The vision of biocomputing is to develop computing paradigms using biological systems, ranging from micron-level components to collections of cells, such as organoids. This paradigm shift exploits hidden natural computing properties, developing miniaturized wet computing devices deployable in harsh environments, and exploring designing novel energyefficient systems. Parallelly, we witness the emergence of AI hardware including neuromorphic processors aiming to improve computational capacity. This study brings together the concept of bio-computing and neuromorphic systems by focusing on the Bacterial gene regulatory networks and their transformation into Gene Regulatory Neural Networks (GRNNs) that can be used for biocomputing. We explore the intrinsic properties of gene regulations, map this to a gene-perceptron function, and propose an application-specific sub-GRNN search algorithm that maps the network structure to match a problem. Focusing on the model organism Escherichia coli (E. coli), the base-GRNN is initially extracted and validated for accuracy. Subsequently, a comprehensive feasibility analysis of the derived GRNN confirms its computational prowess in classification and regression tasks. Furthermore, we discuss the possibility of performing a wellknown digit classification task as a use case. Our analysis and simulation experiments show promising results in offloading computation to GRNN in bacterial cells, advancing wet-neuromorphic computing using natural cells.