Enhanced transport through confined channels by stationary and fluctuating potentials

Binding-sites which facilitate the transport of substrates across membranes are ubiquitous in membrane proteins. To understand this fundamental process in cells, we build up a synthetic membrane system consisting of microfluidic channels and colloidal particles. Holographic optical tweezers are used to modulate the potential energy landscape in those channels. We show how to extract the underlying energy potential by analysing local transition probabilities. Our method is applicable both to equilibrium systems and non-equilibrium steady states. Our method offers improved robustness when dealing with fragmented trajectories or small ensembles of data compared to other established approaches, such as probability density function and splitting probability. Meanwhile, we utilise the intensity distribution of the optical traps generated by holographic optical tweezers to estimate energy landscapes featuring high energy barriers where transitions rarely occur. We use this newly developed experimental system to mimic the functionality of membrane protein transporters that are known to alternate their substrate-binding sites between the extracellular and cytosolic side of the membrane. We study particle transport through a channel coupled with an energy well that oscillates its position between the two entrances of the channel deterministically and stochastically. Optimised particle transport across the channel is obtained by adjusting the oscillation frequency. At the optimal oscillation frequency, the translocation rate of particles through the channel is a hundred times higher with respect to free diffusion across the channel. Our findings reveal the effect of time dependent potentials on particle transport across a channel. This work adds a new tool for the investigation of highly controlled membrane transport processes at the micron scale. Our results are relevant for improving our understanding of membrane transport especially for microfluidics application