Search for: All records

Abstract Embedding a collective of tumor cells, i.e. a tumor spheroid, in a fibrous environment, such as a collagen network, provides an essentialin vitroplatform to investigate the biophysical mechanisms of tumor invasion. To predict new mechanisms, we develop a three-dimensional computational model of an embedded spheroid using a vertex model, with cells represented as deformable polyhedrons, mechanically coupled to a fiber network via active linker springs. As the linker springs actively contract, the fiber network remodels. As we tune the rheology of the spheroid and the fiber network stiffness, we find that both factors affect the remodeling of the fiber network with fluid-like spheroids densifying and radially realigning the fiber network more on average than solid-like spheroids but only for a range of intermediate fiber network stiffnesses. Our predictions are supported by experimental studies comparing non-tumorigenic MCF10A spheroids and malignant MDA-MB-231 spheroids embedded in collagen networks. The spheroid rheology-dependent effects are the result of cellular motility generating spheroid shape fluctuations. These shape fluctuations lead to emergent feedback between the spheroid and the fiber network to further remodel the fiber network. This emergent feedback occurs only at intermediate fiber network stiffness since at low fiber network stiffness, the mechanical response of the coupled system is dominated by the spheroid and for high fiber network stiffness, the mechanical response is dominated by the fiber network. We are therefore able to quantify the regime of optimal spheroid-fiber network mechanical reciprocity. Our results uncover intricate morphological-mechanical interplay between an embedded spheroid and its surrounding fiber network with both spheroid contractile strengthandspheroid shape fluctuations playing important roles in the pre-invasion stages of tumor invasion. 

Nanoparticles, such as viruses, can enter cells via endocytosis, a process by which the cell membrane wraps around them. The role of nanoparticle size and shape on endocytosis has been well studied, but the biophysical details of how extracellular proteins on the cell membrane surface mediate uptake are less clear. Motivated by recent discoveries regarding extracellular vimentin in viral and bacterial uptake and the structure of coronaviruses, we construct a computational model with a cell-like and virus-like construct containing filamentous protein structures protruding from their surfaces. We study the impact of these additional degrees of freedom on viral wrapping. The cell surface is modeled as a deformable sheet with bending rigidity, and extracellular vimentin as semiflexible polymers, or extracellular components (ECC), placed randomly on the sheet. The virus is modeled as a deformable shell that also has explicit, freely rotating spike filaments on its surface. Our results indicate that cells with optimally populated filaments are more susceptible to infection as they take up the virus more quickly and utilize a relatively smaller area of the cell surface. At optimal ECC density, the cell surface forms a fold around the virus, which is faster and more efficient at wrapping than localized crumples. Additionally, cell surface bending rigidity aids in the generation of folds by increasing force transmission across the surface. Changing other mechanical parameters, such as the stretching stiffness of filamentous ECC or virus spikes, can result in localized crumple formation on 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. Published by the American Physical Society2025 

Abstract The viscoelastic properties of tissues influence their morphology and cellular behavior, yet little is known about changes in these properties during brain malformations. Lissencephaly, a severe cortical malformation caused byLIS1mutations, results in a smooth cortex. Here, we show that human-derived brain organoids withLIS1mutation exhibit increased stiffness compared to controls at multiple developmental stages. This stiffening correlates with abnormal extracellular matrix (ECM) expression and organization, as well as elevated water content, measured by diffusion-weighted MRI. Short-term MMP9 treatment reduces both stiffness and water diffusion levels to control values. Additionally, a computational microstructure mechanical model predicts mechanical changes based on ECM organization. These findings suggest thatLIS1plays a critical role in ECM regulation during brain development and that its mutation leads to significant viscoelastic alterations. 

Abstract We introduce frequency propagation, a learning algorithm for nonlinear physical networks. In a resistive electrical circuit with variable resistors, an activation current is applied at a set of input nodes at one frequency and an error current is applied at a set of output nodes at another frequency. The voltage response of the circuit to these boundary currents is the superposition of an activation signal and an error signal whose coefficients can be read in different frequencies of the frequency domain. Each conductance is updated proportionally to the product of the two coefficients. The learning rule is local and proved to perform gradient descent on a loss function. We argue that frequency propagation is an instance of a multimechanism learning strategy for physical networks, be it resistive, elastic, or flow networks. Multimechanism learning strategies incorporate at least two physical quantities, potentially governed by independent physical mechanisms, to act as activation and error signals in the training process. Locally available information about these two signals is then used to update the trainable parameters to perform gradient descent. We demonstrate how earlier work implementing learning via chemical signaling in flow networks (Anisetti, Scellier, et al., 2023) also falls under the rubric of multimechanism learning. 

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 whichboththe 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. 

Chromatin is an essential component of nuclear mechanical response and shape that maintains nuclear compartmentalization and function. However, major genomic functions, such as transcription activity, might also impact cell nuclear shape via blebbing and rupture through their effects on chromatin structure and dynamics. To test this idea, we inhibited transcription with several RNA polymerase II inhibitors in wild type cells and perturbed cells that present increased nuclear blebbing. Transcription inhibition suppresses nuclear blebbing for several cell types, nuclear perturbations, and transcription inhibitors. Furthermore, transcription inhibition suppresses nuclear bleb formation, bleb stabilization, and bleb-based nuclear ruptures. Interestingly, transcription inhibition does not alter either H3K9 histone modification state, nuclear rigidity, or actin compression and contraction, which typically control nuclear blebbing. Polymer simulations suggest that RNA pol II motor activity within chromatin could drive chromatin motions that deform the nuclear periphery. Our data provide evidence that transcription inhibition suppresses nuclear blebbing and rupture, separate and distinct from chromatin rigidity. 

Brain organoids recapitulate a number of brain properties, including neuronal diversity. However, do they recapitulate brain structure? Using a hydrodynamic description for cell nuclei as particles interacting initially via an effective , attractive force as mediated by the respective, surrounding cytoskeletons, we quantify structure development in brain organoids to determine what physical mechanism regulates the number of cortex-core structures. Regions of cell nuclei overdensity in the linear regime drive the initial seeding for cortex-core structures, which ultimately develop in the non-linear regime, as inferred by the emergent form of an effective interaction between cell nuclei and with the extracellular environment. Individual cortex-core structures then provide a basis upon which we build an extended version of the buckling without bending morphogenesis (BWBM) model, with its proliferating cortex and constraining core, to predict foliations/folds of the cortex in the presence of a nonlinearity due to cortical cells actively regulating strain. In doing so, we obtain asymmetric foliations/folds with respect to the trough (sulci) and the crest (gyri). In addition to laying new groundwork for the design of more familiar and less familiar brain structures, the hydrodynamic description for cell nuclei during the initial stages of brain organoid development provides an intriguing quantitative connection with large-scale structure formation in the universe. 

Abstract Disordered spring networks can exhibit rigidity transitions, due to either the removal of material in over-constrained networks or the application of strain in under-constrained ones. While an effective medium theory (EMT) exists for the former, there is none for the latter. We, therefore, formulate an EMT for random regular, under-constrained spring networks with purely geometrical disorder to predict their stiffness via the distribution of tensions. We find a linear dependence of stiffness on strain in the rigid phase and a nontrivial dependence on both the mean and standard deviation of the tension distribution. While EMT does not yield highly accurate predictions of shear modulus due to spatial heterogeneities, it requires only the distribution of tensions for an intact system, therefore making it an ideal starting point for experimentalists quantifying the mechanics of such networks.