A probabilistic model of diffusion through a semi-permeable barrier

TL;DR

This paper develops a probabilistic framework using extended snapping out Brownian motion to model diffusion through semipermeable barriers, incorporating modifications like stochastic resetting and non-Markovian absorption.

Contribution

It introduces an extended snapping out Brownian motion model that generalizes diffusion through barriers, including time-dependent permeabilities and non-Markovian effects.

Findings

Extended model captures stochastic resetting effects

Incorporates non-Markovian membrane absorption

Provides a unified probabilistic framework

Abstract

Diffusion through semipermeable structures arises in a wide range of processes in the physical and life sciences. Examples at the microscopic level range from artificial membranes for reverse osmosis to lipid bilayers regulating molecular transport in biological cells to chemical and electrical gap junctions. There are also macroscopic analogs such as animal migration in heterogeneous landscapes. It has recently been shown that one-dimensional diffusion through a barrier with constant permeability $\kappa_0$ is equivalent to snapping out Brownian motion (BM). The latter sews together successive rounds of partially reflecting BMs that are restricted to either the left or right of the barrier. Each round is killed when its Brownian local time exceeds an exponential random variable parameterized by $\kappa_0$. A new round is then immediately started in either direction with equal probability. In this paper we use a combination of renewal theory, Laplace transforms and Green's function methods to show how an extended version of snapping out BM provides a general probabilistic framework for modeling diffusion through a semipermeable barrier. This includes modifications of the diffusion process away from the barrier (eg. stochastic resetting) and non-Markovian models of membrane absorption that kill each round of partially reflected BM. The latter leads to time-dependent permeabilities.