The size, shape, and dynamics of cellular blebs

A cellular bleb grows when a portion of the cell membrane detaches from the underlying cortex under the influence of a cytoplasmic pressure. We develop a quantitative model for the growth and dynamics of these objects in a simple two-dimensional setting. In particular, we first find the minimum cytoplasmic pressure and minimum unsupported membrane length for a stationary bleb to nucleate and grow as a function of the membrane-cortex adhesion. We next show how a bleb may travel around the periphery of the cell when the cytoplasmic pressure varies in space and time in a prescribed way and find that the traveling speed is governed by the speed of the pressure change induced by local cortical contraction while the shape of the traveling bleb is governed by the speed of cortical healing. Finally, we relax the assumption that the pressure change is prescribed and couple it hydrodynamically to the cortical contraction and membrane deformation. By quantifying the phase space of bleb formation and dynamics, our framework serves to delineate the range and scope of bleb-associated cell motility and synthesizes a variety of experimental observations

p53-mediated activation of the mitochondrial protease HtrA2/Omi prevents cell invasion

Oncogenic Ras induces cell transformation and promotes an invasive phenotype. The tumor suppressor p53 has a suppressive role in Rasdriven invasion. However, its mechanism remains poorly understood. Here we show that p53 induces activation of the mitochondrial protease high-temperature requirement A2 (HtrA2; also known as Omi) and prevents Ras-driven invasion by modulating the actin cytoskeleton. Oncogenic Ras increases accumulation of p53 in the cytoplasm, which promotes the translocation of p38 mitogen-activated protein kinase (MAPK) into mitochondria and induces phosphorylation of HtrA2/Omi. Concurrently, oncogenic Ras also induces mitochondrial fragmentation, irrespective of p53 expression, causing the release of HtrA2/Omi from mitochondria into the cytosol. Phosphorylated HtrA2/Omi therefore cleaves β-actin and decreases the amount of filamentous actin (F-actin) in the cytosol. This ultimately down-regulates p130 Crk-associated substrate (p130Cas)-mediated lamellipodia formation, countering the invasive phenotype initiated by oncogenic Ras. Our novel findings provide insights into the mechanism by which p53 prevents the malignant progression of transformed cells. © 2014 Yamauchi et al.published_or_final_versio

Myosin concentration underlies cell size–dependent scalability of actomyosin ring constriction

© The Author(s), 2011. This article is distributed under the terms of the Creative Commons Attribution 3.0 License. The definitive version was published in Journal of Cell Biology 195 (2011): 799-813, doi:10.1083/jcb.201101055.In eukaryotes, cytokinesis is accomplished by an actomyosin-based contractile ring. Although in Caenorhabditis elegans embryos larger cells divide at a faster rate than smaller cells, it remains unknown whether a similar mode of scalability operates in other cells. We investigated cytokinesis in the filamentous fungus Neurospora crassa, which exhibits a wide range of hyphal circumferences. We found that N. crassa cells divide using an actomyosin ring and larger rings constricted faster than smaller rings. However, unlike in C. elegans, the total amount of myosin remained constant throughout constriction, and there was a size-dependent increase in the starting concentration of myosin in the ring. We predict that the increased number of ring-associated myosin motors in larger rings leads to the increased constriction rate. Accordingly, reduction or inhibition of ring-associated myosin slows down the rate of constriction. Because the mechanical characteristics of contractile rings are conserved, we predict that these findings will be relevant to actomyosin ring constriction in other cell types.Work in the laboratories of M.K. Balasubramanian and G. Jedd is supported by research funds from Singapore Millennium Foundation and the Temasek Life Sciences Laboratory.2012-05-2

Combined Simulation and Experimental Study of Large Deformation of Red Blood Cells in Microfluidic Systems

Author manuscript; available in PMC 2012 March 1.We investigate the biophysical characteristics of healthy human red blood cells (RBCs) traversing microfluidic channels with cross-sectional areas as small as 2.7 × 3 μm. We combine single RBC optical tweezers and flow experiments with corresponding simulations based on dissipative particle dynamics (DPD), and upon validation of the DPD model, predictive simulations and companion experiments are performed in order to quantify cell deformation and pressure–velocity relationships for different channel sizes and physiologically relevant temperatures. We discuss conditions associated with the shape transitions of RBCs along with the relative effects of membrane and cytosol viscosity, plasma environments, and geometry on flow through microfluidic systems at physiological temperatures. In particular, we identify a cross-sectional area threshold below which the RBC membrane properties begin to dominate its flow behavior at room temperature; at physiological temperatures this effect is less profound.Singapore-MIT Alliance for Research and TechnologyUnited States. National Institutes of Health (National Heart, Lung, and Blood Institute Award R01HL094270

Spatiotemporal Chaos in Rayleigh-Bénard Convection

Spatiotemporal chaos, or disorder in both the space and time coordinates, is studied in direct numerical simulations of Rayleigh-Bénard convection. In particular, the following investigations pertaining to spiral defect chaos are discussed. First, in the absence of the mean flow, spiral defect chaos is found to collapse to a stationary pattern comprising textures of stripes with angular bends. The quenched patterns are characterized by mean wave numbers that approach those uniquely selected by focus-type singularities, which, in the absence of the mean flow, lie at the zig zag instability boundary. In addition, mean flow is shown to contribute to the phenomenon of rolls terminating perpendicularly into lateral walls. In the absence of the mean flow, rolls begin to terminate into lateral walls at an oblique angle. This obliqueness increases with the Rayleigh number. Second, the transport of passive tracers in the presence of advection by spiral defect chaos is found to be characterized by normal diffusion. The enhancement in the tracer diffusivity follows two regimes. When the molecular diffusivity of the tracer concentration is small, the enhancement is proportional to the Péclet number. When the molecular diffusivity is large, the enhancement is proportional to the square root of the Péclet number. This difference is explained in terms of the dependence of the transport on the local wave numbers. It is found that tracer concentrations with small molecular diffusivity experience enhanced longitudinal diffusion and suppressed lateral diffusion at regions of the flow occupied by defects. Third, perturbations in spiral defect chaos are found to propagate in a localized manner. In particular, they nucleate around the defect structures in the flow. In addition, an oscillatory instability at the spiral core is discovered. Finally, the propagation in pre-chaotic stripe textures is explained in terms of the diffusion of the phase variable of the stripe state.</p

Synthetic data generation method for data-free knowledge distillation in regression neural networks

Knowledge distillation is the technique of compressing a larger neural network, known as the teacher, into a smaller neural network, known as the student, while still trying to maintain the performance of the larger neural network as much as possible. Existing methods of knowledge distillation are mostly applicable for classification tasks. Many of them also require access to the data used to train the teacher model. To address the problem of knowledge distillation for regression tasks under the absence of original training data, previous work has proposed a data-free knowledge distillation method where synthetic data are generated using a generator model trained adversarially against the student model. These synthetic data and their labels predicted by the teacher model are then used to train the student model. In this study, we investigate the behavior of various synthetic data generation methods and propose a new synthetic data generation strategy that directly optimizes for a large but bounded difference between the student and teacher model. Our results on benchmark and case study experiments demonstrate that the proposed strategy allows the student model to learn better and emulate the performance of the teacher model more closely.Comment: 19 pages, 9 figure