currency metabolites such as ATP, GTP, and NAD(P)H. These metabolites cycle between charged (high-energy) and uncharged (low-energy) states to mediate energy transfer. While distinct currency metabolites are associated with different metabolic functions, their charged and uncharged forms are generally interconverted through biochemical reactions such as $\mathrm{ATP}+\mathrm{GDP}\rightleftharpoons \mathrm{ADP}+\mathrm{GTP}$ and ${\mathrm{NADP}}^{+}+\mathrm{NADH}\rightleftharpoons \mathrm{NADPH}+{\mathrm{NAD}}^{+}$. Thus, their energetic states are generally coupled and influence each other, which would hinder the independent regulation of different currency metabolites. Despite extensive knowledge of the molecular biology of individual currency metabolites, it remains poorly understood how the coordination of various coupled currency metabolites shapes metabolic regulation, efficiency, and ultimately the evolution of organisms. Here, we present a minimal theoretical model of metabolic currency coupling and reveal a fundamental tradeoff relationship between metabolic controllability and thermodynamic cost: increasing the capacity to independently regulate multiple currency metabolites generally requires comparable abundances of those metabolites, which in turn incurs a higher entropy production rate. The tradeoff suggests that in complex environments, organisms evolutionarily favor comparably sized currency-metabolite pools to enhance metabolic controllability at the expense of a higher thermodynamic cost; conversely, in simple environments, organisms are expected to evolve more imbalanced currency pools to reduce heat dissipation. These considerations also offer a hypothesis regarding evolutionary trends in nucleotide-pool balance and genomic GC content.

How can protein-independent, side-by-side alignment of homologous double-stranded DNA (dsDNA) arise as an early step in genome repair and genetic exchange, and what determines their kinetics under confinement? Although recombination proteins are known to facilitate this process, experiments indicate that an initial side-by-side arrangement of homologs can occur even in their absence. Helical Coherence Theory (HCT) proposes that sequence-dependent distortions of the double helix lead to commensurate charge patterns between interacting dsDNAs, favoring homologous over nonhomologous alignment. However, HCT has largely been developed for straight, rigid dsDNA rods, an approximation limited to roughly one persistence length (${\ell }_B\approx 50$ nm), leaving the question open as to how recognition manifests in longer, fluctuating chains. Moreover, the dynamics and timescales of homolog searching under confinement, and the internal dynamics of the paired state, such as transient formation of local unpaired regions (“bubbles”) and end fraying, remain unexplored. Here we extend HCT to dsDNA chains of multiple persistence lengths under confinement using coarse-grained simulations with a custom HCT-based force field to promote our understanding of how homologous genes pair in vivo. We simulate two homologous chains in spherical cavities spanning 0.3 to 1 times the free-space gyration radius under two limiting ionic conditions, and use the ratio of confinement radius to free-space gyration radius as a control parameter. Pairing occurs on microsecond timescales, with intermediate confinement giving the most favorable balance between rapid encounter and avoidance of kinetically trapped states. This behavior reflects a crossover from diffusion-limited search under weak confinement to trapping-dominated dynamics under strong confinement. To resolve pairing dynamics, we develop and fit a kinetic theory to simulation trajectories, distinguishing rates of independent pairing modes underlying bubble formation and fraying. Together, these results establish a general physical principle: confinement acts as a control parameter that governs a crossover between diffusion-limited and kinetically trapped pairing regimes for homologous DNA chains, contributing to a minimal protein-free mechanism for homologous recognition on biologically relevant timescales.

Circuits in the brain commonly exhibit modular architectures that factorize complex tasks, resulting in the ability to compositionally generalize and reduce catastrophic forgetting. In contrast, artificial neural networks (ANNs) appear to mix all processing, because modular solutions are difficult to find as they are vanishing subspaces in the space of possible solutions. Here we draw inspiration from fault-tolerant computation and the Poisson-like firing of real neurons to show that activity-dependent neural noise, combined with nonlinear neural responses, drives the emergence of solutions that reflect an accurate understanding of modular tasks, corresponding to acquisition of a correct world model. We find that noise-driven modularization can be recapitulated by a deterministic regularizer that multiplicatively combines weights and activations, revealing rich phenomenology not captured in linear networks or by standard regularization methods. We show that modularized ANNs exhibit superior robustness to noise and adversarial attacks. When applied as an architectural constraint, a premodularized ANN acquires the ability to generalize and extrapolate well beyond training data, compared to ANNs without such inductive biases. Together, our work demonstrates a regularizer and architectures that could encourage modularity emergence to yield functional benefits.

Cytoplasm is a heterogeneous mixture containing many types of proteins that self-assemble into a wide variety of complexes. The accuracy and speed of cytoplasmic self-assembly is astonishing because it involves the correct identification of components shared among different structures, despite pervasive thermal fluctuations. Typical toy models of self-assembly are based on the specificity of binding energies among components. However, kinetics plays a key role in biological self-assembly, often catalyzed by a plethora of assembly factors. Building on this observation, we extend a previous heteropolymer growth model to describe the retrieval of two-dimensional structures via quasi-two-dimensional growth. We find that the self-assembly of structures in this model is subject to strong speed and encoding bottlenecks. Moreover, we show that these bottlenecks can be suppressed by increasing the connectivity of a small fraction of components. This mechanism of kinetically controlling a small number of critical binding events provides a simple explanation for the timely assembly of large heteromeric protein complexes and suggests a unifying principle for the role of assembly factors.

Biological cells contain actin structures of different types and sizes, but all of them are continuously turned over. Since they all compete for a shared pool of actin monomers, the question arises how they can coexist in these dynamic steady states. Recently, the coexistence of branched actin networks with different densities growing in a shared and finite pool of purified proteins has been demonstrated in a biomimetic bead assay. However, theoretical work in the context of organelle size regulation has mainly been focused on linear architectures, such as single filaments and bundles, and thus is not able to explain this observation. Here we show theoretically that the local depletion of actin monomers caused by the growth of a branched network naturally gives rise to a negative feedback loop between network density and growth rate and that this competition is captured by one central ordinary differential equation. A comprehensive bifurcation analysis shows that the theory leads to well-defined steady states even in the case of multiple networks sharing the same pool of monomers, without any need for specific molecular processes. Under increasing competition strength, coexistence is replaced by selection. We also show that our theory is in excellent agreement with spatiotemporal simulations, implemented in a finite element framework, and that local depletion even occurs in the presence of a large pool of nonpolymerizable actin. In summary, our work suggests that local monomer depletion is the decisive and universal factor controlling growth of branched actin networks.

The timing of reproduction is crucial for biological fitness. Reproductive delays carry an inherent demographic cost: an individual that takes longer to reproduce contributes later to population growth, a priori, without any compensating benefit. However, natural selection favors delayed reproduction under certain environmental conditions, a phenomenon that remains poorly understood. Here, we investigate how the environment can determine the evolution of particular life histories using a parsimonious delayed-logistic model. Individuals become reproductive only after a fixed lag whereas birth rates fluctuate under temporally correlated stochasticity. Numerical integration, analytical calculations and evolutionary simulations reveal that the joint effect of demographic memory and colored multiplicative noise generate a strongly nonmonotonic dependence of fitness on life history, with three distinct regimes of population performance: rapid reproduction maximizes linear growth but amplifies fluctuations and extinction risk; delayed reproduction buffers environmental variability, substantially increasing mean extinction times despite slower growth; strikingly, and central to our results, there is a broad band of maladaptive intermediate life histories that simultaneously reduce both growth and persistence, an effect that arises generically from the mismatch between reproductive timing and environmental autocorrelation. Our results show that reproductive timing is not merely a life-history parameter but an adaptive mechanism tuned to environmental time scales, and that “dangerous middle” maturation times can be inherently disfavored, with implications for understanding reproductive strategies across the tree of life.

Co-folding models like AlphaFold have revolutionized protein complex structure prediction, yet their reliance on multiple sequence alignments (MSAs) limits their applicability on challenging targets such as antibody-antigen complexes. An alternative approach, structure-based protein-protein docking, predicts the bound complex structure from the unbound monomer structures without requiring MSAs. In this work, we propose a method to adapt co-folding models for structure-based protein-protein docking by replacing their template module with a docking module, followed by training end-to-end with a flow-matching objective. We apply our method to AlphaFold-Multimer (AF-M) using the OpenFold implementation and transform it into a generative docking model, which we name AF2Dock. We evaluate AF2Dock and various baseline methods on the PINDER-AF2 benchmark and an antibody/nanobody test set. When using non-holo inputs, AF2Dock shows competitive performance compared to other structure-based docking methods and, in the case of nanobody complexes, outperforms all other docking methods tested here. Although AF2Dock underperforms co-folding AF-M and AF3 in success rates when using non-holo inputs, it produces orthogonal predictions and successfully identifies correct structures for targets where co-folding models fail. Ablation studies confirm that full-parameter fine-tuning of the AF-M components is critical for performance and reveal that, surprisingly, the inclusion of ESM embeddings can hinder success rates in certain cases such as nanobody complexes.

Biomolecular condensates govern essential cellular processes yet elude description by traditional equilibrium models. This roadmap, distilled from structured discussions at a workshop and reflecting the consensus of its participants, clarifies key concepts for researchers, funding bodies, and journals. After unifying terminology that often separates disciplines, we outline the core physics of condensate formation, review their biological roles, and identify outstanding challenges in nonequilibrium theory, multiscale simulation, and quantitative in-cell measurements. We close with a forward-looking outlook to guide coordinated efforts toward predictive, experimentally anchored understanding and control of biomolecular condensates.

Emergent self-organization is a hallmark of natural bacterial communities, whose spatial structures and dynamic gradients are shaped by the complex interplay among bacterial diversity, oxygen and its consumption, and motility and by hydrodynamic flows. Key aspects of the interplay between oxygen gradients and bacterial community spatial structure remain obscure. Here we aim to elucidate the role that oxygen plays in the self-organization of multispecies bacteria in the water column, focusing on oxytactic bioconvection suspensions of naturally coexisting aerobic bacteria and on self-organization near air-water interfaces. Combining microscopy, mapping of the oxygen field and controlled external oxygen levels, we show that species-specific oxygen affinities and consumption rates induce the formation of distinct bacterial layers near air-water interfaces, resulting in dynamic segregation during multispecies bioconvection, and play a key role in determining the nature of bioconvective patterns in single species suspensions. We further find that oxygen and bacterial fields are tightly coupled and fluctuate with similar spatiotemporal scales, giving rise to oxygen advection and a well-defined oxic-anoxic boundary. Together, our results illuminate the fundamental role that oxygen gradients play in multispecies bacterial active matter and its spatial self-organization, with implications for the formation and stability of ecological niches in aquatic and sedimentary environments.

Chromatin structure is a key determinant of gene expression and epigenetic inheritance. It relies on the post-translational modification of histones, the three-dimensional organization of nucleosomal DNA and interactions between chromatin components. To investigate interplays between these elements, we employed molecular dynamics simulations that integrated polymer modeling with enzymatic reactions on nucleosomes catalyzed by read-write enzymes. We explicitly modeled the diffusible heterochromatic nucleosome reader Swi6 (an HP1 homolog) that engages in homotypic and heterotypic interactions, undergoes and induces conformational changes upon binding to heterochromatic nucleosomes, and facilitates the targeted recruitment of histone deacetylases. The model revealed bistable epigenetic states with a silent state associated to the phase separation of heterochromatic domain condensates. Condensates were supported by Swi6 in a dosage-dependent manner that can be recapitulated in an analytic mean-field approximation. Our model of heterochromatic condensates further showed how a heterochromatic region can induce silencing of another region through spatial proximity in three dimensions. Moreover, extensions of the model suggested a mechanism for how negative feedback from antisilencing factors, here fission yeast Epe1, could confine the spreading of the heterochromatic state by acting on the surface of the condensates, altogether leading to epigenetically stable, spatially restricted chromatin states.

Characterizing the mechanical properties of single colloids is a central problem in soft matter physics. It also plays a key role in cell biology through biopolymer condensates, which function as membraneless compartments. Such systems can also malfunction, leading to the onset of a number of diseases, including many neurodegenerative diseases; the functional and pathological condensates are commonly differentiated by their mechanical signature. Comprehensive understanding of mechanical properties of biopolymer condensates has, however, remained challenging. In this study, we demonstrate that acoustic trapping can be used to probe the bulk modulus of biopolymer condensates in a contactless manner. We find that acoustic fields exert an acoustic radiation force on condensates, leading to their migration to a trapping point where acoustic potential energy is minimized. Furthermore, our results show that the positional fluctuation of a condensate undergoing Brownian motion in an acoustic potential well can be used as a probe for its bulk modulus. Using this framework, we infer changes in the bulk modulus of polyadenylic acid condensates in response to variations in ionic strength. Our results show that acoustic trapping opens up a path to characterize the bulk modulus of soft colloids in a noninvasive manner with applications in biology, materials science, and beyond.

Condensed biomolecular phases are fundamental compartments in cells and play intricate roles for cellular organization. Although often referred to as “membraneless organelles,” evidence has emerged that condensates associate with membranes through wetting interactions, which are physiologically important but not well understood. Here we report a novel method that provides a detailed description of how condensates interact with surfaces. Combining technically advantageous flat membranes and reconstituted condensates, we developed an accurate method that automatically analyzes the three-dimensional geometry of single, $\mu \mathrm{m}$-sized condensates acquired as multicondensate confocal image stacks using 96-well plates. We validated our computational routine by quantifying wetting of in vitro and in silico condensates for precisely controlled solution and supported lipid membrane biochemistries. Further, we applied our method to analyze the wetting of condensates formed from the Alzheimer disease-associated protein tau in its phosphorylated and nonphosphorylated forms. Our results demonstrate that both membrane charge and tau posttranslational modifications affect wetting and that our method allows building a systematic, quantitative wetting database, which catalogues conditions controlling wetting in multiple dimensions. We anticipate that upscaled in a high-throughput pipeline, our screening approach will efficiently support efforts to construct a comprehensive wetosome database, which will unravel the mechanisms underlying condensate-membrane interactions and, thus, decipher the molecular grammar governing wetting.

Chromatin mobility in living interphase nuclei provides a dynamical readout of genome regulation. From time-resolved trajectories of chromatin granules in primary acute lymphoblastic leukemia (ALL) samples (one B-ALL, one T-ALL), leukemic cell lines (U266, Jurkat), and healthy donor T lymphocytes, we resolve rare but reproducible transitions between extreme mobility states. We quantify these events with Brownian-bridge ensembles, i.e., trajectories conditioned on crossings between low- and high-mobility quartiles. Despite their low frequency, crossings reveal strong polarity: activation ($\mathrm{Q1}\to \mathrm{Q4}$) concentrates on a superlinear, ballistic-like ridge, whereas reconfinement ($\mathrm{Q4}\to \mathrm{Q1}$) returns more slowly toward confinement. The scarcity of extreme events is comparable in magnitude to reported bursting fractions, motivating future joint live-imaging tests that directly link mobility bridges to transcriptional state. These asymmetric pathways yield a compact dynamical signature of disrupted confinement and lineage-dependent nuclear deregulation in ALL, and generate testable predictions for cohort-level validation.

Evolution has shaped animal bodies, yet to what extent biomechanical systems impose constraints and provide opportunities across different behaviors remains unclear. In birds, quiet breathing operates at a resonance of the respiratory biomechanics, but song, a behavior thought to be shaped by strong sexual selection, requires much higher breathing rates. Combining physiological recordings with a nonlinear biomechanical model, we show in canaries (Serinus canaria) that song production drives the system into a nonlinear regime that broadens the frequency range of amplified responses. This enhancement encompasses the full range of syllabic rates, with an average magnification of $\sim 94\%$ of the theoretical maximum. Thus, song exploits a broadened regime of high amplification, extending biomechanical support beyond the narrow resonance observed during quiet breathing. Our results illustrate a shared optimization strategy across behavioral states, reveal a deep connection between neural and biomechanical dynamical parameters, and show that sexually selected displays may still rely on optimization strategies.

Investigations of jamming in cancer have produced numerous phase diagrams intending to map fluidity across the epithelial-to-mesenchymal transition (EMT). Here, we use coalescence of multicellular spheroids to examine and carefully probe these phase spaces. Small changes in cellular EMT status result in full traversal of the solid-to-fluid continuum. Variations in fluidity as measured by degree of spheroid coalescence are captured by two-dimensional assessment of static cellular properties including internuclear spacing and nuclear shape, which can be determined in individual noninteracting spheroids. We combine these quantities into an effective nuclear packing metric that depends on nuclear occupancy and nuclear elongation. We find that dense nuclear packing impedes spheroid coalescence and that lowering effective nuclear packing by altering nuclear shape fluidizes an otherwise solidlike system. Furthermore, we employ heterotypic spheroids to tune effective nuclear packing and coalescence behavior, thus introducing a novel method of fluidity phase space exploration. Together, these findings reveal that nuclear occupancy and morphology act as important indicators and determinants of fluidity.