arXiv subjects

Chase P. Broedersz

Publications and source records attributed to Chase P. Broedersz.

At least 19 recordsLinked to original sources

arXiv2026

Inferring the dynamics of underdamped stochastic systems

Many complex systems, ranging from migrating cells to animal groups, exhibit stochastic dynamics described by the underdamped Langevin equation. Inferring such an equation of motion from experimental data can provide profound insight into the physical laws governing the system. Here, we derive a principled framework to infer the dynamics of underdamped stochastic systems from realistic experimental trajectories, sampled at discrete times and subject to measurement errors. This framework yields an operational method, Underdamped Langevin Inference (ULI), which performs well on experimental trajectories of single migrating cells and in complex high-dimensional systems, including flocks with Viscek-like alignment interactions. Our method is robust to experimental measurement errors, and includes a self-consistent estimate of the inference error.

physics.bio-ph↗

arXiv2026

Inferring three-body interactions in cell migration dynamics

In active matter and living matter, such as clusters of migrating cells, collective dynamics emerges from the underlying interactions. A common assumption of theoretical descriptions of collective cell migration is that these interactions are pairwise additive. It remains unclear, however, if the dynamics of groups of cells is solely determined by pairwise interactions, or if higher-order interaction terms come into play. To investigate this question, we use time-lapse microscopy to record the dynamics of three cells interacting together in a linear three-site geometry. We collect a large number of cellular trajectories and develop an inference scheme to infer both pairwise and potential three-body cell-cell interactions. Our results reveal evidence of three-body interactions in one of the two cell lines tested. However, these three-body interactions only introduce minor corrections to the overall dynamics. Our work provides a methodology to infer the existence of three-body interactions from trajectory data, and supports the commonly assumed pairwise nature of cell-cell interactions.

physics.bio-ph↗

arXiv2024

Data-driven theory reveals protrusion and polarity interactions governing collision behavior of distinct motile cells

The migration behavior of colliding cells is critically determined by transient contact-interactions. During these interactions, the motility machinery, including the front-rear polarization of the cell, dynamically responds to surface protein-mediated transmission of forces and biochemical signals between cells. While biomolecular details of such contact-interactions are increasingly well understood, it remains unclear what biophysical interaction mechanisms govern the cell-level dynamics of colliding cells and how these mechanisms vary across cell types. Here, we develop a phenomenological theory based on 14 candidate contact-interaction mechanisms coupling cell position, protrusion, and polarity. Using high-throughput micropattern experiments, we detect which of these phenomenological contact-interactions captures the interaction behaviors of cells. We find that various cell types - ranging from mesenchymal to epithelial cells - are accurately captured by a single model with only two interaction mechanisms: polarity-protrusion coupling and polarity-polarity coupling. The qualitatively different interaction behaviors of distinct cells, as well as cells subject to molecular perturbations of surface protein-mediated signaling, can all be quantitatively captured by varying the strength and sign of the polarity-polarity coupling mechanism. Altogether, our data-driven phenomenological theory of cell-cell interactions reveals polarity-polarity coupling as a versatile and general contact-interaction mechanism, which may underlie diverse collective migration behavior of motile cells.

physics.bio-ph↗

arXiv2023

Physical models of bacterial chromosomes

The interplay between bacterial chromosome organization and functions such as transcription and replication can be studied in increasing detail using novel experimental techniques. Interpreting the resulting quantitative data, however, can be theoretically challenging. In this minireview, we discuss how connecting experimental observations to biophysical theory and modeling can give rise to new insights on bacterial chromosome organization. We consider three flavors of models of increasing complexity: simple polymer models that explore how physical constraints, such as confinement or plectoneme branching, can affect bacterial chromosome organization; bottom-up mechanistic models that connect these constraints to their underlying causes, for instance chromosome compaction to macromolecular crowding, or supercoiling to transcription; and finally, data-driven methods for inferring interpretable and quantitative models directly from complex experimental data. Using recent examples, we discuss how biophysical models can both deepen our understanding of how bacterial chromosomes are structured, and give rise to novel predictions about bacterial chromosome organization.

physics.bio-ph↗

arXiv2023

Geometry-sensitive protrusion growth directs confined cell migration

The migratory dynamics of cells can be influenced by the complex micro-environment through which they move. It remains unclear how the motility machinery of confined cells responds and adapts to their micro-environment. Here, we propose a biophysical mechanism for a geometry-dependent coupling between cellular protrusions and the nucleus that leads to directed migration. We apply our model to geometry-guided cell migration to obtain insights into the origin of directed migration on asymmetric adhesive micro-patterns and the polarization enhancement of cells observed under strong confinement. Remarkably, for cells that can choose between channels of different size, our model predicts an intricate dependence for cellular decision making as a function of the two channel widths, which we confirm experimentally.

physics.bio-ph↗

arXiv2023

Learning dynamical models of single and collective cell migration: a review

Single and collective cell migration are fundamental processes critical for physiological phenomena ranging from embryonic development and immune response to wound healing and cancer metastasis. To understand cell migration from a physical perspective, a broad variety of models for the underlying physical mechanisms that govern cell motility have been developed. A key challenge in the development of such models is how to connect them to experimental observations, which often exhibit complex stochastic behaviours. In this review, we discuss recent advances in data-driven theoretical approaches that directly connect with experimental data to infer dynamical models of stochastic cell migration. Leveraging advances in nanofabrication, image analysis, and tracking technology, experimental studies now provide unprecedented large datasets on cellular dynamics. In parallel, theoretical efforts have been directed towards integrating such datasets into physical models from the single cell to the tissue scale with the aim of conceptualizing the emergent behaviour of cells. We first review how this inference problem has been addressed in both freely migrating and confined cells. Next, we discuss why these dynamics typically take the form of underdamped stochastic equations of motion, and how such equations can be inferred from data. We then review applications of data-driven inference and machine learning approaches to heterogeneity in cell behaviour, subcellular degrees of freedom, and to the collective dynamics of multicellular systems. Across these applications, we emphasize how data-driven methods can be integrated with physical active matter models of migrating cells, and help reveal how underlying molecular mechanisms control cell behaviour.

physics.bio-ph↗

arXiv2022

Nonlinear mechanosensation in fiber networks

In a diversity of physiological contexts, eukaryotic cells adhere to an extracellular matrix (ECM), a disordered network with complex nonlinear mechanics. Such cells can perform mechanosensation: using local force probing they can measure and respond to their substrate's mechanical properties. It remains unclear, however, how the mechanical complexity of the ECM at the cellular scale impacts mechanosensation. Here, we investigate the physical limits of mechanosensation imposed by the inherent structural disorder and nonlinear elastic response of the ECM. Using a theoretical framework for disordered fiber networks, we find that the extreme mechanical heterogeneity that cells can locally sense with small probing forces is strongly reduced with increasing force. Specifically, we predict that the accuracy of mechanosensation dramatically improves with force, following a universal power law insensitive to constitutive details, which we quantitatively confirm using microrheology experiments in collagen and fibrin gels. We provide conceptual insights into this behavior by introducing a general model for nonlinear mechanosensation, based on the idea of an emergent nonlinear length-scale associated with fiber buckling. This force-dependent length-scale enhances the range over which local mechanical measurements are performed, thereby averaging the response of a disordered network over an enlarged region. We show with an example how a cell can use this nonlinear mechanosensation to infer the macroscopic mechanical properties of a disordered ECM using local measurements. Together, our results demonstrate that cells can take advantage of the inherent nonlinearity of fibrous networks to robustly sense, control, and respond to their mechanical environment.

cond-mat.soft↗

arXiv2022

Local Nonlinear Elastic Response of Extracellular Matrices

Nonlinear stiffening is a ubiquitous property of major types of biopolymers that make up the extracellular matrices (ECM) including collagen, fibrin and basement membrane. Within the ECM, many types of cells such as fibroblasts and cancer cells are known to mechanically stretch their surroundings that locally stiffens the matrix. Although the bulk nonlinear elastic behaviors of these biopolymer networks are well studied, their local mechanical responses remain poorly characterized. Here, to understand how a living cell feels the nonlinear mechanical resistance from the ECM, we mimic the cell-applied local force using optical tweezers; we report that the local stiffening responses in highly nonlinear ECM are significantly weaker than responses found in bulk rheology, across two orders of magnitude of the locally applied force since the onset of stiffening. With a minimal model, we show that a local point force application can induce a stiffened region in the matrix, which expands with increasing magnitude of the point force. Furthermore, we show that this stiffened region behaves as an effective probe upon local loading. The local nonlinear elastic response can be attributed to the nonlinear growth of this effective probe that linearly deforms an increasing portion of the matrix.

cond-mat.soft↗

arXiv2022

Geometry adaptation of protrusion and polarity dynamics in confined cell migration

Cell migration in confining physiological environments relies on the concerted dynamics of several cellular components, including protrusions, adhesions with the environment, and the cell nucleus. However, it remains poorly understood how the dynamic interplay of these components and the cell polarity determine the emergent migration behavior at the cellular scale. Here, we combine data-driven inference with a mechanistic bottom-up approach to develop a model for protrusion and polarity dynamics in confined cell migration, revealing how the cellular dynamics adapt to confining geometries. Specifically, we use experimental data of joint protrusion-nucleus migration trajectories of cells on confining micropatterns to systematically determine a mechanistic model linking the stochastic dynamics of cell polarity, protrusions, and nucleus. This model indicates that the cellular dynamics adapt to confining constrictions through a switch in the polarity dynamics from a negative to a positive, self-reinforcing feedback loop. Our model further reveals how this feedback loop leads to stereotypical cycles of protrusion-nucleus dynamics that drive the migration of the cell through constrictions. These cycles are disrupted upon perturbation of cytoskeletal components, indicating that the positive feedback is controlled by cellular migration mechanisms. Our data-driven theoretical approach therefore identifies polarity feedback adaptation as a key mechanism in confined cell migration.

physics.bio-ph↗

arXiv2021

Irreversibility in linear systems with colored noise

Time-irreversibility is a distinctive feature of non-equilibrium dynamics and several measures of irreversibility have been introduced to assess the distance from thermal equilibrium of a stochastically driven system. While the dynamical noise is often approximated as white, in many real applications the time correlations of the random forces can actually be significantly long-lived compared to the relaxation times of the driven system. We analyze the effects of temporal correlations in the noise on commonly used measures of irreversibility and demonstrate how the theoretical framework for white noise driven systems naturally generalizes to the case of colored noise. Specifically, we express the auto-correlation function, the area enclosing rates, and mean phase space velocity in terms of solutions of a Lyapunov equation and in terms of their white noise limit values.

cond-mat.stat-mech↗

arXiv2021

Curvature induces active velocity waves in rotating multicellular spheroids

The multicellular organization of diverse systems, such as embryos, intestines and tumours, relies on the coordinated migration of cells in 3D curved environments. In these settings, cells establish supracellular patterns of motion, including collective rotation and invasion. While such collective modes are increasingly well understood in 2D flat systems, the consequences of geometrical and topological constraints on collective cell migration in 3D curved tissues are largely unknown. Here, we study 3D collective migration in mammary cell spheroids, which represent a common and conceptually simple curved geometry. We discover that these rotating spheroids exhibit a collective mode of cell migration in the form of a velocity wave propagating along the equator with a wavelength equal to the spheroid perimeter. This wave is accompanied by a pattern of incompressible cellular flow across the spheroid surface featuring topological defects, as dictated by the closed spherical topology. Using a minimal active particle model, we reveal that this collective mode originates from the active flocking behaviour of a cell layer confined to a curved surface. Our results thus identify curvature-induced velocity waves as a generic mode of collective cell migration, which could impact the dynamical organization of 3D curved tissues.

physics.bio-ph↗

arXiv2021

Non-linear stress relaxation of transient-crosslinked biopolymer networks

A long standing puzzle in the rheology of living cells is the origin of the experimentally observed long time stress relaxation. The mechanics of the cell is largely dictated by the cytoskeleton, which is a biopolymer network consisting of transient crosslinkers, allowing for stress relaxation over time. Moreover, these networks are internally stressed due to the presence of molecular motors. In this work we propose a theoretical model that uses a mode-dependent mobility to describe the stress relaxation of such prestressed transient networks. Our theoretical predictions agree favorably with experimental data of reconstituted cytoskeletal networks and may provide an explanation for the slow stress relaxation observed in cells.

physics.bio-ph↗

arXiv2021

Theory of Active Intracellular Transport by DNA-relaying

The spatiotemporal organization of bacterial cells is crucial for the active segregation of replicating chromosomes. In several species, including Caulobacter crescentus, the ATPase ParA binds to DNA and forms a gradient along the long cell axis. The ParB partitioning complex on the newly replicated chromosome translocates up this ParA gradient, thereby contributing to chromosome segregation. A DNA-relay mechanism - deriving from the elasticity of the fluctuating chromosome - has been proposed as the driving force for this cargo translocation, but a mechanistic theoretical description remains elusive. Here, we propose a minimal model to describe force generation by the DNA-relay mechanism over a broad range of operational conditions. Conceptually, we identify four distinct force-generation regimes characterized by their dependence on chromosome fluctuations. These relay force regimes arise from an interplay of the imposed ParA gradient, chromosome fluctuations, and an emergent friction force due chromosome-cargo interactions.

physics.bio-ph↗

arXiv2020

Learning the dynamics of cell-cell interactions in confined cell migration

The migratory dynamics of cells in physiological processes, ranging from wound healing to cancer metastasis, rely on contact-mediated cell-cell interactions. These interactions play a key role in shaping the stochastic trajectories of migrating cells. While data-driven physical formalisms for the stochastic migration dynamics of single cells have been developed, such a framework for the behavioral dynamics of interacting cells still remains elusive. Here, we monitor stochastic cell trajectories in a minimal experimental cell collider: a dumbbell-shaped micropattern on which pairs of cells perform repeated cellular collisions. We observe different characteristic behaviors, including cells reversing, following and sliding past each other upon collision. Capitalizing on this large experimental data set of coupled cell trajectories, we infer an interacting stochastic equation of motion that accurately predicts the observed interaction behaviors. Our approach reveals that interacting non-cancerous MCF10A cells can be described by repulsion and friction interactions. In contrast, cancerous MDA-MB-231 cells exhibit attraction and anti-friction interactions, promoting the predominant relative sliding behavior observed for these cells. Based on these experimentally inferred interactions, we show how this framework may generalize to provide a unifying theoretical description of the diverse cellular interaction behaviors of distinct cell types.

physics.bio-ph↗

arXiv2020

Resolving the degree of order in the bacterial chromosome using a statistical physics approach

While bacterial chromosomes were long thought to be amorphous, recent experiments reveal pronounced organizational features. However, the extent of bacterial chromosome organization remains unclear. Here, we develop a fully data-driven maximum entropy approach to extract the distribution of single-cell chromosome conformations from experimental normalized Hi-C data. We apply this inference to the model organism Caulobacter crescentus. On small genomic scales of 104-105 basepairs, our model reveals a pattern of local chromosome extensions that correlates with transcriptional and DNA loop extrusion activity. On larger genomic scales, we find that chromosome structure is predominantly present along the long cell axis: chromosomal loci not only have well-defined axial positions, they also exhibit long-ranged correlations due interacting large emergent genomic clusters, termed Super Domains. Finally, our model reveals information contained in chromosome structure that can guide cellular processes. Our approach can be generalized to other species, providing a principled way of analyzing spatial chromosome organization.

physics.bio-ph↗

arXiv2019

Disentangling the Behavioural Variability of Confined Cell Migration

Cell-to-cell variability is inherent to numerous biological processes, including cell migration. Quantifying and characterizing the variability of migrating cells is challenging, as it requires monitoring many cells for long time windows under identical conditions. Here, we observe the migration of single human breast cancer cells (MDA-MB-231) in confining two-state micropatterns. To describe the stochastic dynamics of this confined migration, we employ a dynamical systems approach. We identify statistics to measure the behavioural variance of the migration, which significantly exceed those predicted by a population-averaged stochastic model. This additional variance can be explained by the combination of an 'aging' process and population heterogeneity. To quantify population heterogeneity, we decompose the cells into subpopulations of slow and fast cells, revealing the presence of distinct classes of dynamical systems describing the migration, ranging from bistable to limit cycle behaviour. Our findings highlight the breadth of migration behaviours present in cell populations.

physics.bio-ph↗

arXiv2019

Mesoscopic non-equilibrium measures can reveal intrinsic features of the active driving

Biological assemblies such as chromosomes, membranes, and the cytoskeleton are driven out of equilibrium at the nanoscale by enzymatic activity and molecular motors. Similar non-equilibrium dynamics can be realized in synthetic systems, such as chemically fueled colloidal particles. Characterizing the stochastic non-equilibrium dynamics of such active soft assemblies still remains a challenge. Recently, new non-invasive approaches have been proposed to determine non-equilibrium behavior, which are based on detecting broken detailed balance in the stochastic trajectories of several coordinates of the system. Inspired by the method of two-point microrheology, in which the equilibrium fluctuations of a pair of probe particles reveal the viscoelastic response of an equilibrium system, here we investigate whether we can extend such an approach to non-equilibrium assemblies: can one extract information on the nature of the active driving in a system from the analysis of a two-point non-equilibrium measure? We address this question theoretically in the context of a class of elastic systems, driven out of equilibrium by a spatially heterogeneous stochastic internal driving. We consider several scenarios for the spatial features of the internal driving that may be relevant in biological and synthetic systems, and investigate how such features of the active noise may be reflected in the long-range scaling behavior of two-point non-equilibrium measures.

physics.bio-ph↗

arXiv2019

Scaling behavior of non-equilibrium measures in internally driven elastic assemblies

Detecting and quantifying non-equilibrium activity is essential for studying internally driven assemblies, including synthetic active matter and complex living systems such as cells or tissue. We discuss a non-invasive approach of measuring non-equilibrium behavior based on the breaking of detailed balance. We focus on "cycling frequencies" - the average frequency with which the trajectories of pairs of degrees of freedom revolve in phase space, and explain their connection with other non-equilibrium measures, including the area enclosing rate and the entropy production rate. We test our approach on simple toy-models comprised of elastic networks immersed in a viscous fluid with site-dependent internal driving. We prove both numerically and analytically that the cycling frequencies obey a power-law as a function of distance between the tracked degrees of freedom. Importantly, the behavior of the cycling frequencies contains information about the dimensionality of the system and the amplitude of active noise. The mapping we use in our analytical approach thus offers a convenient framework for predicting the behavior of two-point non-equilibrium measures for a given activity distribution in the network.

physics.bio-ph↗