Publications

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.

We present a new implementation of the driven similarity renormalization group (DSRG) based on a density matrix renormalization group (DMRG) reference. The explicit build of high-order reduced density matrices is avoided by forming matrix-product-state compressed intermediates. This algorithm facilitates the application of DSRG second- and third-order perturbation theories to dodecacene with an active space of 50 electrons in 50 orbitals. This active space appears the largest employed to date within the framework of internally contracted multireference formalism. The DMRG-DSRG approach is applied to several challenging systems, including the singlet-triplet gaps ($\Delta_{\rm ST}$) of oligoacenes ranging from naphthalene to dodecacene, the vertical excitation energies of zeaxanthin, and the ground-state potential energy curve (PEC) of Cr$_2$ molecule. Our best estimate for the vertical $\Delta_{\rm ST}$ of dodecacene is 0.22 eV, showing an excellent agreement with that of the linearized adiabatic connection method (0.24 eV). For zeaxanthin, all DSRG schemes suggest the order of $\rm 2\, ^1 A_g^- < 1\, ^1 B_u^+ < 1\, ^1 B_u^-$ for excited states. Both the equilibrium and the shoulder regions of the Cr$_2$ PEC are reasonably reproduced by the linearized DSRG with one- and two-body operators.

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.

Fast Ewald summation is the most widely used approach for computing long-range Coulomb interactions in molecular dynamics (MD) simulations. While the asymptotic scaling is nearly optimal, its performance on parallel architectures is dominated by the global communication required for the underlying fast Fourier transform (FFT). Here, we develop a novel method, ESP - Ewald summation with prolate spheroidal wave functions (PSWFs) - that, for a fixed precision, sharply reduces the size of this transform by performing the Ewald split via a PSWF. In addition, PSWFs minimize the cost of spreading and interpolation steps that move information between the particles and the underlying uniform grid. We have integrated the ESP method into two widely-used open-source MD packages: LAMMPS and GROMACS. Detailed benchmarks show that this reduces the cost of computing far-field electrostatic interactions by an order of magnitude, leading to better strong scaling with respect to number of cores. The total execution time is reduced by a factor of 2 to 3 when using more than one thousand cores, even after optimally tuning the existing internal parameters in the native codes. We validate the accelerated codes in realistic long-time biological simulations.

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.

s we move through the world, the pattern of light projected on our eyes is complex and dynamic. Even in a world that is completely stationary, our self-motion results in velocities on the retina. Added to this there exist independently moving objects, which also create evolving patterns of light on our eyes. Despite the fact that both induce retinal velocities, we are somehow able to accurately distinguish between stable parts of the environment and independently moving objects. One might hypothesize this is achieved by detecting discontinuities in the spatial pattern of velocities, however this computation is also sensitive to velocity discontinuities at the boundaries of stationary objects. We instead propose that humans make use of the specific constraints that self-motion imposes on retinal velocities. When an eye translates and rotates within a rigid 3D world, the velocity at each location on the retina is constrained to a line segment in the 2D space of retinal velocities (Longuet, Higgins, Prazdny 1980). The slope and intercept of this segment is determined by the eye's translation and rotation, and the position along the segment is determined by depth of the scene. Since all possible velocities arising from a rigid world must lie on this segment, velocities not on the segment must correspond to moving objects. We hypothesize that humans make use of these constraints, by partially inferring self-motion based on the global pattern of retinal velocities, and using deviations of local velocity from the resulting constraint lines to detect moving objects. We call this the depth constraint segment.
We first test if the depth constraint has an effect on 2D velocity discrimination using a simplified stimulus made with a collection of plaids that drifted according to a moving observer. Under these conditions, we failed to find convincing evidence that the constraint had on 2D velocity discrimination. We then tried to test the hypothesis with more naturalistic stimuli, viewed within a head-mounted virtual reality device, simulating a translation forward in different virtual environments. This time, consistent with the hypothesis, we found that performance depended on the deviation of the object velocity from the constraint segment, not on the difference between retinal velocities of the object and its surrounding velocities. Finally, we examine the effect of self-motion on detecting a specific kind of motion artifact (jitter) that occurs in an augmented reality display. We found that our ability to perceive the motion artifact depended on self-motion and the evoked eye movements.

As we move through the world, the pattern of light projected on our eyes is complex and dynamic, yet we are still able to distinguish between moving and stationary objects. We propose that humans accomplish this by exploiting constraints that self-motion imposes on retinal velocities. When an eye translates and rotates in a stationary 3D scene, the velocity at each retinal location is constrained to a line segment in the 2D space of retinal velocities. The slope and intercept of this segment is determined by the eye's translation and rotation, and the position along the segment is determined by the local scene depth. Since all possible velocities arising from a stationary scene must lie on this segment, velocities that are not must correspond to objects moving within the scene. We hypothesize that humans make use of these constraints by using deviations of local velocity from these constraint lines to detect moving objects. To test this, we used a virtual reality headset to present rich wide-field stimuli, simulating the visual experience of translating forward in several virtual environments with varied precision of depth information. Participants had to determine if a cued object moved relative to the scene. Consistent with the hypothesis, we found that performance depended on the deviation of the object velocity from the constraint segment, rather than a difference between retinal velocities of the object and its local surround. We also found that the endpoints of the constraint segment reflected the precision of depth information available in the different virtual environments.

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.