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.

翻译：对基因表达动态的深入分析表明,基因表达动态显示基因调节神经网络(GNN)在基因调节神经网络(GNN)中的一种继承行为,该行为使细胞能够根据从环境和邻接细胞收到的信号做出决策。在这项研究中,我们提取了一个与一个恶性因素相关联的子网络,即Pyocyanin生产,作为调查GNN行为的一个案例。此外,我们利用图示神经网络(GNN)架构,模拟一个单一物种生物胶片,以揭示GNN在全生态系统决策中的作用。环境条件差异很大,我们证明所提取的GNNN是类似于该细胞自然决策过程的输入信号。GRN的神经网络行为可能导致更准确的细菌细胞模型活动预测模型,用以设计许多有利于生殖健康的网络应用,包括更精确的系统,以及更精确的遗传变异性数据。