Molecular motor organization and mobility on cargos can overcome a tradeoff between fast binding and run length
Bovyn, M.; Gross, S.; Allard, J.
Show abstract
Cellular cargos, including lipid droplets and mitochondria, are transported along microtubules using molecular motors such as kinesins. Many experimental and computational studies of cargos with rigidly attached motors, in contrast to many biological cargos that have lipid surfaces that may allow surface mobility of motors. We extend a mechanochemical 3D computational model by adding coupled-viscosity effects to compare different motor arrangements and mobilities. We show that organizational changes can optimize for different objectives: Cargos with clustered motors are transported efficiently, but are slow to bind to microtubules, whereas those with motors dispersed rigidly on their surface bind microtubules quickly, but are transported inefficiently. Finally, cargos with freely-diffusing motors have both fast binding and efficient transport, although less efficient than clustered motors. These results suggest that experimentally observed changes in motor organization may be a control point for transport.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Modeling myosin Va liposome transport through actin filament networks reveals a percolation threshold that modulates transport properties 97%
- A hybrid stochastic-deterministic mechanochemical model of cell polarization 96%
- Force-insensitive myosin-I enhances endocytosis robustness through actin network-scale collective ratcheting 96%
Similar papers in this journal
- Conflicting roles of cell geometry, microtubule deflection and orientation-dependent dynamic instability in cortical array organization 96%
- Actin network heterogeneity tunes activator-inhibitor dynamics at the cell cortex 96%
- Emergent Programmable Behavior and Chaos in Dynamically Driven Active Filaments 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.