Search

The need for organisms to regulate their volume and osmolarity when surrounded by freshwater is a basic physical challenge for many bacteria, protists and algae. Taking inspiration from the contractile vacuole complex found in many protists, we discuss how simple models of active membrane tubes can give insights into the fluid and active ionic transport properties of such systems. We show that a simple membrane tube with unidirectional ion pumps, and passive ion and water channels, forms a large vacuole due to osmotically-driven water flow and that this can be used to actively pump water out of the cell interior. We discuss the use of this system as a possible minimal method for osmoregulation., Comment: 12 pages, 8 figures

We study a model of contraction-based cell motility inside a microchannel to investigate the regulation of cell polarization and motion by the mechanical resistance of the environment. A positive feedback between the asymmetry of the acto-myosin cortex density and cell motion gives rise to a spontaneous symmetry breaking beyond a threshold contractility that depends on the resistance of extracellular medium. In highly viscous environments, we predict bistability under moderate contractility, so that symmetry breaking needs to be activated. In a viscoelastic environment, we find periodic oscillations in cortex density and velocity polarization. At the boundary between viscous and viscoelastic environments, the cell may either cross into the viscoelastic medium, bounce back into the viscous medium, or become trapped at the boundary. The different scenarios defined different phase diagram that are confirmed by numerical simulations., Comment: 5 pages, 4 figures

We compare three different setups for measuring cell-cell adhesion. We show that the measured strength depends on the type of setup that is used. For identical cells different assays measure different detachment forces. This can be understood from the fact that cell-cell detachment is a global property of the system. We also analyse the role of external force and line tension on contact angle and cell-cell detachment. Comparison with experiment suggests that the current data are essentially dynamical in nature. We dedicate this article to Fyl Pincus who for many of us is an example to be followed not only for outstanding science but also for a marvelous human behavior., Comment: 7 pages, 5 figures

Federated Learning (FL) is a distributed Machine Learning (ML) technique that can benefit from cloud environments while preserving data privacy. We propose Multi-FedLS, a framework that manages multi-cloud resources, reducing execution time and financial costs of Cross-Silo Federated Learning applications by using preemptible VMs, cheaper than on-demand ones but that can be revoked at any time. Our framework encloses four modules: Pre-Scheduling, Initial Mapping, Fault Tolerance, and Dynamic Scheduler. This paper extends our previous work \cite{brum2022sbac} by formally describing the Multi-FedLS resource manager framework and its modules. Experiments were conducted with three Cross-Silo FL applications on CloudLab and a proof-of-concept confirms that Multi-FedLS can be executed on a multi-cloud composed by AWS and GCP, two commercial cloud providers. Results show that the problem of executing Cross-Silo FL applications in multi-cloud environments with preemptible VMs can be efficiently resolved using a mathematical formulation, fault tolerance techniques, and a simple heuristic to choose a new VM in case of revocation., Comment: In review by Journal of Parallel and Distributed Computing

Cellular uptake through the lipid membranes plays an important role in adsorbing nutrients and fighting infection and can be used for drug delivery and nanomedicine developments. Endocytosis is one of the known pathways of the cellular uptake which associate with elastic deformation of the membrane wrapping around the foreign particle. The deformability of the membrane itself is strongly regulated by the presence of a cortical cytoskeleton placed underneath the membrane. It has been shown that size, shape, and orientation of the particles influence on their entry into the cell. Here, we study the role of particle local curvature in the cellular uptake by investigating the wrapping of an elastic membrane around a long cylindrical object with an elliptical cross section. The membrane itself is adhered to a substrate mimicking the cytoskeleton. Membrane wrapping proceeds differently whether the initial contact occurs at the particle's highly curved tip (prolate) or along its long side (oblate). We obtain a wrapping phase diagram as a function of the membrane-cytoskeleton and the membrane-target adhesion energy, which includes three distinct regimes of engulfment(unwrapped, partially wrapped, and fully wrapped), separated by two phase transitions. We also provide analytical expressions for the boundary between the different regimes which confirm that the transitions strongly depend on the orientation and aspect ratio of the particle.

We propose to demonstrate a network slice placement optimization solution based on Deep Reinforcement Learning (DRL), referred to as Heuristically-controlled DRL, which uses a heuristic to control the DRL algorithm convergence. The solution is adapted to realistic networks with large scale and under non-stationary traffic conditions (namely, the network load). We demonstrate the applicability of the proposed solution and its higher and stable performance over a non-controlled DRL-based solution. Demonstration scenarios include full online learning with multiple volatile network slice placement request arrivals., Comment: arXiv admin note: substantial text overlap with arXiv:2010.08295

We consider online learning for optimal network slice placement under the assumption that slice requests arrive according to a non-stationary Poisson process. We propose a framework based on Deep Reinforcement Learning (DRL) combined with a heuristic to design algorithms. We specifically design two pure-DRL algorithms and two families of hybrid DRL-heuristic algorithms. To validate their performance, we perform extensive simulations in the context of a large-scale operator infrastructure. The evaluation results show that the proposed hybrid DRL-heuristic algorithms require three orders of magnitude of learning episodes less than pure-DRL to achieve convergence. This result indicates that the proposed hybrid DRL-heuristic approach is more reliable than pure-DRL in a real non-stationary network scenario., Comment: arXiv admin note: text overlap with arXiv:2105.06741

The evaluation of the impact of using Machine Learning in the management of softwarized networks is considered in multiple research works. Beyond that, we propose to evaluate the robustness of online learning for optimal network slice placement. A major assumption to this study is to consider that slice request arrivals are non-stationary. In this context, we simulate unpredictable network load variations and compare two Deep Reinforcement Learning (DRL) algorithms: a pure DRL-based algorithm and a heuristically controlled DRL as a hybrid DRL-heuristic algorithm, to assess the impact of these unpredictable changes of traffic load on the algorithms performance. We conduct extensive simulations of a large-scale operator infrastructure. The evaluation results show that the proposed hybrid DRL-heuristic approach is more robust and reliable in case of unpredictable network load changes than pure DRL as it reduces the performance degradation. These results are follow-ups for a series of recent research we have performed showing that the proposed hybrid DRL-heuristic approach is efficient and more adapted to real network scenarios than pure DRL., Comment: arXiv admin note: substantial text overlap with arXiv:2105.06741, arXiv:2108.02495

We present a hybrid ML-heuristic approach that we name "Heuristically Assisted Deep Reinforcement Learning (HA-DRL)" to solve the problem of Network Slice Placement Optimization. The proposed approach leverages recent works on Deep Reinforcement Learning (DRL) for slice placement and Virtual Network Embedding (VNE) and uses a heuristic function to optimize the exploration of the action space by giving priority to reliable actions indicated by an efficient heuristic algorithm. The evaluation results show that the proposed HA-DRL algorithm can accelerate the learning of an efficient slice placement policy improving slice acceptance ratio when compared with state-of-the-art approaches that are based only on reinforcement learning., Comment: arXiv admin note: substantial text overlap with arXiv:2105.06741

Network Slice placement with the problem of allocation of resources from a virtualized substrate network is an optimization problem which can be formulated as a multiobjective Integer Linear Programming (ILP) problem. However, to cope with the complexity of such a continuous task and seeking for optimality and automation, the use of Machine Learning (ML) techniques appear as a promising approach. We introduce a hybrid placement solution based on Deep Reinforcement Learning (DRL) and a dedicated optimization heuristic based on the Power of Two Choices principle. The DRL algorithm uses the so-called Asynchronous Advantage Actor Critic (A3C) algorithm for fast learning, and Graph Convolutional Networks (GCN) to automate feature extraction from the physical substrate network. The proposed Heuristically-Assisted DRL (HA-DRL) allows to accelerate the learning process and gain in resource usage when compared against other state-of-the-art approaches as the evaluation results evidence.

Leading Cloud providers offer several types of Virtual Machines (VMs) in diverse contract models, with different guarantees in terms of availability and reliability. Among them, the most popular contract models are the on-demand and the spot models. In the former, on-demand VMs are allocated for a fixed cost per time unit, and their availability is ensured during the whole execution. On the other hand, in the spot market, VMs are offered with a huge discount when compared to the on-demand VMs, but their availability fluctuates according to the cloud's current demand that can terminate or hibernate a spot VM at any time. Furthermore, in order to cope with workload variations, cloud providers have also introduced the concept of burstable VMs which are able to burst up their respective baseline CPU performance during a limited period of time with an up to 20% discount when compared to an equivalent non-burstable on-demand VMs. In the current work, we present the Burst Hibernation-Aware Dynamic Scheduler (Burst-HADS), a framework that schedules and executes tasks of Bag-of-Tasks applications with deadline constraints by exploiting spot and on-demand burstable VMs, aiming at minimizing both the monetary cost and the execution time. Based on ILS metaheuristics, Burst-HADS defines an initial scheduling map of tasks to VMs which can then be dynamically altered by migrating tasks of a hibernated spot VM or by performing work-stealing when VMs become idle. Performance results on Amazon EC2 cloud with different applications show that, when compared to a solution that uses only regular on-demand instances, Burst-HADS reduces the monetary cost of the execution and meet the application deadline even in scenarios with high spot hibernation rates. It also reduces the total execution time when compared to a solution that uses only spot and non-burstable on-demand instances.

In this demo paper, we consider the network slice placement optimization problem and give some insights into a fast heuristic algorithm tailored to placement in large scale networks. We consider an online optimization scenario with multiple and volatile network slice request arrivals and we showcase the applicability of the proposed Edge-enabled network slice placement solution through a Proof-of-Concept illustrated by large scale networks scenarios.

We propose an online heuristic algorithm for the problem of network slice placement optimization. The solution is adapted to support placement on large scale networks and integrates Edge-specific and URLLC constraints. We rely on an approach called the Power of Two Choices to build the heuristic. The evaluation results show the good performance of the heuristic that solves the problem in few seconds under a large scale scenario. The heuristic also improves the acceptance ratio of network slice placement requests when compared against a deterministic online Integer Linear Programming (ILP) solution.

Utilising Onsager's variational formulation, we derive dynamical equations for the relaxation of a fluid membrane tube in the limit of small deformation, allowing for a contrast of solvent viscosity across the membrane and variations in surface tension due to membrane incompressibility. We compute the relaxation rates, recovering known results in the case of purely axis-symmetric perturbations and making new predictions for higher order (azimuthal) $m$-modes. We analyse the long and short wavelength limits of these modes by making use of various asymptotic arguments. We incorporate stochastic terms to our dynamical equations suitable to describe both passive thermal forces and non-equilibrium active forces. We derive expressions for the fluctuation amplitudes, an effective temperature associated with active fluctuations, and the power spectral density for both the thermal and active fluctuations. We discuss an experimental assay that might enable measurement of these fluctuations to infer the properties of the active noise. Finally we discuss our results in the context of active membranes more generally and give an overview of some open questions in the field., Comment: 14 pages, 9 figures

Cell crawling requires the generation of intracellular forces by the cytoskeleton and their transmission to an extracellular substrate through specific adhesion molecules. Crawling cells show many features of excitable systems, such as spontaneous symmetry breaking and crawling in the absence of external cues, and periodic and propagating waves of activity. Mechanical instabilities in the active cytoskeleton network and feedback loops in the biochemical network of activators and repressors of cytoskeleton dynamics have been invoked to explain these dynamical features. Here, we show that the interplay between the dynamics of cell-substrate adhesion and linear cellular mechanics is sufficient to reproduce many non-linear dynamical patterns observed in spreading and crawling cells. Using an analytical formalism of the molecular clutch model of cell adhesion, regulated by local mechanical forces, we show that cellular traction forces exhibit a stick-slip dynamics resulting in periodic waves of protrusion/retraction and propagating waves along the cell edge. This can explain spontaneous symmetry breaking and polarisation of spreading cells, leading to steady crawling or bipedal motion, and bistability, where persistent cell motion requires a sufficiently strong transient external stimulus. The model also highlight the role of membrane tension in providing the long-range mechanical communication across the cell required for symmetry breaking., Comment: 12 pages, 5 figures

Many proteins that undergo sequential enzymatic modification in the Golgi cisternae are displayed at the plasma membrane as cell identity markers. The modified proteins, called glycans, represent a molecular code. The fidelity of this glycan code is measured by how accurately the glycan synthesis machinery realises the desired target glycan distribution for a particular cell type and niche. In this paper, we quantitatively analyse the tradeoffs between the number of cisternae and the number and specificity of enzymes, in order to synthesize a prescribed target glycan distribution of a certain complexity. We find that to synthesize complex distributions, such as those observed in real cells, one needs to have multiple cisternae and precise enzyme partitioning in the Golgi. Additionally, for fixed number of enzymes and cisternae, there is an optimal level of specificity of enzymes that achieves the target distribution with high fidelity. Our results show how the complexity of the target glycan distribution places functional constraints on the Golgi cisternal number and enzyme specificity., Comment: 30 pages

Cell spreading requires a major reorganisation of the actin cytoskeleton, from a cortical structure to a lamellipodium where filaments are mostly parallel to the substrate. We propose a model inspired by the physics of nematic liquid crystals and fluid membranes, in which the coupling between actin mechanics, filaments orientation, and the local curvature of the cell membrane naturally yields the collective reorientation of actin filaments at the highly curved edge of a spreading cell. Filament orientation increases the traction force exerted by the frictional flow of polymerising actin on the substrate, creating a positive feedback loop between edge curvature, filament alignment, and traction force that promotes cell spreading. We establish the condition under which this feedback leads to a full wetting transition, which we interpret as the initiation of a lamellipodium, and we uncover the existence of bi-stability between partial and full spreading which could trigger spontaneous cell polarization and lead to migration.

We introduce DeepMoD, a Deep learning based Model Discovery algorithm. DeepMoD discovers the partial differential equation underlying a spatio-temporal data set using sparse regression on a library of possible functions and their derivatives. A neural network approximates the data and constructs the function library, but it also performs the sparse regression. This construction makes it extremely robust to noise, applicable to small data sets, and, contrary to other deep learning methods, does not require a training set. We benchmark our approach on several physical problems such as the Burgers', Korteweg-de Vries and Keller-Segel equations, and find that it requires as few as $\mathcal{O}(10^2)$ samples and works at noise levels up to $75\%$. Motivated by these results, we apply DeepMoD directly on noisy experimental time-series data from a gel electrophoresis experiment and find that it discovers the advection-diffusion equation describing this system.

Internalization of particles by cells plays a crucial role for adsorbing nutrients and fighting infection. Endocytosis is one of the most important mechanisms of the particles uptake which encompass multiple pathways. Although endocytosis is a complex mechanism involving biochemical signaling and active force generation, the energetic cost associated to the large deformations of the cell membrane wrapping around the foreign particle is an important factor controlling this process, which can be studied using quantitative physical models. Of particular interest is the competition between membrane - cytoskeleton and membrane - target adhesion. Here, we explore the wrapping of a lipid membrane around a long cylindrical object in the presence of a substrate mimicking the cytoskeleton. Using discretization of the Helfrich elastic energy that accounts for the membrane bending rigidity and surface tension, we obtain a wrapping phase diagram as a function of the membrane-cytoskeleton and the membrane-target adhesion energy that includes unwrapped, partially wrapped and fully wrapped states. We provide an analytical expression for the boundary between the different regimes. While the transition to partial wrapping is independent of membrane tension, the transition to full wrapping is very much influenced by membrane tension. We also show that target wrapping may proceed in an asymmetric fashion in the full wrapping regime.

Cloud platforms have emerged as a prominent environment to execute high performance computing (HPC) applications providing on-demand resources as well as scalability. They usually offer different classes of Virtual Machines (VMs) which ensure different guarantees in terms of availability and volatility, provisioning the same resource through multiple pricing models. For instance, in Amazon EC2 cloud, the user pays per hour for on-demand VMs while spot VMs are unused instances available for lower price. Despite the monetary advantages, a spot VM can be terminated, stopped, or hibernated by EC2 at any moment. Using both hibernation-prone spot VMs (for cost sake) and on-demand VMs, we propose in this paper a static scheduling for HPC applications which are composed by independent tasks (bag-of-task) with deadline constraints. However, if a spot VM hibernates and it does not resume within a time which guarantees the application's deadline, a temporal failure takes place. Our scheduling, thus, aims at minimizing monetary costs of bag-of-tasks applications in EC2 cloud, respecting its deadline and avoiding temporal failures. To this end, our algorithm statically creates two scheduling maps: (i) the first one contains, for each task, its starting time and on which VM (i.e., an available spot or on-demand VM with the current lowest price) the task should execute; (ii) the second one contains, for each task allocated on a VM spot in the first map, its starting time and on which on-demand VM it should be executed to meet the application deadline in order to avoid temporal failures. The latter will be used whenever the hibernation period of a spot VM exceeds a time limit. Performance results from simulation with task execution traces, configuration of Amazon EC2 VM classes, and VMs market history confirms the effectiveness of our scheduling and that it tolerates temporal failures.

Motivated by the mechanics of dynamin-mediated membrane tube fission we analyse the stability of fluid membrane tubes subjected to shear flow in azimuthal direction. We find a novel helical instability driven by the membrane shear flow which results in a non-equilibrium steady state for the tube fluctuations. This instability has its onset at shear rates that may be physiologically accessible under the action of dynamin and could also be probed using in-vitro experiments on membrane nanotubes, e.g. using magnetic tweezers. We discuss how such an instability may play a role in the mechanism for dynamin-mediated membrane tube fission., Comment: Main Text: 5 pages, 3 figures + S.I. 14 pages, 6 figures (Accepted Version)

We study a membrane tube with unidirectional ion pumps driving an osmotic pressure difference. A pressure driven peristaltic instability is identified, qualitatively distinct from similar tension-driven Rayleigh type instabilities on membrane tubes. We discuss how this instability could be related to the function and biogenesis of membrane bound organelles, in particular the contractile vacuole complex. The unusually long natural wavelength of this instability is in agreement with that observed in cells., Comment: 5 pages, 3 figures + Supplementary information

In this work we present VCube-PS, a topic-based Publish/Subscribe system built on the top of a virtual hypercube-like topology. Membership information and published messages are broadcast to subscribers (members) of a topic group over dynamically built spanning trees rooted at the publisher. For a given topic, the delivery of published messages respects the causal order. VCube-PS was implemented on the PeerSim simulator, and experiments are reported including a comparison with the traditional Publish/Subscribe approach that employs a single rooted static spanning-tree for message distribution. Results confirm the efficiency of VCube-PS in terms of scalability, latency, number and size of messages., Comment: Improved text and performance evaluation. Added proof for the algorithms (Section 3.4)

Distributed storage systems such as Hadoop File System or Google File System (GFS) ensure data availability and durability using replication. This paper is focused on the analysis of the efficiency of replication mechanism that determines the location of the copies of a given file at some server. The variability of the loads of the nodes of the network is investigated for several policies. Three replication mechanisms are tested against simulations in the context of a real implementation of a such a system: Random, Least Loaded and Power of Choice. The simulations show that some of these policies may lead to quite unbalanced situations: if $\beta$ is the average number of copies per node it turns out that, at equilibrium, the load of the nodes may exhibit a high variability. It is shown in this paper that a simple variant of a power of choice type algorithm has a striking effect on the loads of the nodes: at equilibrium, the distribution of the load of a node has a bounded support, most of nodes have a load less than $2\beta$ which is an interesting property for the design of the storage space of these systems. Mathematical models are introduced and investigated to explain this interesting phenomenon. The analysis of these systems turns out to be quite complicated mainly because of the large dimensionality of the state spaces involved. Our study relies on probabilistic methods, mean-field analysis, to analyze the asymptotic behavior of an arbitrary node of the network when the total number of nodes gets large. An additional ingredient is the use of stochastic calculus with marked Poisson point processes to establish some of our results.

The proper sorting of membrane components by regulated exchange between cellular organelles is crucial to intra-cellular organisation. This process relies on the budding and fusion of transport vesicles, and should be strongly influenced by stochastic fluctuations considering the relatively small size of many organelles. We identify the perfect sorting of two membrane components initially mixed in a single compartment as a first passage process, and we show that the mean sorting time exhibits two distinct regimes as a function of the ratio of vesicle fusion to budding rates. Low ratio values lead to fast sorting, but results in a broad size distribution of sorted compartments dominated by small entities. High ratio values result in two well defined sorted compartments but is exponentially slow. Our results suggest an optimal balance between vesicle budding and fusion for the rapid and efficient sorting of membrane components, and highlight the importance of stochastic effects for the steady-state organisation of intra-cellular compartments., Comment: Accepted for publication. Phys. Rev. Lets. December 2017

The dynamical organization of membrane-bound organelles along intracellular transport pathways relies on vesicular exchange between organelles and on biochemical maturation of the organelle content by specific enzymes. The relative importance of each mechanism in controlling organelle dynamics remains controversial, in particular for transport through the Golgi apparatus. Using a stochastic model, we show that full maturation of membrane-bound compartments can be seen as the stochastic escape from a steady-state in which export is dominated by vesicular exchange. We show that full maturation can contribute a significant fraction of the total out-flux for small organelles such as endosomes and Golgi cisternae., Comment: Accepted for publication. Biophysical Journal. December 2017

We propose a model for membrane-cortex adhesion which couples membrane deformations, hydrodynamics and kinetics of membrane-cortex ligands. In its simplest form, the model gives explicit predictions for the critical pressure for membrane detachment and for the value of adhesion energy. We show that these quantities exhibit a significant dependence on the active acto-myosin stresses. The model provides a simple framework to access quantitative information on cortical activity by means of micropipette experiments. We also extend the model to incorporate fluctuations and show that detailed information on the stability of membrane-cortex coupling can be obtained by a combination of micropipette aspiration and fluctuation spectroscopy measurements.

In its classical form, a consistent replicated service requires all replicas to witness the same evolution of the service state. Assuming a message-passing environment with a majority of correct processes, the necessary and sufficient information about failures for implementing a general state machine replication scheme ensuring consistency is captured by the {\Omega} failure detector. This paper shows that in such a message-passing environment, {\Omega} is also the weakest failure detector to implement an eventually consistent replicated service, where replicas are expected to agree on the evolution of the service state only after some (a priori unknown) time. In fact, we show that {\Omega} is the weakest to implement eventual consistency in any message-passing environment, i.e., under any assumption on when and where failures might occur. Ensuring (strong) consistency in any environment requires, in addition to {\Omega}, the quorum failure detector {\Sigma}. Our paper thus captures, for the first time, an exact computational difference be- tween building a replicated state machine that ensures consistency and one that only ensures eventual consistency.

This work proposes a new and flexible unreliable failure detector whose output is related to the trust level of a set of processes. By expressing the relevance of each process of the set by an impact factor value, our approach allows the tuning of the detector output, making possible a softer or stricter monitoring. The idea behind our proposal is that, according to an acceptable margin of failures and the impact factor assigned to processes, in some scenarios, the failure of some low impact processes may not change the user confidence in the set of processes, while the crash of a high impact factor process may seriously affect it. We outline the application scenarios and the proposed unreliable failure detector, giving a detailed account of the concept on which it is based., Comment: EDCC-2014, Fast-Abstracts, failure detector, impact factor, trust level

The formation of dynamical clusters of proteins is ubiquitous in cellular membranes and is in part regulated by the recycling of membrane components. Mean-field models of out-of-equilibrium cluster formation with recycling predict a broad cluster size distribution for infinite systems and must be corrected for finite-size effects for small systems such as cellular organelles. We show, using stochastic simulations and analytic modelling, that tuning the system size is an efficient way to control the size of lateral membrane heterogeneities. We apply these findings to a chain of enzymatic reaction sensitive to membrane protein clustering. The reaction efficiency is found to be a non-monotonic function of the system size, and can be optimal for sizes comparable to those of cellular organelles.

The sliding friction force exerted by stochastic linkers interacting with a moving filament is calculated. The elastic properties of the substrate on which the linkers are anchored are shown to strongly influence the friction force. In some cases, the force is maximal for a finite substrate rigidity. Collective effects give rise to a dynamical instability resulting in a stick-slip behaviour, which is substrate-sensitive. The relevance of these results for the motility of crawling cells powered by an actin retrograde flow is discussed., Comment: 6 pages, 4 figures

The membrane components of cellular organelles have been shown to segregate into domains as the result of biochemical maturation. We propose that the dynamical competition between maturation and lateral segregation of membrane components regulates domain formation. We study a two- component fluid membrane in which enzymatic reaction irreversibly converts one component into another, and phase separation triggers the formation of transient membrane domains. The maximum domains size is shown to depend on the maturation rate as a power-law similar to the one observed for domain growth with time in the absence of maturation, despite this time dependence not being verified in the case of irreversible maturation. This control of domain size by enzymatic activity could play a critical role in intra-organelle dynamics., Comment: 7 pages, 6 figures

The mechanisms controlling the transport of proteins across the Golgi stack of mammalian and plant cells is the subject of intense debate, with two models, cisternal progression and inter-cisternal exchange, emerging as major contenders. A variety of transport experiments have claimed support for each of these models. We reevaluate these experiments using a single quantitative coarse-grained framework of intra-Golgi transport that accounts for both transport models and their many variants. Our analysis makes a definitive case for the existence of inter-cisternal exchange both for small membrane proteins (VSVG) and large protein complexes (procollagen) -- this implies that membrane structures larger than the typical protein-coated vesicles must be involved in transport. Notwithstanding, we find that current observations on protein transport cannot rule out cisternal progression as contributing significantly to the transport process. To discriminate between the different models of intra-Golgi transport, we suggest experiments and an analysis based on our extended theoretical framework that compare the dynamics of transiting and resident proteins., Comment: 7 pages, 4 Figures

The ability of cells to undergo reversible shape changes is often crucial to their survival. For Red Blood Cells (RBCs), irreversible alteration of the cell shape and flexibility often causes anemia. Here we show theoretically that RBCs may react irreversibly to mechanical perturbations because of tensile stress in their cytoskeleton. The transient polymerization of protein fibers inside the cell seen in sickle cell anemia or a transient external force can trigger the formation of a cytoskeleton-free membrane protrusion of micrometer dimensions. The complex relaxation kinetics of the cell shape is shown to be responsible for selecting the final state once the perturbation is removed, thereby controlling the reversibility of the deformation. In some case, tubular protrusion are expected to relax via a peculiar "pearling instability"., Comment: 4 pages, 3 figures

Compartmentalization into biochemically distinct organelles constantly exchanging material is one of the hallmarks of eukaryotic cells. In the most naive picture of inter-organelle transport driven by concentration gradients, concentration differences between organelles should relax. We determine the conditions under which cooperative transport, i.e. based on molecular recognition, allows for the existence and maintenance of distinct organelle identities. Cooperative transport is also shown to control the flux of material transiting through a compartmentalized system, dramatically increasing the transit time under high incoming flux. By including chemical processing of the transported species, we show that this property provides a strong functional advantage to a system responsible for protein maturation and sorting., Comment: 9 pages, 5 figures