Particle interactions and lattice dynamics: Scenarios for efficient bidirectional stochastic transport?
M. Ebbinghaus, C. Appert-Rolland, L. Santen

TL;DR
This paper investigates how lattice dynamics and particle interactions influence the efficiency of bidirectional intracellular transport modeled by stochastic lattice systems, revealing cooperative effects that enhance transport performance.
Contribution
It demonstrates that both lattice dynamics and particle interactions can cooperatively improve transport efficiency, challenging previous assumptions about lane formation mechanisms.
Findings
Lanes formation based on modified attachment/detachment rates is not necessarily linked to efficient transport.
Lattice dynamics can lower the threshold for lane formation and enhance transport capacity.
Both lattice dynamics and interactions can synergistically improve bidirectional transport efficiency.
Abstract
Intracellular transport processes driven by molecular motors can be described by stochastic lattice models of self-driven particles. Here we focus on bidirectional transport models excluding the exchange of particles on the same track. We explore the possibility to have efficient transport in these systems. One possibility would be to have appropriate interactions between the various motors' species, so as to form lanes. However, we show that the lane formation mechanism based on modified attachment/detachment rates as it was proposed previously is not necessarily connected to an efficient transport state and is suppressed when the diffusivity of unbound particles is finite. We propose another interaction mechanism based on obstacle avoidance that allows to have lane formation for limited diffusion. Besides, we had shown in a separate paper that the dynamics of the lattice itself could…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
