Publications

Important classes of active matter systems can be modeled using kinetic theories. However, kinetic theories can be high dimensional and challenging to simulate. Reduced-order representations based on tracking only low-order moments of the kinetic model serve as an efficient alternative, but typically require closure assumptions to model unrepresented higher-order moments. In this study, we present a learning framework based on neural networks that exploit rotational symmetries in the closure terms to learn accurate closure models directly from kinetic simulations. The data-driven closures demonstrate excellent a-priori predictions comparable to the state-of-the-art Bingham closure. We provide a systematic comparison between different neural network architectures and demonstrate that nonlocal effects can be safely ignored to model the closure terms. We develop an active learning strategy that enables accurate prediction of the closure terms across the entire parameter space using a single neural network without the need for retraining. We also propose a data-efficient training procedure based on time-stepping constraints and a differentiable pseudo-spectral solver, which enables the learning of stable closures suitable for a-posteriori inference. The coarse-grained simulations equipped with data-driven closure models faithfully reproduce the mean velocity statistics, scalar order parameters, and velocity power spectra observed in simulations of the kinetic theory. Our differentiable framework also facilitates the estimation of parameters in coarse-grained descriptions conditioned on data.

Single-particle cryo-electron microscopy (cryo-EM) is a technique that takes projection images of biomolecules frozen at cryogenic temperatures. A major advantage of this technique is its ability to image single biomolecules in heterogeneous conformations. While this poses a challenge for data analysis, recent algorithmic advances have enabled the recovery of heterogeneous conformations from the noisy imaging data. Here, we review methods for the reconstruction and heterogeneity analysis of cryo-EM images, ranging from linear-transformation-based methods to nonlinear deep generative models. We overview the dimensionality-reduction techniques used in heterogeneous 3D reconstruction methods and specify what information each method can infer from the data. Then, we review the methods that use cryo-EM images to estimate probability distributions over conformations in reduced subspaces or predefined by atomistic simulations. We conclude with the ongoing challenges for the cryo-EM community.

We use a combination of unsupervised clustering and sparsity-promoting inference algorithms to learn locally dominant force balances that explain macroscopic pattern formation in self-organized active particle systems. The self-organized emergence of macroscopic patterns from microscopic interactions between self-propelled particles can be widely observed nature. Although hydrodynamic theories help us better understand the physical basis of this phenomenon, identifying a sufficient set of local interactions that shape, regulate, and sustain self-organized structures in active particle systems remains challenging. We investigate a classic hydrodynamic model of self-propelled particles that produces a wide variety of patterns, like asters and moving density bands. Our data-driven analysis shows that propagating bands are formed by local alignment interactions driven by density gradients, while steady-state asters are shaped by a mechanism of splay-induced negative compressibility arising from strong particle interactions. Our method also reveals analogous physical principles of pattern formation in a system where the speed of the particle is influenced by local density. This demonstrates the ability of our method to reveal physical commonalities across models. The physical mechanisms inferred from the data are in excellent agreement with analytical scaling arguments and experimental observations.

Resolving chromatin-remodeling-linked gene expression changes at cell-type resolution is important for understanding disease states. Here we describe MAGICAL (Multiome Accessibility Gene Integration Calling and Looping), a hierarchical Bayesian approach that leverages paired single-cell RNA sequencing and single-cell transposase-accessible chromatin sequencing from different conditions to map disease-associated transcription factors, chromatin sites, and genes as regulatory circuits. By simultaneously modeling signal variation across cells and conditions in both omics data types, MAGICAL achieved high accuracy on circuit inference. We applied MAGICAL to study Staphylococcus aureus sepsis from peripheral blood mononuclear single-cell data that we generated from subjects with bloodstream infection and uninfected controls. MAGICAL identified sepsis-associated regulatory circuits predominantly in CD14 monocytes, known to be activated by bacterial sepsis. We addressed the challenging problem of distinguishing host regulatory circuit responses to methicillin-resistant and methicillin-susceptible S. aureus infections. Although differential expression analysis failed to show predictive value, MAGICAL identified epigenetic circuit biomarkers that distinguished methicillin-resistant from methicillin-susceptible S. aureus infections.

Bilateral symmetry defines much of the animal kingdom and is crucial for numerous functions of bilaterian organisms. Genetic approaches have discovered highly conserved patterning networks that establish bilateral symmetry in early embryos,1 but how this symmetry is maintained throughout subsequent morphogenetic events remains largely unknown.2 Here we show that the terminal patterning system—which relies on Ras/ERK signaling through activation of the Torso receptor by its ligand Trunk3—is critical for preserving bilateral symmetry during Drosophila body axis elongation, a process driven by cell rearrangements in the two identical lateral regions of the embryo and specified by the dorsal-ventral and anterior-posterior patterning systems.4 We demonstrate that fluctuating asymmetries in this rapid convergent-extension process are attenuated in normal embryos over time, possibly through noise-dissipating forces from the posterior midgut invagination and movement. However, when Torso signaling is attenuated via mutation of Trunk or RNAi directed against downstream Ras/ERK pathway components, body axis elongation results in a characteristic corkscrew phenotype,5 which reflects dramatic reorganization of global tissue flow and is incompatible with viability. Our results reveal a new function downstream of the Drosophila terminal patterning system in potentially active control of bilateral symmetry and should motivate systematic search for similar symmetry-preserving regulatory mechanisms in other bilaterians.

We present a direct derivation of the typical time derivatives used in a continuum description of complex fluid flows{,} harnessing the principles of the kinematics of line elements. The evolution of the microstructural conformation tensor in a flow and the physical interpretation of different derivatives then follow naturally.

Study question:
Is there any relationship between the relative telomere length (RTL) within cumulus cells (CCs) and the outcome of assisted reproductive treatment using the corresponding oocyte?

Summary answer:
Lower RTLs in CCs were significantly associated with embryos chosen for transfer or cryopreservation. In contrast, embryos considered non-viable (discarded) tended to have higher RTLs.

Cryo-electron microscopy (cryo-EM) has recently become a leading method for obtaining high-resolution structures of biological macromolecules. However, cryo-EM is limited to biomolecular samples with low conformational heterogeneity, where most conformations can be well-sampled at various projection angles. While cryo-EM provides single-molecule data for heterogeneous molecules, most existing reconstruction tools cannot retrieve the ensemble distribution of possible molecular conformations from these data. To overcome these limitations, we build on a previous Bayesian approach and develop an ensemble refinement framework that estimates the ensemble density from a set of cryo-EM particle images by reweighting a prior conformational ensemble, e.g., from molecular dynamics simulations or structure prediction tools. Our work provides a general approach to recovering the equilibrium probability density of the biomolecule directly in conformational space from single-molecule data. To validate the framework, we study the extraction of state populations and free energies for a simple toy model and from synthetic cryo-EM particle images of a simulated protein that explores multiple folded and unfolded conformations.

During eukaryotic cell division, microtubules connect to chromosomes by attaching to the kinetochore via the NDC80 complex (NDC80c). The regulation of kinetochore microtubule (KMT) detachment is crucial for correcting mitotic errors. Here, we investigate the mechanism of KMT detachment by combining photoconversion measurements of KMT detachment rate, FLIM-FRET measurements of NCD80c/KMT binding, and mathematical modeling. Our results support a dual detachment mechanism in which KMTs detach from kinetochores when either 1) all NDC80c spontaneously unbind from the KMT or 2) following KMT catastrophe. We identify kinetochore components that selectively impact these two mechanisms and show that the affinity of NDC80c for KMTs is reduced at low-tension, non-bioriented kinetochores due to centromere-localized Aurora B phosphorylating the NDC80c, resulting in an elevated detachment rate for the associated KMTs. Taken together, this work leads to a biophysical model for the molecular basis of KMT detachments and their regulation during mitotic error correction.

The 3D pose estimation problem – aligning pairs of noisy 3D point clouds – is a problem with a wide variety of real- world applications. Here we focus on the use of quaternion- based neural network approaches to this problem and ap- parent anomalies that have arisen in previous efforts to re- solve them. In addressing these anomalies, we draw heav- ily from the extensive literature on closed-form methods to solve this problem. We suggest that the major concerns that have been put forward could be resolved using a sim- ple multi-valued training target derived from rigorous theo- retical properties of the rotation-to-quaternion map of Bar- Itzhack. This multi-valued training target is then demon- strated to have good performance for both simulated and ModelNet targets. We provide a comprehensive theoretical context, using the quaternion adjugate, to confirm and es- tablish the necessity of replacing single-valued quaternion functions by quaternions treated in the extended domain of multiple-charted manifolds.