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Spirostomumis a giant unicellular ciliate that contracts to a quarter of its body length in less than five milliseconds, achieving an order of magnitude higher fractional shortening rate than actomyosin-based systems. This ultrafast contraction is powered by myonemes, calcium-activated protein networks at the cortex whose biochemical mechanism remains unclear. We quantify changes in cortical microtubules, membrane ruffles, and the fishnet-like myoneme mesh during contraction, and develop multiscale models that connect local myoneme shortening to whole-cell shape change. Centrin and an Sfi1 homolog colocalize with the myoneme by immunofluorescence and localize to the myoneme by immunogold electron microscopy. Coarse-grained mesh simulations reproduce the measured deformations and show that fishnet geometry, together with volume conservation, leads to uniform contraction. Finally, we reconstitute aSpirostomumcentrin–Sfi1 repeat complex in vitro and measure calcium-dependent compaction and self-association, supporting a molecular basis for myoneme contractility. Together, these results underpin a multiscale model in which calcium-responsive centrin–Sfi1 structures are the central contractile element inSpirostomumand suggest design principles for fast, calcium-triggered, chemomechanical contractile networks that operate without actomyosin or ATP. 

Uncovering the rules governing the nonequilibrium dynamics of the membranes that define biological cells is of central importance to understanding the physics of living systems. We theoretically and computationally investigate the behavior of flexible quasispherical vesicles that exchange membrane constituents, internal volume, and heat with an external reservoir. The excess chemical potential and osmotic pressure difference imposed by the reservoir act as generalized thermodynamic driving forces that modulate vesicle morphology. We show that the renormalization of membrane mechanical properties by nonequilibrium driving gives rise to a morphological transition between a weakly driven regime, in which growing vesicles remain quasispherical, and a strongly driven regime, in which vesicles accommodate rapid membrane uptake by developing surface wrinkles. Additionally, we propose a minimal vesicle growth-shape law, derived using insights from stochastic thermodynamics, that robustly describes vesicle growth dynamics even in strongly driven, far-from-equilibrium regimes. 

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. 

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. 

Information is an important resource. Storing and retrieving information faithfully are huge challenges and many methods have been developed to understand the principles behind robust information processing. In this review, we focus on information storage and retrieval from the perspective of energetics, dynamics, and statistical mechanics. We first review the Hopfield model of associative memory, the classic energy-based model of memory. We then discuss generalizations and physical realizations of the Hopfield model. Finally, we highlight connections to energy-based neural networks used in deep learning. We hope this review inspires new directions along the lines of information storage and retrieval in physical systems. 

Pooled single-cell perturbation screens represent powerful experimental platforms for functional genomics, yet interpreting these rich datasets for meaningful biological conclusions remains challenging. Most current methods fall at one of two extremes: either opaque deep learning models that obscure biological meaning, or simplified frameworks that treat genes as isolated units. As such, these approaches overlook a crucial insight: gene co-fluctuations in unperturbed cellular states can be harnessed to model perturbation responses. Here we present CIPHER (Covariance Inference for Perturbation and High-dimensional Expression Response), a framework leveraging linear response theory from statistical physics to predict transcriptome-wide perturbation outcomes using gene co-fluctuations in unperturbed cells. We validated CIPHER on synthetic regulatory networks before applying it to 11 large-scale single-cell perturbation datasets covering 4,234 perturbations and over 1.36M cells. CIPHER robustly recapitulated genome-wide responses to single and double perturbations by exploiting baseline gene covariance structure. Importantly, eliminating gene-gene covariances, while retaining gene-intrinsic variances, reduced model performance by 11-fold, demonstrating the rich information stored within baseline fluctuation structures. Moreover, gene-gene correlations transferred successfully across independent experiments of the same cell type, revealing stereotypic fluctuation structures. Furthermore, CIPHER outperformed conventional differential expression metrics in identifying true perturbations while providing uncertainty-aware effect size estimates through Bayesian inference. Finally, most genome-wide responses propagated through the covariance matrix along approximately three independent and global gene modules. CIPHER underscores the importance of theoretically-grounded models in capturing complex biological responses, highlighting fundamental design principles encoded in cellular fluctuation patterns. 

Nonreciprocal interactions fueled by local energy consumption can be found in biological and synthetic active matter at scales where viscoelastic forces are important. Such systems can be described by “odd” viscoelasticity, which assumes fewer material symmetries than traditional theories. Here we study odd viscoelasticity analytically and using lattice Boltzmann simulations. We identify a pattern-forming instability which produces an oscillating array of fluid vortices, and we elucidate which features govern the growth rate, wavelength, and saturation of the vortices. Our observation of pattern formation through odd mechanical response can inform models of biological patterning and guide engineering of odd dynamics in soft active matter systems. Published by the American Physical Society2024