Corey S. O’Hern

MOTIVATION: Advances in high-throughput chromatin conformation capture have provided insight into the three-dimensional structure and organization of chromatin. While bulk Hi-C experiments capture spatio-temporally averaged chromatin interactions across millions of cells, single-cell Hi-C experiments report on the chromatin interactions of individual cells. Supervised and unsupervised algorithms have been developed to embed single-cell Hi-C maps and identify different cell types. However, single-cell Hi-C maps are often difficult to cluster due to their high sparsity, with state-of-the-art algorithms achieving a maximum Adjusted Rand Index (ARI) of only ≲0.4 on several datasets. RESULTS: We introduce a novel unsupervised algorithm, Single-cell Clustering Using Diagonal Diffusion Operators (SCUDDO), to embed and cluster single-cell Hi-C maps. We evaluate SCUDDO on four previously difficult-to-cluster single-cell Hi-C datasets, and show that it can outperform other current algorithms in ARI by ≳0.2. Further, SCUDDO outperforms all other tested algorithms even when we restrict the number of intrachromosomal maps for each cell type and when we use only a small fraction of contacts in each Hi-C map. Thus, SCUDDO can capture the underlying latent features of single-cell Hi-C maps and provide accurate labelling of cell types even when cell types are not known a priori. AVAILABILITY AND IMPLEMENTATION: SCUDDO is freely available at https://www.github.com/lmaisuradze/scuddo as well as https://doi.org/10.6084/m9.figshare.31759915. The tested datasets are publicly available and can be downloaded from the Gene Expression Omnibus.

Multiscale periodic metamaterials have been designed for numerous applications, such as impact absorption, acoustic cloaking, photonic band gaps, and mechanical logic gates. This prior work has focused on optimizing mesoscale structure for desired bulk isotropic properties. In contrast, we seek to develop materials with highly anisotropic elastic properties. To quantify elastic anisotropy, we introduce two rotationally invariant, normalized quantities that characterize the anisotropic response to shear and compression, respectively, $A_G$ and $A_C$. We find that typical crystalline solids possess average elastic anisotropy $\overline{A}_G \approx 0.15$ and $\overline{A}_C \approx 0.09$. Compared to atomic crystals, jammed granular materials can attain elastic anisotropies that are several orders of magnitude larger. Since grain rearrangements reduce anisotropy in granular materials, to preserve strong elastic anisotropy, we design tessellated granular materials that consist of multiple connected grain-filled voxels, which limit rearrangements and enable highly anisotropic elastic properties. Bulk granular packings with $N$ grains prepared at pressure $p$ have maximal anisotropy for $pN^2\sim1$ and become isotropic in the large-$pN^2$ limit. We show that homogeneously tessellated granular systems can inherit the elastic response of the constituent voxel configurations with elastic anisotropy up to $100$ times that of crystalline compounds over a range of $pN^2$. We show further methods to tune the elastic anisotropy of tessellations by designing heterogeneously patterned voxel configurations and tessellations that allow large boundary deformations.

Multiscale periodic metamaterials have been designed for numerous applications, such as impact absorption, acoustic cloaking, photonic band gaps, and mechanical logic gates. This prior work has focused on optimizing mesoscale structure for desired bulk isotropic properties. In contrast, we seek to develop materials with highly anisotropic elastic properties. To quantify elastic anisotropy, we introduce two rotationally invariant, normalized quantities that characterize the anisotropic response to shear and compression, respectively, $A_G$ and $A_C$. We find that typical crystalline solids possess average elastic anisotropy $\overline{A}_G \approx 0.15$ and $\overline{A}_C \approx 0.09$. Compared to atomic crystals, jammed granular materials can attain elastic anisotropies that are several orders of magnitude larger. Since grain rearrangements reduce anisotropy in granular materials, to preserve strong elastic anisotropy, we design tessellated granular materials that consist of multiple connected grain-filled voxels, which limit rearrangements and enable highly anisotropic elastic properties. Bulk granular packings with $N$ grains prepared at pressure $p$ have maximal anisotropy for $pN^2\sim1$ and become isotropic in the large-$pN^2$ limit. We show that homogeneously tessellated granular systems can inherit the elastic response of the constituent voxel configurations with elastic anisotropy up to $100$ times that of crystalline compounds over a range of $pN^2$. We show further methods to tune the elastic anisotropy of tessellations by designing heterogeneously patterned voxel configurations and tessellations that allow large boundary deformations.

An important goal of computational studies of protein-protein interfaces (PPIs) is to predict the binding site between two monomers that form a heterodimer. The simplest version of this problem is to rigidly re-dock the bound forms of the monomers, which involves generating computational models of the heterodimer and then scoring them to determine the most native-like models. PPI scoring functions have been assessed previously using rank- and classification-based metrics; however, these methods are sensitive to the number and quality of models in the scoring function training set. We assess the accuracy of seven PPI scoring functions by comparing their scores of computational models of PPIs to a measure of structural similarity to the x-ray crystal structure (i.e. the DockQ score) for a non-redundant set of heterodimers from the Protein Data Bank. For each heterodimer, we generate re-docked models uniformly sampled over DockQ and calculate the Spearman correlation between the PPI scores and DockQ. For some targets, the scores and DockQ are highly correlated; however, for many targets, there are weak correlations. Several physical features explain the difference between difficult- and easy-to-score targets. Strong correlations exist between the score and DockQ for targets with highly intertwined monomers and many interface contacts. We also develop a new score based on only two physical features that matches the performance of current PPI scoring functions. In addition, we address the more general problem of flexible-body docking by generating and docking intermediate monomer conformations between their bound and unbound forms. We score the docked models and find that the Spearman correlations between the PPI scores and DockQ decrease strongly as the monomers are deformed from their bound conformations. These results emphasize that PPI docking predictions can be improved by focusing on correlations between the PPI score and DockQ and incorporating more discriminating physical features into PPI scoring functions.

Quasi-2D droplets pushed by fluid have predictable breakup behavior against cylindrical obstacles.

Bedload transport occurs when the shear stress, or non-dimensional Shields stress, imparted by a fluid onto a sediment bed exceeds a critical value for sediment entrainment. The history of fluid stress imparted onto a sediment bed influences this critical Shields stress, with bed strengthening occurring under unidirectional flows and bed weakening occurring when the flow direction is reversed. In this study, we examine directional strengthening and weakening in a sediment bed for multiple fluid stress orientations using a rotating bed of sand in a laboratory flume. This sediment bed is exposed to an initial subcritical conditioning flow followed by a subsequent erosive flow at an offset angle of 0º, 45º, 90º, 135º, or 180º. We identify the particle trajectories of a population of sediment grains to measure their velocity, activity, and associated bulk statistics. We confirm bed strengthening (i.e., lower grain velocity and activity) in the unidirectional case, especially for flows at or below the nominal critical Shields stress. As the angular offset increases between the conditioning and erosive flows, both grain velocity and activity increase, with the greatest bed weakening at offsets of 135° and 180°. Our results confirm that stress history is stored anisotropically in the sediment bed, supporting mechanisms such as shear jamming where an anisotropic granular fabric develops in response to shear. These results inform our understanding of how subcritical and critical fluid-imposed stresses can modify the grain contact and force networks in geophysical contexts.

We perform cell segmentation on images from experimental studies of confluent, mobile cells in epithelial monolayers and show that these systems possess a broad, positively-skewed shape parameter distribution $P(\mathcal{A})$, where $\mathcal{A}=p^2/4πa$, $p$ is the perimeter, and $a$ is area of each cell. $P(\mathcal{A})$ is peaked at a value higher than the typical shape parameter $\mathcal{A}^* \sim 1.15$ that occurs for randomly packed, static confluent cell monolayers. The distribution does not arise from a heterogeneous population of cells with different fixed $\mathcal{A}$, nor can it arise from cell shape fluctuations from strains below the elastic limit. Instead, we find that all cells in each monolayer sample $\mathcal{A}$ values that span the full shape parameter distribution. We develop a deformable particle model that allows cell perimeter to adapt to local forces during cell motion, and this model recovers $P(\mathcal{A})$ to within $5\%$ for both MDCK and HaCaT epithelial cell monolayers. These results emphasize that confluent epithelial monolayers of mobile cells generate a well-defined broad shape parameter distribution that is independent of the initial cell shapes.

Proteins are composed of chains of amino acids that fold into complex three-dimensional structures. Several key features, such as the radius of gyration, fraction of core amino acids f_{core}, packing fraction 〈ϕ〉 of core amino acids, and structure factor S(q) define the structure of folded proteins. It is well-known that folded proteins are compact with a radius of gyration R_{g}(N)∼N^{ν} that obeys power-law scaling with the number of amino acids N and ν∼1/3, f_{core}≈0.09, and 〈ϕ〉≈0.55. We also investigate the internal scaling of the radius of gyration R_{g}(n) versus the chemical separation n between amino acids for subchains of length n and show that it does not obey simple power-law scaling with ν∼1/3. Instead, R_{g}(n)∼n^{ν_{1,2}} with a larger exponent ν_{1}>1/3 for small n and a smaller exponent ν_{2}<1/3 for large n. To develop a minimal model for proteins that recapitulates these defining structural features, we carry out collapse simulations for a series of coarse-grained models with increasing complexity. We show that a model, which coarse-grains amino acids into a single spherical backbone bead and several variable-sized side-chain beads and enforces bend- and dihedral-angle constraints for the backbone, recapitulates R_{g}(n), f_{core}, 〈ϕ〉, and S(q) for more than 2500 x-ray crystal structures of proteins.

Breast cancer cells invade mammary adipose tissue during initial stages of metastasis but how the physical properties of adipose tissue regulate this process remains unclear. Here, we combined single cell mechanical characterization of primary adipocytes with microfluidic hydrogel fabrication, quantitative multiparametric imaging, Discrete Element Method (DEM) simulations, and in vivo experiments to elucidate these connections. First, we quantified the heterogeneous size and stiffness of primary adipocytes, and replicated these properties by fabricating adipocyte-sized polyacrylamide (PAAm) beads with tunable elasticity. Subsequently, we embedded these beads into type I collagen, the primary fibrillar extracellular matrix (ECM) component of breast adipose tissue, to form 3D granular hydrogels mimicking aspects of native adipose tissue architecture. Granular hydrogels embedded with beads demonstrated increased breast cancer cell invasion relative to bead-free controls, an effect that was more pronounced with soft versus stiff beads and correlated with increased collagen fiber alignment and hierarchical organization. In addition, live cell imaging and DEM simulations revealed that soft beads promoted invasion relative to stiff beads by deforming in response to confined cancer cell migration. Fiber alignment and adipocyte deformation trends were validated in vivo via intravital imaging of cancer cell migration in mammary fat pads of mice, and suggest that adipocyte mechanics regulate breast cancer invasion by coordinating both ECM architecture and cellular confinement. Ultimately, this work highlights the utility of tunable PAAm bead-collagen composites as micromechanical models to study the effect of adipose tissue structure on cancer cell invasion.

We identify the local structural defects that control the nonaffine displacement fields in jammed disk packings subjected to athermal, quasistatic simple shear. While complex nonaffine displacement fields typically occur during simple shear, isolated effective quadrupoles are also observed and their probability increases with increasing pressure. We show that the emergence of an isolated effective quadrupole requires the breaking of an interparticle contact that is aligned with low-frequency, spatially extended vibrational modes. Since the Eshelby inhomogeneity problem gives rise to quadrupolar displacement fields in continuum materials, we reformulate and implement Eshelby's equivalent inclusion method (EIM) for jammed disk packings. Using EIM, we show that we can reconstruct the nonaffine displacement fields for jammed disk packings in response to applied shear as a sum of discrete Eshelby-like defects that are caused by mismatches in the local stiffnesses of triangles formed from Delaunay triangulation of the disk centers.

As Moore’s law approaches its terminus, the need for alternative computing paradigms becomes increasingly pressing. A promising alternative exploits mechanical interactions in materio to perform computation. One way to achieve this is with computational granular metamaterials (CGMMs), materials that have been optimized to harness mechanical signals such as force, shear, or wave propagation to process information. When materials are designed to perform several computations simultaneously, each at a unique vibrational frequency, the resulting polycomputational materials may eventually achieve functional densities superior to traditional computing substrates. However, the relationship between material structure and computational ability is not yet understood. To address this gap, we adopt lesioning methods from neuroscience to probe the structure-function relationship within CGMMs. By systematically disabling grains in optimized configurations, we identify critical components and reveal how specific grains participate in computation. We complement our in silico work with a hardware demonstration of a vibrational granular metamaterial, illustrating how future, more complex, and useful CGMMs, designed in silico, may be physically realized. These findings offer a new understanding of the computational dynamics of CGMMs, which may suggest ways to further increase their computational density in the future. This may eventually allow them to take their place among the next generation of computing systems in the post-Moore’s Law era.

Animal tissues exist within a continuum of fluid to solid states, and transitions between states are important for embryonic development, wound healing and cancer metastasis. Fluid-to-solid transitions are governed by the ratio of adhesive energy to kinetic energy. Here, we find that presomitic mesoderm solidification is driven by an intrinsic decline in cell speed along with an increase in adhesion mediated by Cadherin 2 in parallel with fibronectin and its receptor Integrin α5. A computational model of cell-cell adhesion in the central tissue mesenchyme and cell-ECM adhesion on the tissue surface explains the observed phenotypes. Further, we identify negative feedback within the ECM as fibronectin supports the formation of a separate layer of Fibrillin 2b matrix that inhibits solidification. These data reveal a tissue fluidity code in which solidification is promoted by cadherins in parallel with Integrin α5 and fibronectin, whereas negative feedback through Fibrillin 2b promotes fluidization.

Particulate materials, such as granular materials, foams, emulsions, and packings of cells, including epithelial monolayers and tissues, are composed of “particles” that can change their shapes under external stresses. Computational methods for modeling the structural and mechanical properties of particulate materials either prescribe a fixed shape for each particle, or construct volumetric particle meshes that can be computationally expensive. In this chapter, we review the recently developed deformable particle model, which only requires a surface mesh to describe arbitrary changes in particle shape. We introduce the shape-energy function for the deformable particle model, which can be tuned to model the mechanics of bulk elastic particles, elastic shells, particles governed by surface tension, and particles that undergo plastic shape changes. In addition, we specify the particle interactions, including frictional interactions between contacting rough and smooth deformable particle surfaces. We compare the compressive force between a flat wall and a deformable particle to that between two contacting bulk elastic particles and elastic shells as a function of the particle deformation. We also illustrate how to perform calculations of the pressure, packing fraction, and vibrational density of states for static packings of deformable particles. In addition, we describe extensions of the deformable particle model to flexible, tesselated granular materials, which have applications in soft robotics.

Glasses, or amorphous solids, can possess enhanced mechanical, optical, and electromagnetic properties compared to crystalline solids. Preparation of glasses often involves rapid quenching of liquids at rates faster than the critical cooling rate Rc, which quantifies the glass-forming ability (GFA) of the material. Understanding the GFA of condensed matter systems is of both theoretical and practical importance. In this chapter, we identify the connections between the GFA in particle-based materials (such as atomic and colloidal systems) and dense packing of hard spheres. We first review previous results for hard-sphere crystallization and glass formation. We then discuss computer simulation methods and results concerning the GFA for hard spheres and the relevance of these results for the GFA of alloys.

Bedload transport occurs when the shear stress, or non-dimensional Shields stress, imparted by a fluid onto a sediment bed exceeds a critical value for sediment entrainment. The history of fluid stress imparted onto a sediment bed influences this critical Shields stress, with bed strengthening occurring under unidirectional flows and bed weakening occurring when the flow direction is reversed. In this study, we examine directional strengthening and weakening in a sediment bed for multiple fluid stress orientations using a rotating bed of sand in a laboratory flume. This sediment bed is exposed to an initial subcritical conditioning flow followed by a subsequent erosive flow at an offset angle. We identify the particle trajectories of a population of sediment grains to measure their velocity, activity, and associated bulk statistics. We confirm bed strengthening (i.e., lower grain velocity and activity) in the unidirectional case, especially for flows at or below the nominal critical Shields stress. As the angular offset increases between the conditioning and erosive flows, both grain velocity and activity increase, with the greatest bed weakening at offsets of 135° and 180°. Our results confirm that stress history is stored anisotropically in the sediment bed, supporting mechanisms such as shear jamming where an anisotropic granular fabric develops in response to shear. Our results have implications for predicting sediment transport in natural settings where flows can come from many directions, and for improving our understanding of how subcritical and critical fluid-imposed stresses can modify the grain contact and force networks in geophysical contexts.

Soft robots can achieve exceptional adaptability through tunable morphological and mechanical properties. Incorporating materials with dynamically adjustable characteristics can enhance this versatility further. Granular meta-materials, consisting of discrete particles with individually variable properties, offer a promising approach to bulk property adaptation by adjusting the properties of constituent particles. This work introduces variable size and variable stiffness (VS<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) particles, in which both particle size and stiffness are independently modulated through concentric pneumatic chambers. We characterize the achievable workspace, mapping particle responses to independent chamber inflation. To demonstrate their use in a granular assembly, we arrange an array of VS<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> particles in a hexagonal packing and validate that behavior in packed configurations aligns with free-space characterizations. This study establishes a foundation for adaptive granular materials and provides a platform for further computational and experimental exploration of 2D and 3D granular metamaterials with tunable properties.

Proteins fold to a specific functional conformation with a densely packed core that controls their stability. Despite their importance, we lack a quantitative explanation for why all protein cores, regardless of their overall fold, possess the same average packing fraction <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"> <a:mrow> <a:mo>〈</a:mo> <a:mi>ϕ</a:mi> <a:mo>〉</a:mo> <a:mo>≈</a:mo> <a:mn>0.55</a:mn> </a:mrow> </a:math> . However, important developments in the physics of jamming in particulate systems can shed light on the packing of protein cores. Here, we extend the framework of jamming to describe core packing in collapsed polymers, as well as in all-atom models of folded proteins. First, we show in a spherical bead-spring polymer model (with and without bond-angle constraints) that as the hydrophobic interactions increase relative to thermal fluctuations, a jamming-like transition occurs when the core packing fraction exceeds <b:math xmlns:b="http://www.w3.org/1998/Math/MathML"> <b:msub> <b:mi>ϕ</b:mi> <b:mi>c</b:mi> </b:msub> </b:math> with the same power-law scaling behavior for the potential energy <c:math xmlns:c="http://www.w3.org/1998/Math/MathML"> <c:msub> <c:mi>V</c:mi> <c:mi>r</c:mi> </c:msub> </c:math> , excess contact number <d:math xmlns:d="http://www.w3.org/1998/Math/MathML"> <d:mrow> <d:mi mathvariant="normal">Δ</d:mi> <d:mi>N</d:mi> </d:mrow> </d:math> , and characteristic frequency of the vibrational density of states <f:math xmlns:f="http://www.w3.org/1998/Math/MathML"> <f:msup> <f:mi>ω</f:mi> <f:mo>*</f:mo> </f:msup> </f:math> versus <g:math xmlns:g="http://www.w3.org/1998/Math/MathML"> <g:mrow> <g:mi mathvariant="normal">Δ</g:mi> <g:mi>ϕ</g:mi> <g:mo>=</g:mo> <g:mi>ϕ</g:mi> <g:mo>−</g:mo> <g:msub> <g:mi>ϕ</g:mi> <g:mi>c</g:mi> </g:msub> </g:mrow> </g:math> as that for jammed particulate systems. Then, we develop an all-atom model for proteins and find that, above <i:math xmlns:i="http://www.w3.org/1998/Math/MathML"> <i:mrow> <i:msub> <i:mi>ϕ</i:mi> <i:mi>c</i:mi> </i:msub> <i:mo>∼</i:mo> <i:mn>0.55</i:mn> </i:mrow> </i:math> , protein cores undergo a jamming-like transition, but with anomalous power-law scaling for <j:math xmlns:j="http://www.w3.org/1998/Math/MathML"> <j:msub> <j:mi>V</j:mi> <j:mi>r</j:mi> </j:msub> <j:mo>,</j:mo> <j:mo> </j:mo> <j:mrow> <j:mi mathvariant="normal">Δ</j:mi> <j:mi>N</j:mi> </j:mrow> </j:math> , and <l:math xmlns:l="http://www.w3.org/1998/Math/MathML"> <l:msup> <l:mi>ω</l:mi> <l:mo>*</l:mo> </l:msup> </l:math> versus <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mi mathvariant="normal">Δ</m:mi> <m:mi>ϕ</m:mi> </m:mrow> </m:math> . The all-atom protein model remains close to the native protein structure during jamming and accurately refolds from partially unfolded states.

We describe size-varying cylindrical particles made from silicone elastomers that can serve as building blocks for robotic granular materials. The particle size variation, which is achieved by inflation, gives rise to changes in stiffness under compression. We design and fabricate inflatable particles that can become stiffer or softer during inflation, depending on key parameters of the particle geometry, such as the ratio of the fillet radius to the wall thickness, r/t. We also conduct numerical simulations of the inflatable particles and show that they only soften during inflation when localization of large strains occurs in the regime r/t -&gt; 0. This work introduces novel particle systems with tunable size and stiffness that can be implemented in numerous soft robotic applications.

Machine learning approaches have been applied to numerous biological problems, including the scoring of protein-protein interface (PPI) models. Graph neural networks (GNNs) have been identified as an effective tool for PPI scoring due to their ability to represent three-dimensional protein structures as nodes and edges of a graph, preserving rotational invariance without loss of structural information. However, in a comparison study of leading PPI scoring functions, we observed that GNN-based models consistently underperformed compared to non-GNN-based methods. Using a large dataset of unique heterodimers, we demonstrated that the number of heavy atom contacts at the protein interface showed stronger Spearman correlations with the ground truth score, DockQ, than two recently published GNN-based models. We propose that the poor performance of GNN-based scoring functions can be attributed to several factors, including a nonuniform distribution of ground truth scores in GNN training datasets and overgeneralized or non-local node and edge features. To address these issues, we propose a new GNN-based PPI scoring function trained on a dataset that uniformly samples DockQ, with equal representation of near-native and non-native models. We also incorporate local geometric and physical features into our scoring function using contacts defined through Voronoi tessellations, such as the local packing fraction and interface-specific electrostatic and hydrophobic interactions.

Proteins are composed of chains of amino acids that fold into complex three-dimensional structures. Several key features, such as the radius of gyration, fraction of core amino acids $f_{\rm core}$, packing fraction $\langle ϕ\rangle$ of core amino acids, and structure factor $S(q)$ define the structure of folded proteins. It is well-known that folded proteins are compact with a radius of gyration $R_g(N) \sim N^ν$ that obeys power-law scaling with the number of amino acids $N$ and $ν\sim 1/3$, $f_{\rm core} \approx 0.09$, and $\langle ϕ\rangle \approx 0.55$. We also investigate the {\it internal} scaling of the radius of gyration $R_g(n)$ versus the chemical separation $n$ between amino acids for subchains of length $n$ and show that it does not obey simple power-law scaling with $ν\sim 1/3$. Instead, $R_g(n) \sim n^{ν_{1,2}}$ with a larger exponent $ν_1 &gt; 1/3$ for small $n$ and smaller exponent $ν_{2} &lt; 1/3$ for large $n$. To develop a minimal model for proteins that recapitulates these defining structural features, we carry out collapse simulations for a series of coarse-grained models with increasing complexity. We show that a model, which coarse-grains amino acids into a single spherical backbone bead and several variable-sized side-chain beads and enforces bend- and dihedral-angle constraints for the backbone, recapitulates $R_g(n)$, $f_{\rm core}$, $\langle ϕ\rangle$, and $S(q)$ for more than $2500$ x-ray crystal structures of proteins.

Joule heating, which softens the particle. As the particle cools to room temperature, the alloy solidifies and the particle recovers its original modulus. To optimize the mechanical response of granular packings containing both soft and stiff particles, we employ an evolutionary algorithm coupled with discrete element method simulations to predict the patterns of particle moduli that will yield specific force outputs on the assembly boundaries. The predicted patterns of particle moduli from the simulations were realized in experiments using quasi-2D assemblies of VM particles and the force outputs on the assembly boundaries were measured using photoelastic techniques. These studies represent a step towards making robotic granular metamaterials that can dynamically adapt their mechanical properties in response to different environmental conditions or perform specific tasks on demand.

→ 0. This work introduces novel particle systems with tunable size and stiffness that can be implemented in particle packings for soft robotic applications.

Abstract Proteins naturally occur in crowded cellular environments and interact with other proteins, nucleic acids, and organelles. Since most previous experimental protein structure determination techniques require that proteins occur in idealized, non‐physiological environments, the effects of realistic cellular environments on protein structure are largely unexplored. Recently, Förster resonance energy transfer (FRET) has been shown to be an effective experimental method for investigating protein structure in vivo. Inter‐residue distances measured in vivo can be incorporated as restraints in molecular dynamics (MD) simulations to model protein structural dynamics in vivo. Since most FRET studies only obtain inter‐residue separations for a small number of amino acid pairs, it is important to determine the minimum number of restraints in the MD simulations that are required to achieve a given root‐mean‐square deviation (RMSD) from the experimental structural ensemble. Further, what is the optimal method for selecting these inter‐residue restraints? Here, we implement several methods for selecting the most important FRET pairs and determine the number of pairs that are needed to induce conformational changes in proteins between two experimentally determined structures. We find that enforcing only a small fraction of restraints, , where is the number of amino acids, can induce the conformational changes. These results establish the efficacy of FRET‐assisted MD simulations for atomic scale structural modeling of proteins in vivo.

Externally driven dense packings of particles can exhibit nonlinear wave phenomena that are not described by effective medium theory or linearized approximate models. Such nontrivial wave responses can be exploited to design sound-focusing/scrambling devices, acoustic filters, and analog computational units. At high amplitude vibrations or low confinement pressures, the effect of nonlinear particle contacts becomes increasingly noticeable, and the interplay of nonlinearity, disorder, and discreteness in the system gives rise to remarkable properties, particularly useful in designing structures with exotic properties. In this paper, we build upon the data-driven methods in dynamical system analysis and show that the Koopman spectral theory can be applied to granular crystals, enabling their phase space analysis beyond the linearizable regime and without recourse to any approximations considered in the previous works. We show that a deep neural network can map the dynamics to a latent space where the essential nonlinearity of the granular system unfolds into a high-dimensional linear space. As a proof of concept, we use data from numerical simulations of a two-particle system and evaluate the accuracy of the trajectory predictions under various initial conditions. By incorporating data from experimental measurements, our proposed framework can directly capture the underlying dynamics without imposing any assumptions about the physics model. Spectral analysis of the trained surrogate system can help bridge the gap between the simulation results and the physical realization of granular crystals and facilitate the inverse design of materials with desired behaviors.

Understanding the dynamic nature of protein structures is essential for comprehending their biological functions. While significant progress has been made in predicting static folded structures, modeling protein motions on microsecond to millisecond scales remains challenging. To address these challenges, we introduce a novel deep learning architecture, Protein Transformer with Scattering, Attention, and Positional Embedding (ProtSCAPE), which leverages the geometric scattering transform alongside transformer-based attention mechanisms to capture protein dynamics from molecular dynamics (MD) simulations. ProtSCAPE utilizes the multi-scale nature of the geometric scattering transform to extract features from protein structures conceptualized as graphs and integrates these features with dual attention structures that focus on residues and amino acid signals, generating latent representations of protein trajectories. Furthermore, ProtSCAPE incorporates a regression head to enforce temporally coherent latent representations.

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Chromatin is a polymer complex of DNA and proteins that regulates gene expression. The three-dimensional (3D) structure and organization of chromatin controls DNA transcription and replication. High-throughput chromatin conformation capture techniques generate Hi-C maps that can provide insight into the 3D structure of chromatin. Hi-C maps can be represented as a symmetric matrix [Formula: see text], where each element represents the average contact probability or number of contacts between chromatin loci i and j. Previous studies have detected topologically associating domains (TADs), or self-interacting regions in [Formula: see text] within which the contact probability is greater than that outside the region. Many algorithms have been developed to identify TADs within Hi-C maps. However, most TAD identification algorithms are unable to identify nested or overlapping TADs and for a given Hi-C map there is significant variation in the location and number of TADs identified by different methods. We develop a novel method to identify TADs, KerTAD, using a kernel-based technique from computer vision and image processing that is able to accurately identify nested and overlapping TADs. We benchmark this method against state-of-the-art TAD identification methods on both synthetic and experimental data sets. We find that the new method consistently has higher true positive rates (TPR) and lower false discovery rates (FDR) than all tested methods for both synthetic and manually annotated experimental Hi-C maps. The TPR for KerTAD is also largely insensitive to increasing noise and sparsity, in contrast to the other methods. We also find that KerTAD is consistent in the number and size of TADs identified across replicate experimental Hi-C maps for several organisms. Thus, KerTAD will improve automated TAD identification and enable researchers to better correlate changes in TADs to biological phenomena, such as enhancer-promoter interactions and disease states.

Breast cancer invasion into adipose tissue strongly influences disease progression and metastasis. The degree of cancer cell invasion into adipose tissue depends on both biochemical signaling and the mechanical properties of cancer cells, adipocytes, and other key components of adipose tissue. We model breast cancer invasion into adipose tissue using discrete element method simulations of active, cohesive spherical particles (cancer cells) invading into confluent packings of deformable polyhedra (adipocytes). We quantify the degree of invasion by calculating the interfacial area At between cancer cells and adipocytes. We determine the long-time value of At vs the activity and strength of the cohesion between cancer cells, as well as the mechanical properties of the adipocytes and extracellular matrix in which adipocytes are embedded. We show that the degree of invasion collapses onto a master curve as a function of the dimensionless energy scale Ec, which grows linearly with the cancer cell velocity persistence time and fluctuations, is inversely proportional to the system pressure, and is offset by the cancer cell cohesive energy. When Ec&amp;gt;1, cancer cells will invade the adipose tissue, whereas for Ec&amp;lt;1, cancer cells and adipocytes remain de-mixed. We also show that At decreases when the adipocytes are constrained by the ECM by an amount that depends on the spatial heterogeneity of the adipose tissue.

Capillary droplets form due to surface tension when two immiscible fluids are mixed. We describe the motion of gravity-driven capillary droplets flowing through narrow constrictions and obstacle arrays in both simulations and experiments. Our new capillary deformable particle model recapitulates the shape and velocity of single oil droplets in water as they pass through narrow constrictions in microfluidic chambers. Using this experimentally validated model, we simulate the flow and clogging of single capillary droplets in narrow channels and obstacle arrays and find several important results. First, the capillary droplet speed profile is nonmonotonic as the droplet exits the narrow orifice, and we can tune the droplet properties so that the speed overshoots the terminal speed far from the constriction. Second, in obstacle arrays, we find that extremely deformable droplets can wrap around obstacles, which leads to decreased average droplet speed in the continuous flow regime and increased probability for clogging in the regime where permanent clogs form. Third, the wrapping mechanism causes the clogging probability in obstacle arrays to become nonmonotonic with surface tension $Γ$. At large $Γ$, the droplets are nearly rigid and the clogging probability is large since the droplets can not squeeze through the gaps between obstacles. With decreasing $Γ$, the clogging probability decreases as the droplets become more deformable. However, in the small-$Γ$ limit the clogging probability increases, since the droplets are extremely deformable and cause clogs as they wrap around the obstacles. The results from these studies are important for developing a predictive understanding of capillary droplet flows through complex and confined geometries.

There is growing interest in engineering unconventional computing devices that leverage the intrinsic dynamics of physical substrates to perform fast and energy-efficient computations. Granular metamaterials are one such substrate that has emerged as a promising platform for building wave-based information processing devices with the potential to integrate sensing, actuation, and computation. Their high-dimensional and nonlinear dynamics result in nontrivial and sometimes counter-intuitive wave responses that can be shaped by the material properties, geometry, and configuration of individual grains. Such highly tunable rich dynamics can be utilized for mechanical computing in special-purpose applications. However, there are currently no general frameworks for the inverse design of large-scale granular materials. Here, we build upon the similarity between the spatiotemporal dynamics of wave propagation in material and the computational dynamics of Recurrent Neural Networks to develop a gradient-based optimization framework for harmonically driven granular crystals. We showcase how our framework can be utilized to design basic logic gates where mechanical vibrations carry the information at predetermined frequencies. We compare our design methodology with classic gradient-free methods and find that our approach discovers higher-performing configurations with less computational effort. Our findings show that a gradient-based optimization method can greatly expand the design space of metamaterials and provide the opportunity to systematically traverse the parameter space to find materials with the desired functionalities.

Previous studies have shown that the interiors of proteins are densely packed, reaching packing fractions that are as large as those found for static packings of individual amino-acid-shaped particles. How can the interiors of proteins take on such high packing fractions given that amino acids are connected by peptide bonds and many amino acids are hydrophobic with attractive interactions? We investigate this question by comparing the structural and mechanical properties of collapsed attractive disk-shaped bead-spring polymers to those of three reference systems: static packings of repulsive disks, of attractive disks, and of repulsive disk-shaped bead-spring polymers. We show that the attractive systems quenched to temperatures below the glass transition T≪T_{g} and static packings of both repulsive disks and bead-spring polymers possess similar interior packing fractions. Previous studies have shown that static packings of repulsive disks are isostatic at jamming onset, i.e., the number of interparticle contacts N_{c} matches the number of degrees of freedom, which strongly influences their mechanical properties. We find that repulsive polymer packings are hypostatic at jamming onset (i.e., with fewer contacts than degrees of freedom) but are effectively isostatic when including stabilizing quartic modes, which give rise to quartic scaling of the potential energy with displacements along these modes. While attractive disk and polymer packings are often considered hyperstatic with excess contacts over the isostatic number, we identify a definition for interparticle contacts for which they can also be considered as effectively isostatic. As a result, we show that the mechanical properties (e.g., scaling of the potential energy with excess contact number and low-frequency contribution to the density of vibrational modes) of weakly attractive disk and polymer packings are similar to those of isostatic repulsive disk and polymer packings. Our results demonstrate that static packings generated via attractive collapse or compression of repulsive particles possess similar structural and mechanical properties.

ABSTRACT Swarming is a macroscopic phenomenon in which surface bacteria organize into a motile population. The flagellar motor that drives swarming in Pseudomonas aeruginosa is powered by stators MotAB and MotCD. Deletion of the MotCD stator eliminates swarming, whereas deletion of the MotAB stator enhances swarming. Interestingly, we measured a strongly asymmetric stator availability in the wild-type (WT) strain, with MotAB stators produced at an approximately 40-fold higher level than MotCD stators. However, utilization of MotCD stators in free swimming cells requires higher liquid viscosities, while MotAB stators are readily utilized at low viscosities. Importantly, we find that cells with MotCD stators are ~10× more likely to have an active motor compared to cells uses the MotAB stators. The spectrum of motility intermittency can either cooperatively shut down or promote flagellum motility in WT populations. In P. aeruginosa , transition from a static solid-like biofilm to a dynamic liquid-like swarm is not achieved at a single critical value of flagellum torque or stator fraction but is collectively controlled by diverse combinations of flagellum activities and motor intermittencies via dynamic stator utilization. Experimental and computational results indicate that the initiation or arrest of flagellum-driven swarming motility does not occur from individual fitness or motility performance but rather related to concepts from the “jamming transition” in active granular matter. IMPORTANCE It is now known that there exist multifactorial influences on swarming motility for P. aeruginosa , but it is not clear precisely why stator selection in the flagellum motor is so important. We show differential production and utilization of the stators. Moreover, we find the unanticipated result that the two motor configurations have significantly different motor intermittencies: the fraction of flagellum-active cells in a population on average with MotCD is active ~10× more often than with MotAB. What emerges from this complex landscape of stator utilization and resultant motor output is an intrinsically heterogeneous population of motile cells. We show how consequences of stator recruitment led to swarming motility and how the stators potentially relate to surface sensing circuitry.

During epithelial wound healing, cell morphology near the healed wound and the healing rate vary strongly among different developmental stages even for a single species like . We develop deformable particle (DP) model simulations to understand how variations in cell mechanics give rise to distinct wound closure phenotypes in the embryonic ectoderm and larval wing disc epithelium. We find that plastic deformation of the cell membrane can generate large changes in cell shape consistent with wound closure in the embryonic ectoderm. Our results show that the embryonic ectoderm is best described by cell membranes with an elasto-plastic response, whereas the larval wing disc is best described by cell membranes with an exclusively elastic response. By varying the mechanical response of cell membranes in DP simulations, we recapitulate the wound closure behavior of both the embryonic ectoderm and the larval wing disc. Published by the American Physical Society 2024

The mechanical properties of CuTi alloys have been characterized extensively through experimental studies. However, a detailed understanding of why the strength of Cu increases after a small fraction of Ti atoms are added to the alloy is still missing. In this work, we address this question using density functional theory (DFT) and molecular dynamics (MD) simulations with the modified embedded atom method (MEAM) interatomic potentials. First, we performed calculations of the uniaxial tension deformations of small bicrystalline Cu cells using DFT static simulations. We then carried out uniaxial tension deformations on much larger bicrystalline and polycrystalline Cu cells by using MEAM MD simulations. In bicrystalline Cu, the inclusion of Ti increases the grain boundary separation energy and the maximum tensile stress. The DFT calculations demonstrate that the increase in the tensile stress can be attributed to an increase in the local charge density arising from Ti. MEAM simulations in larger bicrystalline systems have shown that increasing the Ti concentration decreases the density of the stacking faults. This observation is enhanced in polycrystalline Cu, where the addition of Ti atoms, even at concentrations as low as 1.5 atomic (at.) %, increases the yield strength and elastic modulus of the material compared to pure Cu. Under uniaxial tensile loading, the addition of small amounts of Ti hinders the formation of partial Shockley dislocations in the grain boundaries of Cu, leading to a reduced level of local deformation. These results shed light on the role of Ti in determining the mechanical properties of polycrystalline Cu and enable the engineering of grain boundaries and the inclusion of Ti to improve degradation resistance.

limit, the clogging probability increases since the droplets are extremely deformable and wrap around the obstacles. The results from these studies are important for developing a predictive understanding of capillary droplet flows through complex and confined geometries.

Unconventional computing seeks to develop new means of acting on and interpreting the world. These emerge when new tools and computational substrates are built or discovered, or when existing artifacts are deployed in novel ways. Prior work designed sheets of vibrating particles to achieve mechanical polycomputation, wherein multiple logical operations were physically executed by the same parts at the same time. This works by exploiting the vibrational superposition of particles induced by external drives acting at multiple frequencies. In this paper, we introduce an idea called refractive computation, in which a sufficiently high density of polycomputed logic gates results in parallelized computations across driving frequencies. Parallelized logic gates are split across external drive frequencies in a single simulation, and emerge in the course of polycomputing sequential logic gates.