Escape from a channel

A challenge in biophysics is to devise mathematically tractable descriptions of complex processes in molecular and cellular biology. Twenty-five years ago, an elegant formalism was posited to describe channel-facilitated transport across biological membranes. This seminal work derived an effective escape rate that depends on the channel geometry and the diffusivity inside and outside of the channel. This prior escape rate formula has been used in many subsequent investigations to study a variety of biophysical systems. In this paper, we derive a new escape rate formula and rigorously show its validity in various parameter regimes. Our analysis resolves some counterintuitive predictions of the prior formula. We conclude that the prior formula is appropriate in limited biophysical circumstances.

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Publication Details

Journal
The Journal of Chemical Physics
Published
2026-02-12
DOI
https://doi.org/10.1063/5.0313333
Primary Topic
Molecular Communication and Nanonetworks
Type
article
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article

Escape from a channel

Sean D. Lawley
The Journal of Chemical Physics
Molecular Communication and Nanonetworks
article

Escape from a channel

Sean D. Lawley
article en

Abstract

A challenge in biophysics is to devise mathematically tractable descriptions of complex processes in molecular and cellular biology. Twenty-five years ago, an elegant formalism was posited to describe channel-facilitated transport across biological membranes. This seminal work derived an effective escape rate that depends on the channel geometry and the diffusivity inside and outside of the channel. This prior escape rate formula has been used in many subsequent investigations to study a variety of biophysical systems. In this paper, we derive a new escape rate formula and rigorously show its validity in various parameter regimes. Our analysis resolves some counterintuitive predictions of the prior formula. We conclude that the prior formula is appropriate in limited biophysical circumstances.

The Journal of Chemical PhysicsVol. 164(6)
University of Utah (US)
Openalex Percentile: Top 59%
Molecular Communication and Nanonetworks
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