Publications

Characterizing the conformational ensemble of biomolecular systems is key to understand their functions. Cryoelectron microscopy (cryo-EM) captures two-dimensional snapshots of biomolecular ensembles, giving in principle access to thermodynamics. However, these images are very noisy and show projections of the molecule in unknown orientations, making it very difficult to identify the biomolecule’s conformation in each individual image. Here, we introduce cryo-EM simulation-based inference (cryoSBI) to infer the conformations of biomolecules and the uncertainties associated with the inference from individual cryo-EM images. CryoSBI builds on simulation-based inference, a merger of physics-based simulations and probabilistic deep learning, allowing us to use Bayesian inference even when likelihoods are too expensive to calculate. We begin with an ensemble of conformations, templates from experiments, and molecular modeling, serving as structural hypotheses. We train a neural network approximating the Bayesian posterior using simulated images from these templates and then use it to accurately infer the conformation of the biomolecule from each experimental image. Training is only done once on simulations, and after that, it takes just a few milliseconds to make inference on an image, making cryoSBI suitable for arbitrarily large datasets and direct analysis on micrographs. CryoSBI eliminates the need to estimate particle pose and imaging parameters, significantly enhancing the computational speed compared to explicit likelihood methods. Importantly, we obtain interpretable machine learning models by integrating physics-based approaches with deep neural networks, ensuring that our results are transparent and reliable. We illustrate and benchmark cryoSBI on synthetic data and showcase its promise on experimental single-particle cryo-EM data.

The remarkable cohesion and coordination observed in moving animal groups and their collective responsiveness to threats are thought to be mediated by scale-free correlations, where changes in the behavior of one animal influence others in the group, regardless of the distance between them. But are these features independent of group size? Here, we investigate group cohesiveness and collective responsiveness in computational models of massive schools of fish of up to 50,000 individuals. We show that as the number of swimmers increases, flow interactions destabilize the school, creating clusters that constantly fragment, disperse, and regroup, similar to their biological counterparts. We calculate the spatial correlation and speed of information propagation in these dynamic clusters. Spatial correlations in cohesive and polarized clusters are indeed scale free, much like in natural animal groups, but fragmentation events are preceded by a decrease in correlation length, thus diminishing the group's collective responsiveness, leaving it more vulnerable to predation events. Importantly, in groups undergoing collective turns, the information about the change in direction propagates linearly in time among group members, thanks to the non-reciprocal nature of the visual interactions between individuals. Merging speeds up the transfer of information within each cluster by several fold, while fragmentation slows it down. Our findings suggest that flow interactions may have played an important role in group size regulation, behavioral adaptations, and dispersion in living animal groups.

Tumor-initiating cells (TICs) share features and regulatory pathways with normal stem cells, yet how the stem cell niche contributes to tumorigenesis remains unclear. Here, we identify CXCR4+ macrophages as a niche population enriched in normal mammary ducts, where they promote the regenerative activity of basal cells in response to luminal cell-derived CXCL12. CXCL12 triggers AKT-mediated stabilization of β-catenin, which induces Wnt ligands and pro-migratory genes, enabling intraductal macrophage infiltration and supporting regenerative activity of basal cells. Notably, these same CXCR4+ niche macrophages regulate the tumor-initiating activity of various breast cancer subtypes by enhancing TIC survival and tumor-forming capacity, while promoting early immune evasion through regulatory T cell induction. Furthermore, a CXCR4+ niche macrophage gene signature correlates with poor prognosis in human breast cancer. These findings highlight the pivotal role of the CXCL12-CXCR4 axis in orchestrating interactions between niche macrophages, mammary epithelial cells, and immune cells, thereby establishing a supportive niche for both normal tissue regeneration and mammary tumor initiation.

Suspensions of swimming particles exhibit complex collective behaviors driven by hydrodynamic interactions, showing persistent large-scale flows and long-range correlations. While heavily studied, it remains unclear how such structures depend on the system size and swimmer concentration. To address these issues, we simulate very large systems of suspended swimmers across a range of system sizes and volume fractions. For this we use high-performance simulation tools that build on slender body theory and implicit resolution of steric interactions. At low volume fractions and long times, the particle simulations reveal dynamic flow structures and correlation functions that scale with the system size. These results are consistent with a mean-field limit and agree well with a corresponding kinetic theory. At higher concentrations, the system departs from mean-field behavior. Flow structures become cellular, and correlation lengths scale with the particle size. Here, translational motion is suppressed, while rotational dynamics dominate. These findings highlight the limitations of dilute mean-field models and reveal new behaviors in dense active suspensions.

We examine theoretically the flow interactions and forward flight dynamics of tandem or in-line flapping wings. Two wings are driven vertically with prescribed heaving-and-plunging motions, and the horizontal propulsion speeds and positions are dynamically selected through aero- or hydro-dynamic interactions. Our simulations employ an improved vortex sheet method to solve for the locomotion of the pair within the collective flow field, and we identify 'schooling states' in which the wings travel together with nearly constant separation. Multiple terminal configurations are achieved by varying the initial conditions, and the emergent separations are approximately integer multiples of the wavelength traced out by each wing. We explain the stability of these states by perturbing the follower and mapping out an effective potential for its position in the leader's wake. Each equilibrium position is stabilized since smaller separations are associated with in-phase follower-wake motions that constructively reinforce the flow but lead to decreased thrust on the follower; larger separations are associated with antagonistic follower-wake motions, increased thrust, and a weakened collective wake. The equilibria and their stability are also corroborated by a linearized theory for the motion of the leader, the wake it produces, and its effect on the follower. We also consider a weakly-flapping follower driven with lower heaving amplitude than the leader. We identify 'keep-up' conditions for which the wings may still 'school' together despite their dissimilar kinematics, with the 'freeloading' follower passively assuming a favorable position within the wake that permits it to travel significantly faster than it would in isolation.

Time-resolved single-cell omics data offers high-throughput, genome-wide measurements of cellular states, which are instrumental to reverse-engineer the processes underpinning cell fate. Such technologies are inherently destructive, allowing only cross-sectional measurements of the underlying stochastic dynamical system. Furthermore, cells may divide or die in addition to changing their molecular state. Collectively these present a major challenge to inferring realistic biophysical models. We present a novel approach, \emph{unbalanced} probability flow inference, that addresses this challenge for biological processes modelled as stochastic dynamics with growth. By leveraging a Lagrangian formulation of the Fokker-Planck equation, our method accurately disentangles drift from intrinsic noise and growth. We showcase the applicability of our approach through evaluation on a range of simulated and real single-cell RNA-seq datasets. Comparing to several existing methods, we find our method achieves higher accuracy while enjoying a simple two-step training scheme.

Cells must limit RNA–RNA interactions to avoid irreversible RNA entanglement. Cells may prevent deleterious RNA-RNA interactions by genome organization to avoid complementarity however, RNA viruses generate long, perfectly complementary antisense RNA during replication. How do viral RNAs avoid irreversible entanglement? One possibility is RNA sequestration into biomolecular condensates. To test this, we reconstituted critical SARS-CoV-2 RNA–RNA interactions in Nucleocapsid condensates. We observed that RNAs with low propensity RNA–RNA interactions resulted in more round, liquid-like condensates while those with high sequence complementarity resulted in more heterogeneous networked morphology independent of RNA structure stability. Residue-resolution molecular simulations and direct sequencing-based detection of RNA–RNA interactions support that these properties arise from degree of trans RNA contacts. We propose that extensive RNA–RNA interactions in cell and viral replication are controlled via a combination of genome organization, timing, RNA sequence content, RNA production ratios, and emergent biomolecular condensate material properties.

This work probes the role of cell geometry in orienting self-organized fluid flows in the late-stage Drosophila oocyte. Recent theoretical work has shown that a model, which relies only on hydrodynamic interactions of flexible, cortically anchored microtubules and the mechanical loads from molecular motors moving upon them, is sufficient to generate observed flows. While the emergence of flows has been studied in spheres, oocytes change shape during streaming, and it was unclear how robust these flows are to the geometry of the cell. Here we use biophysical theory and computational analysis to investigate the role of geometry and find that the axis of rotation is set by the shape of the domain and that the flow is robust to biologically relevant perturbations of the domain shape. Using live imaging and three-dimensional flow reconstruction, we test the predictions of the theory/simulation, finding consistency between the model and live experiments, further demonstrating a geometric dependence on flow direction in late-stage Drosophila oocytes.

Huntington's disease (HD) is an inherited neurodegenerative disorder associated with motor and cognitive decline, caused by a mutation in the poly-glutamine (polyQ) region near the N-terminus of the huntingtin (htt) protein. Expansion of the polyQ region results in the disease that is characterized by oligomeric and fibrillar aggregates of mutated protein. The first 17 amino acids (Nt17) of htt, which are adjacent to the polyQ tract, function as a lipid-binding domain, facilitated by the formation of an amphipathic α-helix. There is increasing evidence that lipid interactions may play a role in the toxic gain of function associated with the htt polyQ expansion, as membrane-related changes, including structural abnormalities of several organelles, are observed in HD. Given the uneven and curved shapes of organelles, it is important to examine the mechanistic preferences that drive the preferential partitioning of Nt17 to curved membranes. To better understand the role of the cell membrane environment in the interaction and aggregation of htt, circular dichroism, fluorescence microscopy, and coarse-grained molecular dynamics were employed to measure the association of Nt17 with phospholipid vesicles and subsequent effects throughout time. In zwitterionic curved membranes, sensing was driven by the bulky sidechains of phenylalanine residues, which are able to sense lipid packing defects in the curved regions of the membrane. However, in a mixture of zwitterionic and anionic lipids, curvature sensing is affected by the anionic lipid content, implying the surface charge of membranes affects the curvature sensing process. Salt screening experiments suggest a balance between the electrostatic and hydrophobic interactions that governs the extent to which Nt17 can sense physiologically relevant regions of curvature.

We introduce in-context denoising, a task that refines the connection between attention-based architectures and dense associative memory (DAM) networks, also known as modern Hopfield networks. Using a Bayesian framework, we show theoretically and empirically that certain restricted denoising problems can be solved optimally even by a single-layer transformer. We demonstrate that a trained attention layer processes each denoising prompt by performing a single gradient descent update on a context-aware DAM energy landscape, where context tokens serve as associative memories and the query token acts as an initial state. This one-step update yields better solutions than exact retrieval of either a context token or a spurious local minimum, providing a concrete example of DAM networks extending beyond the standard retrieval paradigm. Overall, this work solidifies the link between associative memory and attention mechanisms first identified by Ramsauer et al., and demonstrates the relevance of associative memory models in the study of in-context learning.