Transcription factor concentrations provide signals to cells that allow them to regulate gene expression to make correct cell fate decisions. Calculations for noise bounds in gene regulation suggest that clustering or cooperative binding of transcription factors decreases signal-to-noise ratios at binding sites. However, clustering of transcription factor molecules around binding sites is frequently observed. We develop two complementary models for clustering transcription factors at binding site sensors that allow us to study information transfer from a signal, the morphogen Bicoid, to a variable relevant to development, namely, future cell fates. We find that weak cooperativity or clustering can allow for maximal information transfer, especially about the relevant variable. The timescale of measurement is crucial for predicting the optimal clustering strength: for short measurements, finite clustering is optimal because it allows for the implementation of a switch, while for long measurements, a range of weak clustering strengths allow binding site sensors to access near-maximal developmental information. Weak transcription factor clustering also helps binding site sensors achieve optimality consistent with the information bottleneck bound, which encodes an optimal trade-off between conveying relevant information and making costly measurements: changes in clustering in conjunction with changes in the binding energy can shift the binding site sensor along the optimal bound, and towards an optimal trade-off between obtaining information about the signal and obtaining relevant information.

In unconfined environments, bacterial motility patterns directly reflect the internal states of the cell. Bacteria operating a run-and-tumble behavioral program swim forward when in a “run” state, and they are stalled in place when in a reorienting “tumble” state. However, in natural environments, motility dynamics are a convolution of bacterial behavior and physical constraints. Recent investigations showed that Escherichia coli swimming through highly confined porous media exhibit extended periods of “trapping” punctuated by forward “hops,” suggesting a potential shift in motility strategy. We introduce a microfluidic device to systematically explore bacterial movement in a range of spatially structured environments, bridging the extremes of unconfined and highly confined conditions. We show that run-and-tumble and hop-and-trap are not distinct locomotive modes, but end points of a continuous spectrum of motility. We present the first unifying framework, “swim-and-stall”, to characterize this continuum of observed motility patterns. We demonstrate that a single control program underlies motility across all environments tested—that is, physical structure alone shapes changes in observed output. Our results establish a quantitative link between behavioral rules and environmental context, and show that E. coli can navigate dynamic, complex habitats without reprogramming their motility strategy. This robustness may explain the evolutionary persistence of run-and-tumble behavior in a diverse range of peritrichously flagellated bacteria and inform broader models of active transport in structured media.

In this work we show how a network inspired by a coarse-grained description of the cytoskeleton can learn—in a contrastive learning framework—from environmental perturbations if it is endowed with mechanosensitive proteins and motors. Our work is a proof of principle for how force-sensitive proteins and molecular motors can form the basis of a general strategy to learn in biological systems without neurons. Our work identifies a minimal biologically plausible learning mechanism and also explores its implications for commonly occurring phenomenology, such as adaptation and homeostasis.

Noninvasive, label-free imaging of freely moving Caenorhabditis elegans nematodes offers a powerful but technically demanding approach for behavioral and morphological studies. Key challenges arise from their microscopic size, rapid and intricate locomotion patterns, uniform soft-tissue composition with low intrinsic contrast, and the stringent requirements for imaging systems capable of simultaneously achieving high spatiotemporal resolution and exceptional soft-tissue sensitivity. To overcome these challenges, we employed ultrafast phase-contrast x-ray imaging (PCXI) and phase-contrast computed tomography (PCCT). PCXI was conducted at a detector acquisition speed of 500 fps, while PCCT leveraged a synchronized system comprising a fast-acquisition detector (4000 fps), a rapid-rotation stage (${1800}^{\circ }\text{/}\mathrm{s}$), and a precision electromechanical shutter (3-ms actuation). This configuration enabled complete three-dimensional (3D) tomographic acquisition (400 projections over ${180}^{\circ }$) in just 100 ms. Our integrated imaging platform delivers dual capabilities: PCXI enables real-time monitoring of multiple nematodes' natural behaviors, including dynamic irradiation avoidance responses, while PCCT offers comprehensive whole-organism morphometric analysis—quantifying body length, thickness, and volume—all under physiologically natural conditions without the need for anesthesia or contrast agent staining. By synergistically integrating ultrafast PCXI for dynamic two-dimensional functional analysis with PCCT for comprehensive 3D morphological characterization, this multimodal platform establishes an imaging paradigm that significantly advances the study of fast-moving microscopic organisms.

The spontaneous self-assembly of proteins and nucleic acids into biomolecular condensates has been shown to ubiquitously contribute to the functional compartmentalization of the cell. However, their liquid-to-solid transformation (i.e., aging) driven by interprotein $\beta$-sheet transitions represents a hallmark of multiple neurodegenerative disorders. We perform molecular dynamics simulations to elucidate the role of different biomolecules in regulating the aging kinetics of TDP-43, an RNA-binding protein linked to amyotrophic lateral sclerosis and frontotemporal dementia. We find that while arginine-rich peptides accelerate the nucleation of interprotein $\beta$-sheet structures, the inclusion of RNAs and the HSP70 chaperone slows down their emergence. Interestingly, we observe a correlation between the protein compactness—governed by the condensate composition—and aging kinetics. Moreover, we find that near-interfacial regions of TDP-43 condensates exhibit faster interprotein transitions than the core of the condensate. Together, our findings underscore the major role of client biomolecules in controlling the propensity of multicomponent condensates to form harmful solidlike states.

Epithelial tissues are driven out of thermodynamic equilibrium by internally generated forces, causing complex patterns of motion. Even when both the forces and motion are measurable, it is not yet possible to relate the two, because the sources of energy injection and dissipation are often unclear. Here, we study how energy is transferred by developing a method to measure the effective viscosity from the shear stresses and strain rates within an epithelial cell monolayer. Interestingly, there emerged multicellular regions in which the relationship between shear stress and shear strain rate was negatively proportional, indicating a negative effective viscosity. The negative effective viscosity occurred in regions wherein cell stresses were less efficient at producing tissue deformations compared to regions of positive effective viscosity. Regions of negative effective viscosity consistently exhibited greater cell speed and vorticity, and the cells had elevated metabolic activity, reflecting an increased energy demand in these cells. Our study shows that negative effective viscosity is a useful means of quantifying the flow of energy in living matter.

Cells use signaling pathways as windows into the environment to gather information, transduce it into their interior, and use it to drive behaviors. MAPK (ERK) is a highly conserved signaling pathway in eukaryotes, directing multiple fundamental cellular behaviors such as proliferation, migration, and differentiation, making it one of few central hubs in the signaling circuitry of cells. Despite this versatility of behaviors, population-level measurements have reported low information content (<1 bit) relayed through the ERK pathway, rendering the population barely able to distinguish the presence or absence of stimuli. Here, we contrast the information transmitted by a single cell and a population of cells. Using a combination of optogenetic experiments, data analysis based on information theory, and numerical simulations, we quantify the amount of information transduced from the receptor to ERK, from responses to singular, brief, and sparse input pulses. We show that single cells are indeed able to resolve between graded stimuli, yielding over 2 bits of information, however showing a large population heterogeneity.

Myxococcus xanthus is a soil-dwelling bacterium that lives in dense populations of millions of cells and displays an array of collective behaviors and macroscopic patterns in response to environmental conditions. The transitions between macroscopic patterns formed by this species are driven by changes in cell motility such as increasing Péclet number via increasing cell speed and reversal period. While cells are capable of passing contact-mediated signals that can trigger reversal events, we set out to study which aspects of M. xanthus collective behavior may be driven by simple individual cell behavior changes in the absence of direct chemical cell signaling. We do this through three-dimensional agent-based simulations with a bead-spring chain model. We find that when tuned to match single-cell properties, this model captures multicellular structures observed at both intermediate and global scales in M. xanthus experiments. Increasing cell speed and reversal period together (as cells do when they starve) maintains a constant overall nematic structure in the population, while significantly increasing the net flow into and out of topological defects, which drives an increased aggregation that leads to fruiting-body formation during starvation.

How do worms navigate their complex natural surroundings? Laboratory studies using nematodes such as Caenorhabditis elegans interrogate motility patterns as an indicator of their behavioral responses using liquid cultures or two-dimensional (2D) agar pads. However, C. elegans typically inhabit three-dimensional granular niches—such as soft soil and decomposed biological matter—with spatiotemporally varying viscoelastoplastic material properties that impose physical constraints on their motion. Here, by directly visualizing worm motility in transparent 3D granular matrices with tunable mechanical properties, we find that the propulsive speed of nematodes shows a nonmonotonic dependence on the yield stress of their microenvironment. Biophysical measurements reveal that this nonmonotonicity emerges as nematodes optimize for efficient navigation under higher confinement by matching their forward propulsive speed to the wave speed along their body. This regulation of locomotory behavior is purely dictated by the physical interaction of the nematode with its environment without involving soft-touch sensory neurons. Remarkably, our predictions from a slender body theory of undulatory motion in yield stress environments exactly capture the scaling behavior for both efficiency and mode of motion as obtained from the experimental data. Further, in a nondimensionalized phase space defined by propulsive efficiency and timescale of motility, we show that increasing confinement leads to a sharp gait transition from poorly efficient thrashing to highly efficient crawling. Our work thus establishes a biophysical paradigm wherein the physical interaction of worms with their mechanical microenvironment leads to a transition between distinct modes of undulatory motion, entirely as a consequence of 3D physical confinement.

A key process during animal morphogenesis is oriented tissue deformation, which is often driven by internally generated active stresses. Yet, such active oriented materials are prone to well-known instabilities, raising the question of how oriented tissue deformation can be robust during morphogenesis. Here we study under which conditions active oriented deformation can be stabilized by the concentration pattern of a signaling molecule, which is secreted by a localized source region, diffuses across the tissue, and degrades. Consistent with earlier results, we find that oriented tissue deformation is always unstable in the gradient-contractile case, i.e., when active stresses act to contract the tissue along the direction of the signaling gradient, and we show that this is true even in the limit of large diffusion. However, active deformationcan be stabilized in the gradient-extensile case, i.e., when active stresses act to extend the tissue along the direction of the signaling gradient. Specifically, we show that gradient-extensile systems can be stable when the tissue is already elongated in the direction of the gradient. We moreover point out the existence of a formerly unknown, additional instability of the tissue shape change. This instability results from the interplay of active tissue shear and signal diffusion, and it indicates that some additional feedback mechanism may be required to control the target tissue shape. Taken together, our theoretical results provide quantitative criteria for robust active tissue deformation and explain the lack of gradient-contractile systems in the biological literature, suggesting that the active matter instability acts as an evolutionary selection criterion.

Understanding how biomechanical reorganization governs key biological processes, such as morphogenesis and development, requires predictive insights into stress distributions and cellular behavior. While traditional approaches focused on cell motion as a response to stress, we demonstrate that Lagrangian coherent structures (LCSs)—robust attractors and repellers in cellular flows—precede and drive long-term intercellular stress reorganization, physically governed by the mechanical properties of intercellular junctions. We show that this hidden flow skeleton correlates strongly with biomechanical metrics, bridging microscopic cell motion with mesoscopic biomechanics. Specifically, attractors and repellers mark hotspots of compressive and tensile stress enrichment (exceeding tenfold), alongside heterogeneities in cell packing. Notably, these connections remain robust across varying strengths of cell-cell and cell-substrate force transmission. Finally, by linking the attracting regions in the flow skeleton to future cell extrusion spots, we establish a direct link between cell motion and biologically significant outcomes. Together, these findings establish a framework for using cell motion to independently infer biomechanical metrics and bridge the scale mismatch between cell motion and biomechanics, potentially offering a new route to interpret mechanosensitive biological processes directly from cell trajectories.

Biological systems are remarkably susceptible to relatively small temperature changes. The most obvious example is fever, when a modest rise in body temperature of only few Kelvin has strong effects on our immune system and how it fights pathogens. Another very important example is climate change, when even smaller temperature changes lead to dramatic shifts in ecosystems. Although it is generally accepted that the main effect of an increase in temperature is the acceleration of biochemical reactions according to the Arrhenius equation, it is not clear how it affects large biochemical networks with complicated architectures. For developmental systems such as fly and frog, it has been shown that the system response to temperature deviates in a characteristic manner from the linear Arrhenius plot of single reactions, but a rigorous explanation has not been given yet. Here we use a graph-theoretical interpretation of the mean first-passage times of a biochemical master equation to give a statistical description. We find that in the limit of large system size and if the network has a bias towards a target state, then the Arrhenius plot is generically quadratic, in excellent agreement with numerical simulations for large networks as well as with experimental data for developmental times in fly. We also discuss under which conditions this generic response can be violated, for example for linear chains, which have only one spanning tree.

The classic paradigms for learning and memory recall focus on the strengths of synaptic couplings and how these can be modulated to encode memories. In this work, using analytical theory and computational inference schemes that use specialized restricted Boltzmann machines, we show that the dynamical steady state accessed due to a class of nonequilibrium detailed balance breaking dynamics is in fact similar to those accessed after the operation of a classic unsupervised scheme for improving memory recall, Hebbian unlearning, or “dreaming.” Our work suggests how nonequilibrium dynamics can provide an alternative route for controlling the memory encoding and retrieval properties of a variety of synthetic (neuromorphic) and biological systems.

Embryogenesis integrates morphogenesis—coordinated cell movements—with morphogen patterning and cell differentiation. While largely studied independently, morphogenesis and patterning often unfold simultaneously in early embryos. Yet how cell movements affect morphogen transport and cells' exposure over time remains unclear, as most pattern formation models assume static tissues. Here we develop a theoretical framework for morphogen patterning in dynamic tissues, recasting advection-reaction-diffusion equations in the cells' moving reference frames. This framework (i) elucidates how morphogenesis mediates morphogen transport and compartmentalization: cell-cell diffusive transport is enhanced at multicellular flow attractors, while repellers act as barriers, affecting cell fate induction and bifurcations. (ii) It formalizes cell-cell signaling ranges in dynamic tissues, deconfounding morphogenetic movements to identify which cells could communicate via morphogens. (iii) It provides two new nondimensional numbers to assess when and where morphogenesis affects morphogen transport. We demonstrate this framework by analyzing classical patterning models with common morphogenetic motifs as well as experimental tissue flows. Our work rationalizes dynamic tissue patterning in development, constraining candidate patterning mechanisms and parameters using accessible cell motion data.

A hallmark of biological tissues, viewed as complex cellular materials, is the active generation of mechanical stresses by cellular processes, such as cell divisions. Each cellular event generates a force dipole that deforms the surrounding tissue. Therefore, a quantitative description of these force dipoles, and their consequences on tissue mechanics, is one of the central problems in understanding the overall tissue mechanics. In this work, we analyze previously published experimental data on fruit fly Drosophila melanogaster wing epithelia to quantitatively describe the deformation fields induced by a cell-scale force dipole. We find that the measured deformation field can be explained by a simple model of wing epithelium as a linearly elastic sheet. This allows us to infer the magnitude and dynamics of the mechanical forces generated by the cell divisions. In particular, we find that cell divisions exert a transient isotropic force dipole field, corresponding to the temporary localization of the cell nucleus to the tissue surface during the division, and traceless-symmetric force dipole field that remains detectable from the tissue strain field for up to about 3.5 h after the division. This is the timescale on which elastic strains are erased by other mechanical processes and therefore it corresponds to the tissue fluidization timescale. In summary, we have developed a method to infer force dipoles induced by cell divisions, by observing the strain field in the surrounding tissues. Using this method we quantitatively characterize mechanical forces generated during a cell division and their effects on the tissue mechanics.

We assess the potential of quantum computing to accelerate computation of central tasks in genomics, focusing on often-neglected theoretical limitations. We discuss state-of-the-art challenges of quantum search, optimization, and machine learning algorithms. Examining database search with Grover's algorithm, we show that the expected speedup vanishes under realistic assumptions. For combinatorial optimization prevalent in genomics, we discuss the limitations of theoretical complexity in practice and suggest carefully identifying problems genuinely suited for quantum acceleration. Given the competition from excellent classical approximate solvers, quantum computing could offer a speedup in the near future only for a specific subset of hard enough tasks in assembly, gene selection, and inference. These tasks need to be characterized by core optimization problems that are particularly challenging for classical methods while requiring relatively limited variables. We emphasize rigorous empirical validation through runtime scaling analysis to avoid misleading claims of quantum advantage. Finally, we discuss the problem of trainability and data-loading in quantum machine learning. This work advocates for a balanced perspective on quantum computing in genomics, guiding future research toward targeted applications and robust validation.

The hierarchical organization of cell identity, characterized by progenitor cells coexpressing multiple transcriptional programs, is a hallmark of animal development with well-documented experimental phenomenology. However, the mechanisms underlying its emergence remain unclear. Here we investigate the origin and regulation of progenitor states using a mechanistic mathematical framework based on transcription factor-enhancer interactions. This framework maps the regulation of cell identity genes to the dynamics of a continuous Modern Hopfield network, governed by an inverse-temperature-like parameter, $\beta$, representing epigenetic modifier activity, and an input field, $\mathbf{w}$, reflecting signaling activity. We propose that multilineage-primed progenitor states emerge from the interplay between enhancer competition for epigenetic readers and the activation of overlapping transcriptional programs. Mathematically, these progenitor states correspond to mixed attractor patterns in the network at intermediate $\beta$ values. We introduce an analytical approach to characterize the identity, stability, and basins of attraction of progenitor states, including their emergence and destabilization at specific $\beta$ thresholds. Applying this approach, we explain how signaling can instruct differentiation, reconstruct the complex blood progenitor landscape from terminal lineages, and identify potential mechanisms of differentiation dysregulation in cancer. We propose that nonspecific modulation of enhancer competition is a key regulatory axis underlying cell identity transitions.

The spatial organization of chromatin within the nucleus plays a crucial role in gene expression and genome function. However, the quantitative relationship between this organization and nuclear biochemical processes remains under debate. In this study, we present a graph-based generative model, bioSBM, designed to capture long-range chromatin interaction patterns from Hi-C data and, importantly, simultaneously link these patterns to biochemical features. Applying bioSBM to Hi-C maps of the GM12878 lymphoblastoid cell line, we identified a latent structure of chromatin interactions, revealing seven distinct communities that strongly align with known biological annotations. Additionally, we infer a linear transformation that maps biochemical observables, such as histone marks, to the parameters of the generative graph model, enabling accurate genome-wide predictions of chromatin contact maps on out-of-sample data, both within the same cell line and on the completely unseen HCT116 cell line under RAD21 depletion. These findings highlight bioSBM's potential as a powerful tool for elucidating the relationship between biochemistry and chromatin architecture and predicting long-range genome organization from independent biochemical data.

Collective information processing is fundamental in various biological systems, where the cooperation of multiple cells results in complex functions beyond individual capabilities. A distinctive example is collective exploration in which chemotactic cells locate sparsely distributed targets by sharing information of them through active generation and modulation of endogenous chemical cues. While the optimality of sensing target-derived gradients has been studied extensively in the context of single-cell information processing, the optimality of collective information processing that self-organizes endogenous cue gradient towards the targets remains underexplored. In this study, we employ the reinforcement learning (RL) framework to formalize the collective exploration, in which a population adaptively optimize its navigation toward targets through active cue modulation. Based on RL theory, we derive the optimal exploration dynamics of agents and identify their structural correspondence with the Keller-Segel model, the established phenomenological model of collective cellular dynamics. Our theory identifies an optimal coupling relationship between self-organized gradient generation and sensing and demonstrates that the optimal cue-generation strategy changes qualitatively depending on whether cells sense gradients linearly or logarithmically. The underlying RL structure is leveraged to compare the derived collective dynamics with single-agent searching dynamics, showing that distributed information processing by the population enables a fraction of agents to reach the target robustly. Our formulation provides a foundation for understanding collective exploration mediated by active modulation and sensing of cues.