The role of filament recycling in stable flows of 2D active networks
Committee meeting
William McFadden 2016
How to change your own shape
how can an organism rearrange itself internally and externally?
Don Kane (U. Rochester)
Cellular processes are the drivers of macroscopic reorganization
Yumura et al. 2013
ed munro
crawling
division
polarization
cellular reorganization via cortical flows
Mmayer et al. 2010
ed munro
flows of cortical actomyosin cytoskeleton
actin filaments
and myosin motors
2D disordered network
flows of cortical actomyosin cytoskeleton
Actomyosin networks generate forces and spontaneously deform
Murrell and Gardel 2012
flow is driven by internal force imbalances
Passive
Active
Mayer et al. 2010
filament recycling: key to sustaining flows
F-actin filaments are highly dynamic
cellular mechanisms of filament recycling
disassembly takes places in 100 s
Kuey et al. JCB 2008
in vivo, the lifetime can be even shorter
during division
pre-division
Robin et al. 2012
Preventing filament recycling stalls flows or leads to cortical tearing
Jon Michaux
japlakinolide
goal: understand what sets flow rate
Passive
Active
stress
viscosity
approach: determine what sets network viscosity and internal stress
Probing passive viscosity of 2D filament networks in presence of applied force
frictional cross-link stress dissipation
measuring passive network deformation
position
x velocity
This led to typical 3 phase deformation
\tau_c
1/\eta
G_0
G0 agrees with prior theoretical prediction
Head Levine Mackintosh, PRL 2003
estimate of viscosity of networks with cross-link relaxation
\tau_c
1/\eta
what happens when we introduce highly simplified filament recycling?
filament recycling led to faster strains
position
x velocity
x velocity
no recycling
tau = 10s
Only with fast enough recycling, the network's effective viscosity decreased
\tau_x \approx 100\tau_c
Measuring deformation and stress buildup in 2D active filament networks
build a minimal model of active networks
asymmetric filament compliance
L
Storm and Nelson PRE 2003
Active networks transiently contract
free boundary
and generate a transient net stress
fixed boundary
\tau_a
recycling tunes the steady state stress
\tau_a
biphasic dependence on recycling time
Combining passive viscosity and active stress to predict 1D flows
recap: estimates for stress and viscosity
recall the dependence on these two important timescales
equations for viscosity and stress can be combined to predict flow speed
\dot{\gamma}=\frac{\sigma}{\eta}
strain rate
viscosity
stress
to test our theory we generated simulations of polarized networks
x velocity
x velocity
position
1000s
\tau_r
33s
active region
charting steady state flow dependence on recycling timescale
dependence of flow speed on recycling qualitatively matches theoretical prediction
flow simulations recapitulated the predicted interplay of these two effects
inert
tearing
conclusions and future directions
this work offers a theoretical framework for understanding cortical flows
it sets up some experiments to test both quantitative and qualitative predictions
it might help explain what needs to be controlled to generate synthetic systems
a theoretical framework
is "X experiment" important for tuning actomyosin flow?
experimental tests
synthetic systems
