Publications

Habituation—a phenomenon in which a dynamical system exhibits a diminishing response to repeated stimulations that eventually recovers when the stimulus is withheld—is universally observed in living systems from animals to unicellular organisms. Despite its prevalence, generic mechanisms for this fundamental form of learning remain poorly defined. Drawing inspiration from prior work on systems that respond adaptively to step inputs, we study habituation from a nonlinear dynamics perspective. This approach enables us to formalize classical hallmarks of habituation that have been experimentally identified in diverse organisms and stimulus scenarios. We use this framework to investigate distinct dynamical circuits capable of habituation. In particular, we show that driven linear dynamics of a memory variable with static nonlinearities acting at the input and output can implement numerous hallmarks in a mathematically interpretable manner. This work establishes a foundation for understanding the dynamical substrates of this primitive learning behavior and offers a blueprint for the identification of habituating circuits in biological systems.

2D template matching (2DTM) can be used to detect molecules and their assemblies in cellular cryo-EM images with high positional and orientational accuracy. While 2DTM successfully detects spherical targets such as large ribosomal subunits, challenges remain in detecting smaller and more aspherical targets in various environments. In this work, a novel 2DTM metric, referred to as the 2DTM p-value, is developed to extend the 2DTM framework to more complex applications. The 2DTM p-value combines information from two previously used 2DTM metrics, namely the 2DTM signal-to-noise ratio (SNR) and z-score, which are derived from the cross-correlation coefficient between the target and the template. The 2DTM p-value demonstrates robust detection accuracies under various imaging and sample conditions and outperforms the 2DTM SNR and z-score alone. Specifically, the 2DTM p-value improves the detection of aspherical targets such as a modified artificial tubulin patch particle (500 kDa) and a much smaller clathrin monomer (193 kDa) in simulated data. It also accurately recovers mature 60S ribosomes in yeast lamellae samples, even under conditions of increased Gaussian noise. The new metric will enable the detection of a wider variety of targets in both purified and cellular samples through 2DTM.

The terminal cells of the Drosophila larval tracheal system are perhaps the simplest delivery networks, providing an analogue for mammalian vascular growth and function in a system with many fewer components. These cells are a prime example of single-cell morphogenesis, branching significantly over time to adapt to the needs of the growing tissue they supply. While the genetic mechanisms governing local branching decisions have been studied extensively, an understanding of the emergence of a global network architecture is still lacking. Mapping out the full network architecture of populations of terminal cells at different developmental times of Drosophila larvae, we find that cell growth follows scaling laws relating the total edge length, supply area, and branch density. Using time-lapse imaging of individual terminal cells, we identify that the cells grow in three ways: by extending branches, by the side budding of new branches, and by internally growing existing branches. A generative model based on these modes of growth recapitulates statistical properties of the terminal cell network data. These results suggest that the scaling laws arise from the coupled contributions of branching and internal growth. This study establishes the terminal cell as a uniquely tractable model system for further studies of transportation and distribution networks.

In vivo molecular imaging tools hold immense potential to drive transformative breakthroughs by enabling researchers to visualize cellular and molecular interactions in real-time and/or at high resolution. These advancements will facilitate a deeper understanding of fundamental biological processes and their dysregulation in disease states. Here, we develop and characterize a self-assembling protein nanomicelle called collagen type I binding – thermoresponsive assembled protein (Col1-TRAP) that binds tightly to type I collagen in vitro with nanomolar affinity. For ex vivo visualization, Col1-TRAP is labeled with a near-infrared fluorescent dye (NIR-Col1-TRAP). Both Col1-TRAP and NIR-Col1-TRAP display approximately a 3.8-fold greater binding to type I collagen compared to TRAP when measured by surface plasmon resonance (SPR). We present a proof-of-concept study using NIR-Col1-TRAP to detect fibrotic type I collagen deposition ex vivo in the livers of mice with non-alcoholic steatohepatitis (NASH). We show that NIR-Col1-TRAP demonstrates significantly decreased plasma recirculation time as well as increased liver accumulation in the NASH mice compared to mice without disease over 4 hours. As a result, NIR-Col1-TRAP shows potential as an imaging probe for NASH with in vivo targeting performance after injection in mice.

Multiomic studies at a single cell and spatial resolution are powerful approaches to define molecular and cellular landscape of the human kidney and understand etiology of failed or successful repair in acute or chronic injury. We expand KPMP AtlasV1 with clinicopathological correlations and maps of immune-fibroblast-vascular niches with insights into AKI-CKD transition.

From the Golgi apparatus to endosomes, organelles in the endomembrane system exhibit complex and varied morphologies that are often related to their function. Such membrane-bound organelles operate far from equilibrium due to directed fluxes of smaller trafficking vesicles; the physical principles governing the emergence and maintenance of these structures have thus remained elusive. By understanding individual fission and fusion events in terms of active mechano-chemical cycles, we show how such trafficking manifests at the hydrodynamic scale, resulting not only in fluxes of material -- such as membrane area and encapsulated volume -- but also in active stresses that drive momentum transfer between an organelle and its cytosolic environment. Due to the fluid and deformable nature of the bounding membrane, this gives rise to novel physics, coupling nonequilibrium forces to organelle composition, morphology and hydrodynamic flows. We demonstrate how both stable compartment drift and ramified sac-like morphologies, each reminiscent of Golgi-cisternae, emerge naturally from the same underlying nonequilibrium dynamics of fission and fusion.

Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small classes of physical behavior, it can be difficult to evaluate the efficacy of new approaches. To address this gap, we introduce the Well: a large-scale collection of datasets containing numerical simulations of a wide variety of spatiotemporal physical systems. The Well draws from domain experts and numerical software developers to provide 15TB of data across 16 datasets covering diverse domains such as biological systems, fluid dynamics, acoustic scattering, as well as magneto-hydrodynamic simulations of extra-galactic fluids or supernova explosions. These datasets can be used individually or as part of a broader benchmark suite. To facilitate usage of the Well, we provide a unified PyTorch interface for training and evaluating models. We demonstrate the function of this library by introducing example baselines that highlight the new challenges posed by the complex dynamics of the Well. The code and data is available at https://github.com/PolymathicAI/the_well.

During eukaryotic cell division, a microtubule-based structure called the spindle exerts forces on chromosomes. The best-studied spindle forces, including those responsible for the separation of sister chromatids, are directed parallel to the spindle’s long axis. By contrast, little is known about forces perpendicular to the spindle axis, which determine the metaphase plate configuration and thus the location of chromosomes in the subsequent nucleus. Using live-cell microscopy, we find that metaphase chromosomes are spatially anti-correlated in mouse oocyte spindles, evidence of previously unknown long-range forces acting perpendicular to the spindle axis. We explain this observation by showing that the spindle’s microtubule network behaves as a nematic liquid crystal and that deformation of the nematic field around embedded chromosomes causes long-range repulsion between them.

Viral infections can induce changes in innate immunity that persist after virus clearance. Here, we used blood samples from a human influenza H3N2 challenge study to perform comprehensive multi-omic analyses. We detected remodeling of immune programs in innate immune cells after resolution of the infection that was proportional in magnitude to the level of prior viral load. We found changes associated with suppressed inflammation including decreased cytokine and AP-1 gene expression as well as decreased accessibility at AP-1 targets and interleukin-related gene promoter regions. We also found decreased histone deacetylase gene expression, increased MAP kinase gene expression, and increased accessibility at interferon-related gene promoter regions. Genes involved in inflammation and epigenetic-remodeling showed modulation of gene-chromatin site regulatory circuit activity. These results reveal a coordinated rewiring of the epigenetic landscape in innate immune cells induced by mild influenza virus infection.

The forces that orient the spindle in human cells remain poorly understood due to a lack of direct mechanical measurements in mammalian systems. We use magnetic tweezers to measure the force on human mitotic spindles. Combining the spindle’s measured resistance to rotation, the speed at which it rotates after laser ablating astral microtubules, and estimates of the number of ablated microtubules reveals that each microtubule contacting the cell cortex is subject to ∼5 pN of pulling force, suggesting that each is pulled on by an individual dynein motor. We find that the concentration of dynein at the cell cortex and extent of dynein clustering are key determinants of the spindle’s resistance to rotation, with little contribution from cytoplasmic viscosity, which we explain using a biophysically based mathematical model. This work reveals how pulling forces on astral microtubules determine the mechanics of spindle orientation and demonstrates the central role of cortical dynein clustering.