A Stochastic Model for Electron Transfer in Bacterial Cables

TL;DR

This paper introduces a stochastic model for electron transfer in bacterial communities, linking it to cellular energy states and enabling analysis of electron-based communication networks in microbial systems.

Contribution

It develops a novel stochastic model that connects electron transfer mechanisms to cell energetics and extends to multi-cell communities, fitting experimental data effectively.

Findings

Model accurately fits experimental data

Enables analysis of electron-based microbial communication

Provides a framework for larger community modeling

Abstract

Biological systems are known to communicate by diffusing chemical signals in the surrounding medium. However, most of the recent literature has neglected the electron transfer mechanism occurring amongst living cells, and its role in cell-cell communication. Each cell relies on a continuous flow of electrons from its electron donor to its electron acceptor through the electron transport chain to produce energy in the form of the molecule adenosine triphosphate, and to sustain the cell's vital operations and functions. While the importance of biological electron transfer is well-known for individual cells, the past decade has also brought about remarkable discoveries of multi-cellular microbial communities that transfer electrons between cells and across centimeter length scales, e.g., biofilms and multi-cellular bacterial cables. These experimental observations open up new frontiers in the design of electron-based communications networks in microbial communities, which may coexist with the more well-known communication strategies based on molecular diffusion, while benefiting from a much shorter communication delay. This paper develops a stochastic model that links the electron transfer mechanism to the energetic state of the cell. The model is also extensible to larger communities, by allowing for electron exchange between neighboring cells. Moreover, the parameters of the stochastic model are fit to experimental data available in the literature, and are shown to provide a good fit.