  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.
