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Bacterial cells are sensitive to a range of external signals used to learnthe environment. These incoming external signals are then processed using aGene Regulatory Network (GRN), exhibiting similarities to modern computingalgorithms. An in-depth analysis of gene expression dynamics suggests aninherited Gene Regulatory Neural Network (GRNN) behavior within the GRN thatenables the cellular decision-making based on received signals from theenvironment and neighbor cells. In this study, we extract a sub-network of\textit{Pseudomonas aeruginosa} GRN that is associated with one virulencefactor: pyocyanin production as a use case to investigate the GRNN behaviors.Further, using Graph Neural Network (GNN) architecture, we model a singlespecies biofilm to reveal the role of GRNN dynamics on ecosystem-widedecision-making. Varying environmental conditions, we prove that the extractedGRNN computes input signals similar to natural decision-making process of thecell. Identifying of neural network behaviors in GRNs may lead to more accuratebacterial cell activity predictive models for many applications, includinghuman health-related problems and agricultural applications. Further, thismodel can produce data on causal relationships throughout the network, enablingthe possibility of designing tailor-made infection-controlling mechanisms. Moreinterestingly, these GRNNs can perform computational tasks for bio-hybridcomputing systems.

Inferring Gene Regulatory Neural Networks for Bacterial Decision Making in Biofilms