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Open AccessJournal ArticleDOI

Understanding cytoskeletal avalanches using mechanical stability analysis

TLDR
In this article, the authors use agent-based simulations of cytoskeletal self-organization to study fluctuations in the network's mechanical energy and find that the changes in the localization of tension and the projections of the network motion onto the vibrational normal modes are asymmetrically distributed for energy release and accumulation.
Abstract
Eukaryotic cells are mechanically supported by a polymer network called the cytoskeleton, which consumes chemical energy to dynamically remodel its structure. Recent experiments in vivo have revealed that this remodeling occasionally happens through anomalously large displacements, reminiscent of earthquakes or avalanches. These cytoskeletal avalanches might indicate that the cytoskeleton's structural response to a changing cellular environment is highly sensitive, and they are therefore of significant biological interest. However, the physics underlying "cytoquakes" is poorly understood. Here, we use agent-based simulations of cytoskeletal self-organization to study fluctuations in the network's mechanical energy. We robustly observe non-Gaussian statistics and asymmetrically large rates of energy release compared to accumulation in a minimal cytoskeletal model. The large events of energy release are found to correlate with large, collective displacements of the cytoskeletal filaments. We also find that the changes in the localization of tension and the projections of the network motion onto the vibrational normal modes are asymmetrically distributed for energy release and accumulation. These results imply an avalanche-like process of slow energy storage punctuated by fast, large events of energy release involving a collective network rearrangement. We further show that mechanical instability precedes cytoquake occurrence through a machine-learning model that dynamically forecasts cytoquakes using the vibrational spectrum as input. Our results provide a connection between the cytoquake phenomenon and the network's mechanical energy and can help guide future investigations of the cytoskeleton's structural susceptibility.

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Journal ArticleDOI

Membrane-MEDYAN: Simulating Deformable Vesicles Containing Complex Cytoskeletal Networks

TL;DR: A triangulated membrane model is introduced that accounts for the membrane's elastic properties, as well as for membrane-filament steric interactions, and paves the way for simulations of biological systems involving intricate membrane-cytoskeleton interactions, such as those occurring at the leading edge and the cortex.
Journal ArticleDOI

On Stretching, Bending, Shearing, and Twisting of Actin Filaments I: Variational Models

TL;DR: In this article , a variational model of the Cosserat theory of elastic rods is proposed to describe a smooth filament shape with six degrees of freedom at every point on the filament's backbone, and the strain energy function is calculated without resorting to a small-angle expansion.
Journal ArticleDOI

Nucleation causes an actin network to fragment into multiple high-density domains.

TL;DR: In this paper , the authors use the simulation platform for active matter MEDYAN to generate 2000 s long stochastic trajectories of actin networks, under varying Arp2/3 concentrations, in reaction volumes of biologically meaningful size.
Journal ArticleDOI

Computational simulations reveal that Abl activity controls cohesiveness of actin networks in growth cones

TL;DR: In this article , a comparison of computational simulations to in vivo data suggests that Abl kinase and Arp2/3 expand actomyosin networks by fragmenting them into multiple domains, thus toggling the axon between states of local versus global internal connectivity.
Journal ArticleDOI

Measuring Cytoskeletal Mechanical Fluctuations and Rheology with Active Micropost Arrays

TL;DR: Techniques are described to track the magnetic microposts’ motion with nanometer precision at up to 100 video frames per second to measure the local cellular rheology at well‐defined positions and special‐purpose software routines to reduce and analyze these data.
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Journal ArticleDOI

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