We propose a neural network based architecture to infer which parameters are fundamental, and their values, for producing specific instances of spatial patterns formed through cell colony growth. The system is trained on variations of the same pattern to recognize features that characterize it. Furthermore, selecting important parameters within our study mainly focuses on the fact that cells communicate. We use two forms of this communication as fundamental in finding the parameter values: bacterial conjugation, and environmental signals. The neural network is trained during 3000 epochs to identify the pattern class and specific parameter values needed to reproduce the desired pattern. These parameters are then inputted into a gro simulation to assess proximity to the original pattern. Our architecture achieved a 5% error upon pattern reproduction.