Bayesian dictionary learning estimation of cell membrane permeability from surface pH data

TL;DR

This paper introduces a Bayesian dictionary learning algorithm to estimate cell membrane permeability from surface pH data, offering a more efficient alternative to particle filters in biochemical modeling.

Contribution

The paper presents a novel dictionary learning-based method for estimating membrane permeability, improving computational efficiency over previous particle filter approaches.

Findings

The new method accurately estimates permeability from surface pH data.

It is significantly faster than particle filter methods.

Applicable when membrane properties remain constant during data collection.

Abstract

Gas transport across cell membrane is a very important process in biochemistry which is essential for many crucial tasks, including cell respiration pH regulation in the cell. In the late 1990's, the suggestion that gasses are transported via preferred gas channels embedded into the cell membrane challenged the century old Overton's theory that gases pass through the lipid cell membrane by diffusing across the concentration gradient. Since experimental evidence alone does not provide enough evidence to favor one of the proposed mechanisms, mathematical models have been introduced to provide a context for the interpretation of laboratory measurement. Following up on previous work where the membrane permeability was estimated using particle filter, in this article we propose an algorithm based on dictionary learning for estimating cell membrane permeability. Computed examples illustrate that the novel approach, which can be applied when the properties of the membrane do not change in the course of the data collection process, is computationally much more efficient than particle filter.