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.read more
Citations
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References
More filters
Journal ArticleDOI
Self-organized criticality: An explanation of the 1/ f noise
TL;DR: It is shown that dynamical systems with spatial degrees of freedom naturally evolve into a self-organized critical point, and flicker noise, or 1/f noise, can be identified with the dynamics of the critical state.
Journal ArticleDOI
Dynamic instability of microtubule growth
TL;DR: It is reported here that microtubules in vitro coexist in growing and shrinking populations which interconvert rather infrequently and this dynamic instability is a general property of micro Tubules and may be fundamental in explaining cellular microtubule organization.
Book
Introduction to Mathematical Statistics
TL;DR: In this article, the authors present a list of common distributions of probability and distribution of likelihood for Bayesian models. But they do not discuss the relation between distributions and normal models.
Book
Mechanics of Motor Proteins and the Cytoskeleton
Jonathon Howard,RL Clark +1 more
TL;DR: The Motility Models: From Crossbridges to Motion chapter describes the building blocks of the Cytoskeleton and some of the mechanisms used in its manufacture are described.
Book
Molecular Modelling: Principles and Applications
TL;DR: In this article, the authors introduce the concept of Computational Quantum Mechanics (CQM) and present four challenges in molecular modelling: Free Energies, Solvation, Reactions and Solid-State Defects.