arXiv subjects

Cornelis Storm

Publications and source records attributed to Cornelis Storm.

At least 19 recordsLinked to original sources

arXiv2024

Formation of motile cell clusters in heterogeneous model tumors: the role of cell-cell alignment

Circulating tumor cell clusters play an important role in the metastatic cascade. These clusters can acquire a migratory and more invasive phenotype, and coordinate their motion to migrate as a collective. Before such clusters can form by collectively detaching from a primary tumor, however, the cluster must first aggregate in the tumor interior. The mechanism of this cluster formation process is still poorly understood. One of the possible ways for cells to cluster is by aligning their direction of motion with their neighboring cells. This work aims to investigate the role of this cell-cell alignment interaction on the formation of motile cell clusters inside the bulk of a tumor using computer simulations. We employ a Cellular Potts model in which we model a two-dimensional heterogeneous confluent layer containing both motile and non-motile cells. Our results indicate that the degree of clustering is governed by two distinct processes: the formation of clusters due to the presence of cell-cell alignment interactions among motile cells, and the suppression of clustering due to the presence of the dynamic cellular environment (comprised of the non-motile cells). We find that the largest motile clusters are formed for intermediate alignment strengths, contrary to what is observed for motile cells in free space (that is, unimpeded by a dense cellular environment), in which case stronger cell-cell alignment always leads to larger clustering. Our findings suggest that the presence of a densely-packed cellular environment and strong cell-cell alignment inhibits the formation of large migratory clusters within the primary tumor, providing physical insight into potential factors at play during the early stages of metastasis.

physics.bio-ph↗

arXiv2023

Classifying the age of a glass based on structural properties: A machine learning approach

It is well established that physical aging of amorphous solids is governed by a marked change in dynamical properties as the material becomes older. Conversely, structural properties such as the radial distribution function exhibit only a very weak age dependence, usually deemed negligible with respect to the numerical noise. Here we demonstrate that the extremely weak age-dependent changes in structure are in fact sufficient to reliably assess the age of a glass with the support of machine learning. We employ a supervised learning method to predict the age of a glass based on the system's instantaneous radial distribution function. Specifically, we train a multilayer perceptron for a model glassformer quenched to different temperatures, and find that this neural network can accurately classify the age of our system across at least four orders of magnitude in time. Our analysis also reveals which structural features encode the most useful information. Overall, this work shows that through the aid of machine learning, a simple structure-dynamics link can indeed be established for physically aged glasses.

cond-mat.soft↗

arXiv2023

Dead or alive: Distinguishing active from passive particles using supervised learning

A longstanding open question in the field of dense disordered matter is how precisely structure and dynamics are related to each other. With the advent of machine learning, it has become possible to agnostically predict the dynamic propensity of a particle in a dense liquid based on its local structural environment. Thus far, however, these machine-learning studies have focused almost exclusively on simple liquids composed of passive particles. Here we consider a mixture of both passive and active (i.e.\ self-propelled) Brownian particles, with the aim to identify the active particles from minimal local structural information. We compare a state-of-the-art machine learning approach for passive systems with a new method we develop based on Voronoi tessellation. Both methods accurately identify the active particles based on their structural properties at high activity and low concentrations of active particles. Our Voronoi method is, however, substantially faster to train and deploy because it requires fewer, and easy to compute, input features. Notably, both become ineffective when the activity is low, suggesting a fundamentally different structural signature for dynamic propensity and non-equilibrium activity. Ultimately, these efforts might also find relevance in the context of biological active glasses such as confluent cell layers, where subtle changes in the microstructure can hint at pathological changes in cell dynamics.

cond-mat.soft↗

arXiv2022

Controlled gel expansion through colloid oscillation

We model the behaviour of a single colloid embedded in a cross-linked polymer gel, immersed in a viscous background fluid. External fields actuate the particle into a periodic motion, which deforms the embedding matrix and creates a local micro-cavity, containing the particle and any free volume created by its motion. This cavity exists only as long as the particle is actuated and, when present, reduces the local density of the material, leading to swelling. We show that the model exhibits rich resonance features, but is overall characterised by clear scaling laws at low and high driving frequencies, and a pronounced resonance at intermediate frequencies. Our model predictions suggest that both the magnitude and position of the resonance can be varied by varying the material's elastic modulus or cross-linking density, whereas the local viscosity primarily has a dampening effect. Our work implies appreciable free-volume generation is possible by dispersing a collection of colloids in the medium, even at the level of a simple superposition approximation.

cond-mat.soft↗

arXiv2022

Transient response and domain formation in electrically deforming liquid crystal networks

Recently, Van der Kooij and co-workers recognised three distinct, transient regimes in the dynamics of electrically-deforming liquid crystal networks [Van der Kooij et al., Nat. Commun. 10, 1 (2019)]. Based on a Landau-theoretical framework, which encompasses spatially resolved information, we interpret these regimes: initially, the response is dominated by thermal noise, then the top of the film expands, followed by a permeation of this response into the bulk. An important signature of this interpretation is a significant dependence of the regime time scales on film thickness, where we observe a clear thin-film-to-bulk transition. The point of transition coincides with the emergence of spatial inhomogeneities in the bulk, i.e., domain formation, and should be avoided due to the less predictable steady-state expansion it gives rise to. Finally, we show that this domain formation can be suppressed by decreasing the initial thickness of the film, and increasing the linear dimensions of the mesogens, or their orientational order when crosslinked into the network, though this comes at the cost of the deformation magnitude. Our results contribute to achieving finer control over how smart liquid crystal network coatings are activated.

cond-mat.soft↗

arXiv2021

Controlling permeation in electrically-deforming liquid crystal network films: a dynamical Landau theory

Liquid crystal networks exploit the coupling between the responsivity of liquid-crystalline mesogens, e.g., to electric fields, and the (visco)elastic properties of a polymer network. Because of this, these materials have been put forward for a wide array of applications, including responsive surfaces such as artificial skins and membranes. For such applications, the desired functional response must generally be realized under strict geometrical constraints, such as provided by supported thin films. To model such settings, we present a dynamical, spatially-heterogeneous Landau-type theory for electrically-actuated liquid crystal network films. We find that the response of the liquid crystal network permeates the film from top to bottom, and illustrate how this affects the time scale associated with macroscopic deformation. Finally, by linking our model parameters to experimental quantities, we suggest that the permeation rate can be controlled by varying the aspect ratio of the mesogens and their degree of orientational order when cross-linked into the polymer network, for which we predict a single optimum. Our results contribute specifically to the rational design of future applications involving transport or on-demand release of molecular cargo in liquid crystal network films.

cond-mat.soft↗

arXiv2020

Enhanced persistence and collective migration in cooperatively aligning cell clusters

Most cells possess the capacity to locomote. Alone or collectively, this allows them to adapt, to rearrange, and to explore their surroundings. The biophysical characterization of such motile processes, in health and disease, has so far focused mostly on two limiting cases: single-cell motility on the one hand, and the dynamics of confluent tissues such as the epithelium on the other. The in-between regime of clusters, composed of relatively few cells, moving as a coherent unit has received less attention. Such small clusters are, however, deeply relevant in development but also in cancer metastasis. In this work, we use cellular Potts models and analytical active matter theory to understand how the motility of small cell clusters changes with N, the number of cells in the cluster. Modeling and theory reveal our two main findings: Cluster persistence time increases with N while the intrinsic diffusivity decreases with N. We discuss a number of settings in which the motile properties of more complex clusters can be analytically understood, revealing that the focusing effects of small-scale cooperation and cell-cell alignment can overcome the increased bulkiness and internal disorder of multicellular clusters to enhance overall migrational efficacy. We demonstrate this enhancement for small-cluster collective durotaxis, which is shown to proceed more effectively than for single cells. Our results may provide some novel insights into the connection between single-cell and large-scale collective motion and may point the way to the biophysical origins of the enhanced metastatic potential of small tumor cell clusters.

physics.bio-ph↗

arXiv2020

Dynamical Landau-de Gennes Theory for Electrically-Responsive Liquid Crystal Networks

Liquid crystal networks combine the orientational order of liquid crystals with the elastic properties of polymer networks, leading to a vast application potential in the field of responsive coatings, e.g., for haptic feedback, self-cleaning surfaces and static and dynamic pattern formation. Recent experimental work has further paved the way toward such applications by realizing the fast and reversible surface modulation of a liquid crystal network coating upon in-plane actuation with an AC electric field. Here, we construct a Landau-type theory for electrically-responsive liquid crystal networks and perform Molecular Dynamics simulations to explain the findings of these experiments and inform on rational design strategies. Qualitatively, the theory agrees with our simulations and reproduces the salient experimental features. We also provide a set of testable predictions: the aspect ratio of the nematogens, their initial orientational order when cross-linked into the polymer network and the cross-linking fraction of the network all increase the plasticization time required for the film to macroscopically deform. We demonstrate that the dynamic response to oscillating electric fields is characterized by two resonances, which can likewise be influenced by varying these parameters, providing an experimental handle to fine-tune device design.

cond-mat.soft↗

arXiv2019

Non-affinity and fluid-coupled viscoelastic plateau for immersed fiber networks

We employ a matrix-based solver for the linear rheology of fluid-immersed disordered spring networks to reveal four distinct dynamic response regimes. One regime - completely absent in the known vacuum response - exhibits coupled fluid flow and network deformation, with both components responding non-affinely. This regime contains an additional plateau (peak) in the frequency-dependent storage (loss) modulus - features which vanish without full hydrodynamic interactions. The mechanical response of immersed networks such as biopolymers and hydrogels is thus richer than previously established, and offers additional modalities for design and control through fluid interactions.

cond-mat.soft↗

arXiv2019

Combined force-torque spectroscopy of proteins by means of multiscale molecular simulation

Assessing the structural properties of large proteins is important to gain an understanding of their function in, e.g., biological systems or biomedical applications. We propose a method to examine the mechanical properties of proteins subject to applied forces by means of multiscale simulation. We consider both stretching and torsional forces, which can be applied independently of each other. We apply torsional forces to a coarse-grained continuum model of the antibody protein immunoglobulin G (IgG) using Fluctuating Finite Element Analysis and identify the area of strongest deformation. This region is essential to the torsional properties of the molecule as a whole, as it represents the softest, most deformable domain. We subject this part of the molecule to torques and stretching forces on an atomistic level, using molecular dynamics simulations, in order to investigate its torsional properties. We calculate the torsional resistance as a function of the rotation of the domain, while subjecting it to various stretching forces. We learn how these obtained torsion profiles evolve with increasing stretching force and show that they exhibit torsion stiffening, which is in qualitative agreement with experimental findings. We argue that combining the torsion profiles for various stretching forces effectively results in a combined force-torque spectroscopy analysis, which may serve as a mechanical signature for the examined molecule.

physics.bio-ph↗

arXiv2019

Evolving roles and dynamics for catch and slip bonds during adhesion cluster maturation

Focal adhesions are the loci of cellular adhesion to the extracellular matrix. At these sites, various integrins forge connections between the intracellular cytoskeleton and the outside world: large patches of multiple types of integrins together grip hold of collagen, fibronectin and other extracellular matrix components. The mixture of integrins composing the FA will, in general, contain both slip bond integrins and catch bond integrins---bonds whose lifetime increases with applied load and bonds for whom it decreases when forced. Prior work suggests that catch bonds are essential for proper FA stability and mechanosensory functionality. In the present work, we investigate, numerically, the interplay between the two distinct types of bonds and ask how the presence, in the same FA cluster, of slip bonds augments the behavior of the catch bonds. We show, that mixing the two components m low-force mechanical integrity, lacking in purely catch systems, while preserving the potential to strengthen the FA bond by force as well as the mechanosensory qualities of the catch bonds. We investigate the spatial distribution in mixed-integrin FA's and show that the differential response to loading leads, via an excluded volume interaction, to a dependence of the individual integrin diffusivities on the applied load, an effect that has been reported in experiments.

physics.bio-ph↗

arXiv2019

Geometric localization in supported elastic struts

Localized deformation patterns are a common motif in morphogenesis and are increasingly finding widespread applications in materials science, for instance as memory devices. Here we describe the emergence of spatially localized deformations in a minimal mechanical system by exploring the impact of growth and shear on the conformation of a semi-flexible filament connected to a pliable shearable substrate. We combine numerical simulations of a discrete rod model with theoretical analysis of the differential equations recovered in the continuum limit to quantify (in the form of scaling laws) how geometry, mechanics, and growth act together to give rise to such localized structures in this system. We find that spatially localized deformations along the filament emerge for intermediate shear modulus and increasing growth. Finally, we use experiments on a 3D printed multi-material model system to demonstrate that external control of the amount of shear and growth regulates the spatial extent of the localized strain texture.

cond-mat.soft↗

arXiv2018

Self-stresses control stiffness and stability in overconstrained disordered networks

We investigate the interplay between pre-stress and mechanical properties in random elastic networks. To do this in a controlled fashion, we introduce an algorithm for creating random freestanding frames that support exactly one state of self stress. By multiplying all the bond tensions in this state of self stress by the same number---which with the appropriate normalization corresponds to the physical pre-stress inside the frame---we systematically evaluate the linear mechanical response of the frame as a function of pre-stress. After proving that the mechanical moduli of affinely deforming frames are rigourously independent of pre-stress, we turn to non-affinely deforming frames. In such frames, pre-stress has a profound effect on linear response: not only can it change the values of the linear modulus---an effect we demonstrate to be related to a suppressive effect of pre-stress on non-affinity---but pre-stresses also generically trigger bistable mechanical response. Thus, pre-stress can be leveraged to both augment the mechanical response of network architectures on the fly, and to actuate finite deformations. These control modalities may be of use in the design of both novel responsive materials and soft actuators.

cond-mat.dis-nn↗

arXiv2018

Harnessing entropy to enhance toughness in reversibly crosslinked polymer networks

Reversible crosslinking is a design paradigm for polymeric materials, wherein they are microscopically reinforced with chemical species that form transient crosslinks between the polymer chains. Besides the potential for self-healing, recent experimental work suggests that freely diffusing reversible crosslinks in polymer networks, such as gels, can enhance the toughness of the material without substantial change in elasticity. This presents the opportunity for making highly elastic materials that can be strained to a large extent before rupturing. Here, we employ Gaussian chain theory, molecular simulation, and polymer self-consistent field theory for networks to construct an equilibrium picture for how reversible crosslinks can toughen a polymer network without affecting its linear elasticity. Maximisation of polymer entropy drives the reversible crosslinks to bind preferentially near the permanent crosslinks in the network, leading to local molecular reinforcement without significant alteration of the network topology. In equilibrium conditions, permanent crosslinks share effectively the load with neighbouring reversible crosslinks, forming multi-functional crosslink points. The network is thereby globally toughened, while the linear elasticity is left largely unaltered. Practical guidelines are proposed to optimise this design in experiment, along with a discussion of key kinetic and timescale considerations.

cond-mat.soft↗

arXiv2017

Geometry and the onset of rigidity in a disordered network

Disordered spring networks that are undercoordinated may abruptly rigidify when sufficient strain is applied. Since the deformation in response to applied strain does not change the generic quantifiers of network architecture - the number of nodes and the number of bonds between them - this rigidity transition must have a geometric origin. Naive, degree-of-freedom based mechanical analyses such as the Maxwell-Calladine count or the pebble game algorithm overlook such geometric rigidity transitions and offer no means of predicting or characterizing them. We apply tools that were developed for the topological analysis of zero modes and states of self-stress on regular lattices to two-dimensional random spring networks, and demonstrate that the onset of rigidity, at a finite simple shear strain $γ^\star$, coincides with the appearance of a single state of self stress, accompanied by a single floppy mode. The process conserves the topologically invariant difference between the number of zero modes and the number of states of self stress, but imparts a finite shear modulus to the spring network. Beyond the critical shear, we confirm previously reported critical scaling of the modulus. In the sub-critical regime, a singular value decomposition of the network's compatibility matrix foreshadows the onset of rigidity by way of a continuously vanishing singular value corresponding to nascent state of self stress.

cond-mat.soft↗

arXiv2017

Catch Bonding in the Forced Dissociation of a Polymer Endpoint

Applying a force to certain supramolecular bonds may initially stabilize them, manifested by a lower dissociation rate. We show that this behavior, known as catch bonding and by now broadly reported in numerous biophysics bonds, is generically expected when either or both the trapping potential and the force applied to the bond possess some degree of nonlinearity. We enumerate possible scenarios, and for each identify the possibility and, if applicable, the criterion for catch bonding to occur. The effect is robustly predicted by Kramers theory, Mean First Passage Time theory, and finally confirmed in direct MD simulation. Among the catch scenarios, one plays out essentially any time the force on the bond originates in a polymeric object, implying that some degree of catch bond behavior is to be expected in {\em any} protein-protein bond, as well as in more general settings relevant to polymer network mechanics or optical tweezer experiments.

physics.bio-ph↗

arXiv2017

Persistence-driven durotaxis: Generic, directed motility in rigidity gradients

Cells move differently on substrates with different elasticities. In particular, the persistence time of their motion is higher on stiffer substrates. We show that this behavior will result in a net transport of cells directed up a soft-to-stiff gradient. Using simple random walk models with controlled persistence and stochastic simulations, we characterize this propensity to move in terms of the durotactic index measured in experiments. A one-dimensional model captures the essential features of this motion and highlights the competition between diffusive spreading and linear, wavelike propagation. Since the directed motion is rooted in a non-directional change in the behavior of individual cells, the motility is a kinesis rather than a taxis. Persistence-driven durokinesis is generic and may be of use in the design of instructive environments for cells and other motile, mechanosensitive objects.

physics.bio-ph↗

arXiv2016

Determination of single-bond association kinetics by tethered particle motion: concept and simulations

Tethered particle motion (TPM) --- the motion of a micro- or nanoparticle tethered to a substrate by a macromolecule --- is a system that has proven extremely useful for its ability to reveal physical features of the tether, because the thermal motion of the bound particle reports sensitively on parameters like the length, the rigidity, or the folding state of its tether. In this paper, we discuss a novel application of TPM, surveying its utility in probing the kinetics of single secondary bonds: bonds that form and break between the tethered particle and a substrate due, for instance, to receptor/ligand pairs on particle and substrate. Much like the tether itself affects the motion pattern, so do the presence and absence of such secondary connections. Keeping the tether properties constant, we demonstrate how raw positional TPM data may be parsed to generate detailed insights into the association and dissociation kinetics of single secondary bonds. We do this using coarse-grained molecular dynamics simulations, specifically developed to treat the motion of particles close to interfaces.

physics.bio-ph↗