Filament turnover tunes both force generation and dissipation to control long-range flows in a model actomyosin
William M McFadden1, Patrick M McCall2,3, Margaret L Gardel2,3,4
1Biophysical Sciences Program, University of Chicago, Chicago, Illinois, United States of America.
Plos Computational Biology
|December 19, 2017
Summary
Cellular morphogenesis relies on actomyosin cortical flow, driven by actin filaments and myosin motors. This study models how filament turnover and cross-link dynamics regulate stress and flow, revealing distinct regimes of cell movement.
Area of Science:
- Cell Biology
- Biophysics
- Computational Modeling
Background:
- Actomyosin-based cortical flow is crucial for cellular morphogenesis.
- This flow arises from actin filaments and myosin motors, balanced by network resistance.
- Local network remodeling is essential for sustained flow, but its precise role is unclear.
Purpose of the Study:
- To investigate how local remodeling, specifically filament turnover and cross-link dynamics, influences stress production and dissipation in actomyosin networks.
- To understand how these remodeling processes collectively shape long-range cortical flow.
- To identify different regimes of steady-state flow based on network remodeling parameters.
Main Methods:
- Development of a computational model for a cross-linked actin-myosin network.
- Incorporation of key elements: asymmetric filament compliance, heterogeneous motor activity, reversible cross-links, and filament turnover.
- Systematic analysis of how cross-link dynamics and filament turnover individually and collectively affect network stress and flow.
Main Results:
- Filament turnover is necessary for maintaining active stress and steady-state flow against resistance.
- Steady-state stress shows a non-monotonic dependence on filament lifetime, peaking at τm.
- Effective viscosity increases with filament lifetime up to τc, then depends primarily on cross-link dynamics.
- Multiple flow regimes emerge based on the interplay between stress and viscosity dependencies.
- Flow speed becomes insensitive to filament turnover when lifetimes are short, simplifying dependence on motor and cross-link dynamics.
Conclusions:
- The model provides a framework for understanding how cells regulate cortical flow through local network remodeling.
- Filament turnover and cross-link dynamics are critical, tunable parameters controlling cell shape and movement.
- Specific regimes of flow behavior are predicted based on the balance of remodeling processes.
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