arXiv subjects

J. M. Schwarz

Publications and source records attributed to J. M. Schwarz.

At least 19 recordsLinked to original sources

arXiv2026

Collective Cell Fluidity Controls Active Prestress Transmission in Cell-Extracellular-Matrix Tissues

Tissues are active composites in which multicellular collectives and extracellular matrices mechanically reorganize one another. We develop a three-dimensional micromechanical model that couples deformable, rearranging cell clusters to a disordered network of semiflexible fibers through a dynamic, force-generating interface. Cell clusters are represented as solid-like or fluid-like vertex-model spheroids and coupled to the matrix by passive or contractile linkers renewed as the cluster boundary reorganizes. Matched intact, voided, and passive-linker controls separate cavity formation, interfacial tethering, and active loading. At small strain, passive tethering provides modest reinforcement, whereas active contraction prestresses and strongly stiffens the matrix. Solid-like clusters preserve coherent force transmission and exhibit an excess modulus scaling approximately as $|σ|^{1.4}$ across changes in activity, cluster size, and cluster number. Fluid-like clusters undergo greater interfacial renewal, producing weaker and nonmonotonic coupling between prestress and stiffness. Increasing cluster number produces collective stiffening when prestressed regions become connected through sufficiently persistent interfaces. At large strain, both solid-like and fluid-like systems approach the corresponding voided-network response as the residual fiber backbone becomes mechanically dominant. Thus, cell-generated prestress controls macroscopic stiffness only together with the organization and persistence of its transmission across the cell-matrix interface.

cond-mat.soft↗

arXiv2026

Active Particles Imprint Persistent Percolating Networks in Polymer Condensates

Fluid condensates readily exchange components and reorganize, and in doing so typically erase structural history. Using simulations of sticker-spacer polymers in an active particle bath, we show that activity drives condensates from compact droplets into system-spanning percolated networks by enhancing interchain connectivity, suppressing intrachain collapse, and increasing topological constraints through interchain winding. The network persists after the active particles are removed, despite continued polymer exchange and contact turnover, revealing a fluid-like state with activity-induced topological imprinting. Hence, activity can write long-lived structural organization and memory into fluid condensates.

cond-mat.soft↗

arXiv2026

Perturbative Contrastive Physical Learning

Responses to perturbations are key to understanding physical systems. The ability to contrast such responses by comparing how a system reacts under slightly different conditions provides a mechanism for learning. Here, we introduce Perturbative Contrastive Physical Learning (PCPL), a general framework in which learning emerges from measurable contrasts between physical states produced by controlled changes to inputs, boundary conditions, parameters, or interpreter functions. PCPL unifies and extends prior approaches: Equilibrium Propagation is rooted in contrasts between free and nudged equilibria in energy-based systems, while Frequency Propagation corresponds to contrasts extracted from sinusoidally driven, frequency-demodulated responses. We show that contrast-driven updates can reflect either local sensitivities or global inverse-problem structure, yet do not require centralized gradient computation. Instead, effective learning geometry emerges implicitly from the system's own physical response, allowing learning behavior to arise without an external processor or explicit backpropagation. We demonstrate PCPL in two platforms: (i) spring networks that update bond stiffness using measured displacements and forces, and (ii) continuous-variable photonic circuits trained via x quadrature measurements and finite-difference estimates of the Jacobian. Both platforms successfully learn classification tasks. We further show that a continuous-variable photonic circuit can be trained to implement analog multiplication, illustrating a step toward more autonomous physical learning systems.

cs.LG↗

arXiv2026

Training cell stress patterns in 3D cellular packings

The task of learning patterns is typically associated with systems that update parameters on fixed architectures, such as neural networks, where learning proceeds through continuous optimization. Here, we demonstrate that pattern learning can also emerge in reconfigurable cellular tissue, where both mechanical parameters and network topology evolve. Using a three-dimensional vertex model, we show that cellular packings can be trained to realize prescribed cell stress patterns through a contrastive learning algorithm to update hidden-cell shape indices. We find that learning is intrinsically collective, requiring coordinated, system-wide parameter adjustments, with learnability governed by an interplay between mechanical state, capacity, and training protocol. In particular, the rigidity of the tissue controls an effective exploration-exploitation tradeoff: fluid-like regimes enhance exploration through cellular rearrangements, while rigid regimes constrain dynamics and favor exploitation of existing configurations. These rearrangements introduce discontinuous learning dynamics, enabling the system to transition between distinct local minima in the cost function landscape. As the ratio of target cells to the total number cells in the packing or constraint load increases, learning becomes slower, more heterogeneous, and increasingly dependent on rare rearrangements that allow escape from geometrically constrained states. Finally, training cells in sequence, in contrast to parallel protocols, provides an alternative route that can be more robust but generally takes longer to train for the constraint loads studied. These results suggest a learning phase diagram governed by constraint load, cell packing rigidity, and training protocol. By enabling the training of localized internal states, this work positions tissues not only as adaptive materials, but as nonconventional AI platforms.

cond-mat.dis-nn↗

arXiv2026

Learning Associations in Reconfigurable Particle Packings via Local Cyclic Driving

We investigate associative-memory behavior in a reconfigurable particle packing programmed by purely local cyclic driving. The system is a two-dimensional bidisperse Lennard--Jones particle assembly with periodic boundaries evolved under athermal quasistatic relaxation. During training, a fixed set of input particles is driven cyclically while output particles are selected on-the-fly by a region-driving rule and driven according to a prescribed flow pattern; during retrieval, only the inputs are driven. Associative-memory performance is quantified by the cosine similarity between realized and target output displacement directions. Unlike physical learning systems with fixed architecture, learning here arises through emergent weight updates: localized rearrangements modify the contact network and reshape the effective mechanical couplings between inputs and outputs. Across task difficulty we identify three regimes. In an easy setting, the intrinsic mechanical response already produces coherent motion in the right-hand region under input-only driving, yielding high performance without training. In a hard setting, the desired mapping conflicts with the dominant collective drift, resulting in low baseline performance and only modest training gains; introducing intermittent relaxation cycles reduces train--retrieval mismatch and improves performance. In an intermediate quadrupolar task, repositioning the input--output geometry stabilizes the desired response and converts initially stochastic trajectories into reproducible learned motions. Together these results identify minimal physical ingredients for association-based functionality in athermally driven particulate media and motivate an association learning phase diagram for reconfigurable matter.

cond-mat.dis-nn↗

arXiv2026

Laser interferometry as a robust neuromorphic platform for machine learning

We present a method for implementing an optical neural network using only linear optical resources, namely field displacement and interferometry applied to coherent states of light. The nonlinearity required for learning in a neural network is realized via an encoding of the input into phase shifts allowing for far more straightforward experimental implementation compared to previous proposals for, and demonstrations of, $\textit{in situ}$ inference. Beyond $\textit{in situ}$ inference, the method enables $\textit{in situ}$ training by utilizing established techniques like parameter shift methods or physical backpropagation to extract gradients directly from measurements of the linear optical circuit. We also investigate the effect of photon losses and find the model to be very resilient to these.

physics.optics↗

arXiv2026

Cell strain-stiffening drives cell breakout from embedded spheroids

Understanding how cells escape from embedded spheroids requires a mechanical framework linking stress generation within cells, across cells, and between cells and the surrounding extracellular matrix (ECM). We develop such a framework by coupling a 3D vertex model of a spheroid to a fibrous ECM network and deriving a 3D Cauchy stress tensor for deformable polyhedral cells, enabling direct cell-level stress quantification in three dimensions. We analyze maximum shear stress in solid-like and fluid-like spheroids: solid-like spheroids exhibit broader stress distributions and radial stress gradients, while fluid-like spheroids show lower stresses with weak spatial organization. Cell shape anisotropy is not generically aligned with principal stress directions, indicating that morphology alone is an unreliable proxy for mechanical state. We further demonstrate strain stiffening at the single-cell level, where elongation produces nonlinear increases in maximum shear stress, allowing boundary cells in otherwise low-stress, fluid-like spheroids to transiently generate forces sufficient to remodel the matrix. To connect strain-induced stress amplification to invasion modes, we introduce an extended 3D vertex model with explicit, tunable cell-cell adhesion springs. In this minimal mechanical framework, single-cell breakout results from strain stiffening combined with reduced adhesion, whereas multi-cell streaming additionally requires anisotropic adhesion strengthened along the elongation axis and weakened orthogonally. Together, these results identify distinct mechanical pathways coupling cell strain, stress amplification, and adhesion organization to spheroid invasion.

physics.bio-ph↗

arXiv2025

Differential Crosslinking and Contractile Motors Drive Nuclear Chromatin Compaction

During interphase, a typical cell nucleus features spatial compartmentalization of transcriptionally active euchromatin and repressed heterochromatin domains. In conventional nuclear organization, euchromatin predominantly occupies the nuclear interior, while heterochromatin, which is approximately 50% more dense than euchromatin, is positioned near the nuclear periphery. Peripheral chromatin organization can be further modulated by the nuclear lamina, which is itself a deformable structure. While a number of biophysical mechanisms for compartmentalization within rigid nuclei have been explored, we study a chromatin model consisting of an active, crosslinked polymer tethered to a deformable, polymeric lamina shell. Contractile motors, the deformability of the shell, and the spatial distribution of crosslinks all play pivotal roles in this compartmentalization. We find that a radial crosslink density distribution, even with a small linear differential of higher crosslinking density at the edge of the nucleus, combined with contractile motor activity, drives genomic segregation, in agreement with experimental observations. This arises from contractile motors preferentially drawing crosslinks into their vicinity at the nuclear periphery, forming high-density domains that promote heterochromatin formation. We also find an increased stiffness of nuclear wrinkles given the preferential heterochromatin compaction below the lamina shell, which is consistent with instantaneous nuclear stiffening under applied nanoindentation. We conclude with the potential for experimental validation of our model predictions.

cond-mat.soft↗

arXiv2025

How does an embedded spheroid affect the rigidity of extracellular matrix?

In vitro collagen networks and in silico fiber network models are typically used to represent extracellular matrix in tissues. Such networks exhibit the phenomenon of strain-stiffening, or an increase in elastic modulus with increasing strain, both under isotropic expansion and shear. However, the deformations induced in an extracellular matrix environment in the presence of a cellular aggregate are more complex, due to the irregularity of the tissue-environment interface, the mechanisms of force transmission between the tissue and the environment, and the rheology of the tissue itself. Therefore, using a two-dimensional vertex model of a tissue coupled to a surrounding spring network model, both of which can undergo rigidity transitions, we investigate the effects of a cellular aggregate on the rigidity of its environment. We find that the network's rigidity transition alone is sensitive to tissue size, mechanical properties, and surface tension. This sensitivity can, in part, be analytically estimated using a mean-field constraint counting approach to arrive at an effective spring network coordination number to determine how the network rigidity transition location shifts in the presence of the tissue spheroid. Moreover, we find that it is energetically favorable to create a ring of high-tension boundary cells in the tissue spheroid as the spring network rigidifies, as opposed to creating a string of high-tension cells through the bulk. We also find that increasing interfacial tension of the tissue spheroid facilitates rigidity in the spring network. In sum, our numerical and analytical results help reveal the complex mechanical interplay between a tissue spheroid and its surrounding environment.

physics.bio-ph↗

arXiv2025

How human-derived brain organoids are built differently from brain organoids derived from genetically-close relatives: A multi-scale hypothesis

How genes affect tissue scale organization remains a longstanding biological puzzle. As experimental efforts aim to quantify gene expression, chromatin organization, cellular structure, and tissue structure, computational modeling lags behind. To address this gap, we merge a cellular-based tissue model with a nuclear model that includes a deformable lamina shell and chromatin to test multiscale hypotheses linking chromatin and tissue scales. We propose a multiscale hypothesis focusing on brain organoids to explain structural differences between brain organoids built from induced-pluripotent human stem cells and induced-pluripotent gorilla and chimpanzee cells. Recent experiments discover that a cell fate transition from neuroepithelial to radial glial cells includes a new intermediate state delayed in human organoids, which narrows and lengthens cells on the apical side. Experiments show that the transcription factor ZEB2 plays a major role in the emergence of this intermediate state with ZEB2 mRNA levels peaking. We postulate that the enhancement of ZEB2 expression is potentially due to chromatin reorganization in response to mechanical deformations of the nucleus. A larger critical mechanical strain triggers reorganization in human-derived stem cells, causing delayed ZEB2 upregulation compared with genetically close relatives. We test this by exploring how slightly different initial configurations of chromatin reorganize under applied strain, with greater representing less genetically-close relatives. We find that larger configuration discrepancies produce increased differences in the magnitude of chromatin displacement that rise faster than linearly yet slower than exponentially. Changes in chromatin strain and contact maps can reveal species-specific differences, aiding our understanding of how one species differs in structure from another.

q-bio.TO↗

arXiv2024

Rigidity condition for gluing two bar-joint rigid graphs embedded in $\mathbb{R}^d$

How does one determine if a collection of bars joined by freely rotating hinges cannot be deformed without changing the length of any of the bars? In other words, how does one determine if a bar-joint graph is rigid? This question has been definitively answered using combinatorial rigidity theory in two dimensions via the Geiringer-Laman Theorem. However, it has not yet been answered using combinatorial rigidity theory in higher dimensions, given known counterexamples to the trivial dimensional extension of the Geiringer-Laman Theorem. To work towards a combinatorial approach in dimensions beyond two, we present a theorem for gluing two rigid bar-joint graphs together that remain rigid. When there are no overlapping vertices between the two graphs, the theorem reduces to Tay's theorem used to identify rigidity in body-bar graphs. When there are overlapping vertices, we rely on the notion of pinned rigid graphs to identify and constrain rigid motions. This theorem provides a basis for an algorithm for recursively constructing rigid clusters that can be readily adapted for computational purposes. By leveraging Henneberg-type operations to grow a rigid (or minimally rigid) graph and treating simplices-where every vertex connects to every other vertex-as fundamental units, our approach offers a scalable solution with computational complexity comparable to traditional methods. Thus, we provide a combinatorial blueprint for algorithms in multi-dimensional rigidity theory as applied to bar-joint graphs.

cond-mat.dis-nn↗

arXiv2024

A two-dimensional vertex model for curvy cell-cell interfaces at the subcellular scale

Cross-sections of cell shapes in a tissue monolayer typically resemble a tiling of convex polygons. Yet, examples exist where the polygons are not convex with curved cell-cell interfaces, as seen in the adaxial epidermis. To date, two-dimensional vertex models predicting the structure and mechanics of cell monolayers have been mostly limited to convex polygons. To overcome this limitation, we introduce a framework to study curvy cell-cell interfaces at the subcellular scale within vertex models by using a parameterized curve between vertices that is expanded in a Fourier series and whose coefficients represent additional degrees of freedom. This extension to non-convex polygons allows for cells with same shape index, or dimensionless perimeter, to be, for example, either elongated or globular with lobes. In the presence of applied, anisotropic stresses, we find that local, subcellular curvature, or buckling, can be energetically more favorable than larger scale deformations involving groups of cells. Inspired by recent experiments, we also find that local, subcellular curvature at cell-cell interfaces emerges in a group of cells in response to the swelling of additional cells surrounding the group. Our framework, therefore, can account for a wider array of multi-cellular responses to constraints in the tissue environment.

physics.bio-ph↗

arXiv2024

Enhanced extracellular matrix remodeling due to embedded spheroid fluidization

Tumor spheroids are in vitro three-dimensional, cellular collectives consisting of cancerous cells. Embedding these spheroids in an in vitro fibrous environment, such as a collagen network, to mimic the extracellular matrix (ECM) provides an essential platform to quantitatively investigate the biophysical mechanisms leading to tumor invasion of the ECM. To understand the mechanical interplay between tumor spheroids and the ECM, we computationally construct and study a three-dimensional vertex model for a tumor spheroid that is mechanically coupled to a cross-linked network of fibers. In such a vertex model, cells are represented as deformable polyhedrons that share faces. Some fraction of the boundary faces of the tumor spheroid contain linker springs connecting the center of the boundary face to the nearest node in the fiber network. As these linker springs actively contract, the fiber network remodels. By toggling between fluid-like and solid-like spheroids via changing the dimensionless cell shape index, we find that the spheroid rheology affects the remodeling of the fiber network. More precisely, fluid-like spheroids displace the fiber network more on average near the vicinity of the spheroid than solid-like spheroids. We also find more densification of the fiber network near the spheroid for the fluid-like spheroids. These spheroid rheology-dependent effects are the result of cellular motility due to active cellular rearrangements that emerge over time in the fluid-like spheroids to generate spheroid shape fluctuations. Our results uncover intricate morphological-mechanical interplay between an embedded spheroid and its surrounding fiber network with both spheroid contractile strength and spheroid shape fluctuations playing important roles in the pre-invasion stages of tumor invasion.

physics.bio-ph↗

arXiv2023

Emergent learning in physical systems as feedback-based aging in a glassy landscape

By training linear physical networks to learn linear transformations, we discern how their physical properties evolve due to weight update rules. Our findings highlight a striking similarity between the learning behaviors of such networks and the processes of aging and memory formation in disordered and glassy systems. We show that the learning dynamics resembles an aging process, where the system relaxes in response to repeated application of the feedback boundary forces in presence of an input force, thus encoding a memory of the input-output relationship. With this relaxation comes an increase in the correlation length, which is indicated by the two-point correlation function for the components of the network. We also observe that the square root of the mean-squared error as a function of epoch takes on a non-exponential form, which is a typical feature of glassy systems. This physical interpretation suggests that by encoding more detailed information into input and feedback boundary forces, the process of emergent learning can be rather ubiquitous and, thus, serve as a very early physical mechanism, from an evolutionary standpoint, for learning in biological systems.

cond-mat.dis-nn↗

arXiv2023

Rectification of Random Walkers Induced by Energy Flow at Boundaries

We explore rectification phenomena in a system where two-dimensional random walkers interact with a funnel-shaped ratchet under two distinct classes of reflection rules. The two classes include the angle of reflection exceeding the angle of incidence ($θ_{reflect} > θ_{incident}$), or vice versa ($θ_{reflect} < θ_{incident}$). These generalized boundary reflection rules are indicative of non-equilibrium conditions due to the introduction of energy flows at the boundary. Our findings reveal that the nature of such particle-wall interactions dictates the system's behavior: the funnel either acts as a pump, directing flow, or as a collector, demonstrating a ratchet reversal. Importantly, we provide a geometric proof elucidating the underlying mechanism of rectification, thereby offering insights into why certain interactions lead to directed motion, while others do not.

cond-mat.stat-mech↗

arXiv2023

A slime mold inspired local adaptive mechanism for flow networks

In the realm of biological flow networks, the ability to dynamically adjust to varying demands is paramount. Drawing inspiration from the remarkable adaptability of Physarum polycephalum, we present a novel physical mechanism tailored to optimize flow networks. Central to our approach is the principle that each network component -- specifically, the tubes -- harnesses locally available information to collectively minimize a global cost function. Our findings underscore the scalability of this mechanism, making it feasible for larger, more complex networks. We construct a comprehensive phase diagram, pinpointing the specific network parameters under which successful adaptation, or tuning, is realized. There exists a phase boundary in the phase diagram, revealing a distinct satisfiability-unsatisfiability (SAT-UNSAT) phase transition delineating successful and unsuccessful adaptation.

cond-mat.soft↗

arXiv2023

Mean field elastic moduli of a three-dimensional cell-based vertex model

The mechanics of a foam typically depends on the bubble geometry, topology, and the material at hand, be it metallic or polymeric, for example. While the foam energy functional for each bubble is typically minimization of surface area for a given volume, biology provides us with a wealth of additional energy functionals, should one consider biological cells as a foam-like material. Here, we focus on a mean field approach to obtain the elastic moduli, within linear response, for an ordered, three-dimensional vertex model using the space-filling shape of a truncated octahedron and whose energy functional is characterized by a restoring surface area spring and a restoring volume spring. The tuning of the three-dimensional shape index exhibits a rigidity transition via a compatible-incompatible transition. Specifically, for smaller shape indices, both the target surface area and volume cannot be achieved, while beyond some critical value of the three-dimensional shape index, they can be, resulting in a zero-energy state. As the elastic moduli depend on curvatures of the energy when the system, we obtain these as well. In addition to analytically determining the location of the transition in mean field, we find that the rigidity transition and the elastic moduli depend on the parameterization of the cell shape with this effect being more pronounced in three dimensions given the array of shapes that a polyhedron can take on (as compared to a polygon). We also uncover nontrivial dependence on the deformation protocol in which some deformations result in affine motion of the vertices, while others result in nonaffine motion. Such dependencies on the shape parameterization and deformation protocol give rise to a nontrivial shape landscape and, therefore, nontrivial mechanical response even in the absence of topology changes.

cond-mat.soft↗

arXiv2023

How cells wrap around virus-like particles using extracellular filamentous protein structures

Nanoparticles, such as viruses, can enter cells via endocytosis. During endocytosis, the cell surface wraps around the nanoparticle to effectively eat it. Prior focus has been on how nanoparticle size and shape impacts endocytosis. However, inspired by the noted presence of extracellular vimentin affecting viral and bacteria uptake, as well as the structure of coronaviruses, we construct a computational model in which both the cell-like construct and the virus-like construct contain filamentous protein structures protruding from their surfaces. We then study the impact of these additional degrees of freedom on viral wrapping. We find that cells with an optimal density of filamentous extracellular components (ECCs) are more likely to be infected as they uptake the virus faster and use relatively less cell surface area per individual virus. At the optimal density, the cell surface folds around the virus, and folds are faster and more efficient at wrapping the virus than crumple-like wrapping. We also find that cell surface bending rigidity helps generate folds, as bending rigidity enhances force transmission across the surface. However, changing other mechanical parameters, such as the stretching stiffness of filamentous ECCs or virus spikes, can drive crumple-like formation of the cell surface. We conclude with the implications of our study on the evolutionary pressures of virus-like particles, with a particular focus on the cellular microenvironment that may include filamentous ECCs.

cond-mat.soft↗