Performance scaling and trade-offs for collective motor-driven transport

TL;DR

This paper models collective motor-driven transport in cells, deriving how key performance metrics scale with the number of motors and revealing trade-offs that inform optimal motor team sizes for various cellular functions.

Contribution

It provides an analytical framework for understanding how transport efficiency, velocity, and precision depend on the number of motors, highlighting simple scaling laws and trade-offs.

Findings

Velocity and efficiency scale with the number of motors following simple laws.

Trade-offs exist between transport speed, precision, and energy use.

Optimal motor numbers depend on specific cellular transport priorities.

Abstract

Motor-driven intracellular transport of organelles, vesicles, and other molecular cargo is a highly collective process. An individual cargo is often pulled by a team of transport motors, with numbers ranging from only a few to several hundred. We explore the behavior of these systems using a stochastic model for transport of molecular cargo by an arbitrary number N of motors obeying linear Langevin dynamics, finding analytic solutions for the N-dependence of the velocity, precision of forward progress, energy flows between different system components, and efficiency. In two opposing regimes, we show that these properties obey simple scaling laws with N. Finally, we explore trade-offs between performance metrics as N is varied, providing insight into how different numbers of motors might be well-matched to distinct contexts where different performance metrics are prioritized.