Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · physics/9902021

Collective motion of organisms in three dimensions

A. Czirok·M. Vicsek·T. Vicsek
View original source ↗Read source document ↗Export metadata ↓

Abstract

We study a model of flocking in order to describe the transitions during the collective motion of organisms in three dimensions (e.g., birds). In this model the particles representing the organisms are self-propelled, i.e., they move with the same absolute velocity. In addition, the particles locally interact by choosing at each time step the average direction of motion of their neighbors and the effects of fluctuations are taken into account as well. We present the first results for large scale flocking in the presence of noise in three dimensions. We show that depending on the control parameters both disordered and long-range ordered phases can be observed. The corresponding phase diagram has a number of features which are qualitatively different from those typical for the analogous equilibrium models.

Explore related subjects

physics.bio-phq-bio

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

A. Czirok, M. Vicsek, T. Vicsek. 1999-02-09. Collective motion of organisms in three dimensions. https://doi.org/10.1016/s0378-4371(98)00468-3

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

KEEP EXPLORING

Related papers

arXiv2026

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↗
arXiv2026

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↗
arXiv2026

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↗