arXiv subjects

Pierre Sens

Publications and source records attributed to Pierre Sens.

At least 19 recordsLinked to original sources

arXiv2026

Mechanical stress induced by the polymerisation of an active gel near a surface

Actin flow in the cortical cytoskeleton underneath the cell membrane generates mechanical stresses that shape the cell surface. We study this mechanism using a hydrodynamic model of a compressible active gel polymerizing at the membrane and undergoing turnover. We determine how actin flow, density relaxation and friction of actin with the membrane generate stress on a corrugated membrane at the linear order in deformation. Analytical solutions in limiting regimes, combined with finite element methods in the general case, provide a map of normal and tangential stresses as functions of compressibility, interfacial friction and actin turnover, and determine the conditions under which actin polymerization can render the membrane linearly unstable. The non-linear regime is also briefly discussed.

cond-mat.soft↗

arXiv2026

Cellular wrapping of elastic particles by a supported lipid membrane

Constancy of life vitally depends on the internalization of particles through biomembranes. Of particular interest, cellular uptake, including phagocytosis, receptor-mediated endocytosis, and membrane fusion, critically depends on the elasticity of particles. Cellular membranes are strongly linked to a supporting cytoskeleton. However, in most previous studies, the effect of this cortical network somehow is overlooked. In this paper, we study the cellular wrapping of a membrane around a 2D elastic particle in the presence of a substrate mimicking cytoskeleton. Our simulations show that the impact of particle flexibility on the wrapping process depends on the magnitude of the membrane particle adhesion. In contrast, the extent of membrane protrusions formed around the target always increases with target stiffness. Since the extension of membrane protrusions is an essential step in the phagocytosis process, this result may indicate a selective behavior of macrophages in the phagocytosis of aged red blood cells.

cond-mat.soft↗

arXiv2025

Curvature instability of an active gel growing on a wavy membrane

Cell shape changes are largely controlled by the actin cytoskeleton, a dynamic filament network beneath the plasma membrane. Several cell types can form extended free-standing protrusions not supported by an extracellular substrate or matrix, and regulated by proteins that modulate cytoskeletal dynamics in a way sensitive to the curvature of the cell membrane. We develop a theoretical model for the mechanics of a free-standing viscous actin network growing on a corrugated membrane. The model couples the dynamics of the viscous active gel with membrane deformation and the recruitment of curvature-sensitive actin nucleators. We show that an actin layer polymerising uniformly on the membrane always exerts a stabilising effect that reduces membrane deformation. However, curvature-sensitive actin nucleator proteins can render the membrane linearly unstable, depending on the interplay between membrane and actin dynamics, giving rise to spontaneous membrane deformation which could initiate extended free-standing cellular protrusion.

cond-mat.soft↗

arXiv2025

IRENE: a fluId layeR finitE-elemeNt softwarE

We present a finite-element software library, IRENE, which allows to solve numerically the dynamics of a viscous fluid layer embedded in three-dimensional space. Unlike finite-element libraries present in the literature, IRENE can handle two-dimensional open surfaces with a wide range of boundary conditions, and inter-surface obstacles with any shapes, and is built upon the user-friendly and versatile finite element computational software (FEniCS). Also, the library can describe a wide range of physical regimes--both low-Reynolds-number and inertia-dominated ones--capturing the complex coupling between in-plane flows, out-of-plane deformations, surface tension, and elastic response. We validate IRENE against known analytical and numerical results, and demonstrate its capabilities through physical examples. Overall, IRENE provides a versatile and efficient tool for understanding fluid-layer dynamics on multiple physical scales, from flows of lipidic membranes on a microscopic level, to fluid flows on a macroscopic scale, to atmospheric air flows on a planetary level.

physics.flu-dyn↗

arXiv2024

Membrane tubes with active pumping: water transport, vacuole formation and osmoregulation

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.

physics.bio-ph↗

arXiv2024

Contractility-driven cell motility against a viscoelastic resistance

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.

physics.bio-ph↗

arXiv2023

Quantitative comparison of cell-cell detachment force in different experimental setups

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.

physics.bio-ph↗

arXiv2023

Multi-FedLS: a Framework for Cross-Silo Federated Learning Applications on Multi-Cloud Environments

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.

cs.DC↗

arXiv2022

Role of particle local curvature in cellular wrapping

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.

cond-mat.soft↗

arXiv2021

DRL-based Slice Placement under Realistic Network Load Conditions

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.

cs.NI↗

arXiv2021

DRL-based Slice Placement Under Non-Stationary Conditions

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.

cs.NI↗

arXiv2021

On the Robustness of Controlled Deep Reinforcement Learning for Slice Placement

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.

cs.NI↗

arXiv2021

Controlled Deep Reinforcement Learning for Optimized Slice Placement

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.

cs.LG↗

arXiv2021

A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement

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.

cs.NI↗

arXiv2021

DeepMoD: Deep learning for Model Discovery in noisy data

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.

physics.comp-ph↗

arXiv2020

Scheduling Bag-of-Tasks in Clouds using Spot and Burstable Virtual Machines

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.

cs.DC↗

arXiv2020

Heuristic for Edge-enabled Network Slicing Optimization using the Power of Two Choices

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.

cs.NI↗

arXiv2020

Edge-enabled Optimized Network Slicing in Large Scale Networks

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.

cs.NI↗