Inferring Gene Regulatory Neural Networks for Bacterial Decision Making in Biofilms

TL;DR

This paper models bacterial gene regulatory networks as neural networks using graph neural networks to understand decision-making in biofilms, revealing potential for predictive modeling and bio-hybrid computing.

Contribution

It introduces a novel approach to analyze bacterial gene regulatory networks as neural networks using GNNs, linking biological decision-making to computational models.

Findings

GRNN behaviors are identified within bacterial GRNs.

The GNN model accurately predicts bacterial responses under different conditions.

Potential applications include disease control and bio-hybrid computing systems.

Abstract

Bacterial cells are sensitive to a range of external signals used to learn the environment. These incoming external signals are then processed using a Gene Regulatory Network (GRN), exhibiting similarities to modern computing algorithms. An in-depth analysis of gene expression dynamics suggests an inherited Gene Regulatory Neural Network (GRNN) behavior within the GRN that enables the cellular decision-making based on received signals from the environment and neighbor cells. In this study, we extract a sub-network of \textit{Pseudomonas aeruginosa} GRN that is associated with one virulence factor: pyocyanin production as a use case to investigate the GRNN behaviors. Further, using Graph Neural Network (GNN) architecture, we model a single species biofilm to reveal the role of GRNN dynamics on ecosystem-wide decision-making. Varying environmental conditions, we prove that the extracted GRNN computes input signals similar to natural decision-making process of the cell. Identifying of neural network behaviors in GRNs may lead to more accurate bacterial cell activity predictive models for many applications, including human health-related problems and agricultural applications. Further, this model can produce data on causal relationships throughout the network, enabling the possibility of designing tailor-made infection-controlling mechanisms. More interestingly, these GRNNs can perform computational tasks for bio-hybrid computing systems.