Tuning the kinetics of intracellular transport

TL;DR

This paper demonstrates that reinforcement learning protocols can be used to precisely control intracellular cargo transport by kinesin motors, enabling targeted manipulation in biological environments with limited control options.

Contribution

The study introduces reinforcement learning-based methods for tuning intracellular transport dynamics, a novel approach in the field of cellular biophysics.

Findings

Reinforcement learning protocols enable localized cargo transport control.

Optical tweezers can implement these control protocols experimentally.

Protocols are feasible within realistic biological parameters.

Abstract

A variety of complex mechanisms, from chemical reaction pathways to active fluctuations, orchestrate molecular transport in intracellular environments. Despite significant recent progress in visualizing and probing these processes, little is known about how tunable the resulting dynamics is through external physical controllers. Here, we demonstrate that coarse-grained, reinforcement learning-based protocols can be developed to achieve highly localized and targeted cargo transport by kinesin motors on intracellular tracks. These protocols can be implemented in practice using optical tweezers, and their feasibility is showcased within experimentally relevant parameter regimes. Our results open new avenues for targeted control of intracellular transport processes, especially when opportunities for control are limited.