Search arXiv⌕ Search

arXiv · physics/0104024

Large deformation of spherical vesicle studied by perturbation theory and Surface evolver

Abstract

With tangent angle perturbation approach the axial symmetry deformation of a spherical vesicle in large under the pressure changes is studied by the elasticity theory of Helfrich spontaneous curvature model.Three main results in axial symmetry shape: biconcave shape, peanut shape, and one type of myelin are obtained. These axial symmetry morphology deformations are in agreement with those observed in lipsome experiments by dark-field light microscopy [Hotani, J. Mol. Biol. 178, (1984) 113] and in the red blood cell with two thin filaments (myelin) observed in living state (see, Bessis, Living Blood Cells and Their Ultrastructure, Springer-Verlag, 1973). Furthermore, the biconcave shape and peanut shape can be simulated with the help of a powerful software, Surface Evolver [Brakke, Exp. Math. 1, 141 (1992) 141], in which the spontaneous curvature can be easy taken into account.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jianjun Zhou, Yong Zhang, Xin Zhou, Ou-Yang Zhong-can. 2001-04-06. Large deformation of spherical vesicle studied by perturbation theory and Surface evolver. https://doi.org/10.1142/s0217979201007087

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Exploring the World of Lipids with Machine-Learned Coarse-Grained Models

Lipid bilayers are central to cellular organization and function, yet their atomistic modelling remains constrained by its high computational cost. While coarse-grained (CG) models can overcome this bottleneck, they are limited in accuracy due to the use of simplified functional forms based on molecular mechanics. Using a hybrid functional form combining physics-based terms with a graph neural network, we present implicit-solvent, machine-learned CG models for POPC. When trained on single-lipid data, the model recovers the atomistic conformational landscape. By adding short atomistic simulations of a small flat bilayer to the training data and combining force matching with a denoising objective, we train a model that accurately reproduces bilayer structure and dynamics, in closer agreement with the atomistic reference than existing models, like Martini3 and DryMartini. Beyond its training data, the model stabilizes curved geometries, including vesicles, and captures complex cooperative phenomena such as bilayer and vesicle self-assembly and pore sealing. While limitations remain, particularly regarding chemical transferability, our results establish machine-learned coarse-grained potentials as a promising path towards robust and accurate CG models of lipids and membranes.

physics.bio-ph↗

Mechanical properties of chiral helical polymers-from single filaments to actin bundles

The mechanical properties of biological polymers are essential to their biological functions. Here, we introduce a highly coarse-grained model of helical filaments that preserves helicity and chirality while enabling mesoscale simulations. This is implemented in Cytosim, a widely-used open-source cytoskeleton simulation platform. We first show that we can predict the shape and mechanical properties of this helical filament, allowing for its fine control. We find the parameters that allow the resulting filament to accurately match the properties of actin filaments found experimentally}. Using this model of actin, we investigated the role of actin chirality in motor-driven dynamics. We first show that in two different experimental configurations, motor movement along a helical filament results in a chiral motion of the filament. In a bundle of helical filaments, dimeric motors exert torques on each filament, inducing collective behaviors in the bundle such as rotation, coiling, and helical buckling, reminiscent of those observed in filopodia. Together, these results demonstrate the central role of helicity and chirality in actin mechanics and motor-driven dynamics, and establish our framework as a powerful tool for mesoscale simulations. This framework can also be used for other helical filaments beyond actin.

physics.bio-ph↗

Finite-size scaling and cutoff functions of critical avalanches under cubic saturation

Power-law avalanche statistics characterize the branching bulk but leave open how finite systems terminate their largest events. Here we determine both the finite-size cutoff and the full crossover function connecting the branching regime to the large-event tail for an exchangeable absorbing event process motivated by Cauchy-stable recurrent dynamics. The Cauchy dynamical mean-field theory yields a linear-cubic low-activity drift, while a repeatable-event lift supplies absorbing branching fluctuations. Their competition at criticality generates a distinct finite-size edge controlled by cubic saturation. For avalanches initiated by a fixed microscopic seed, the rescaled edge measure connects the universal branching power law continuously to an exponential large-area tail. This full crossover admits an analytic spectral representation in terms of the boundary spectrum of a quartic Schrödinger operator on the half-line: high modes reconstruct the branching singularity, while the ground state fixes the far-tail decay. Exact finite-size calculations and Monte Carlo simulations confirm both the predicted cutoff behavior and the full spectral crossover.

physics.bio-ph↗