We introduce a formal framework for specifying, detecting, and generating spatial patterns in reaction diffusion networks. Our approach is based on a novel spatial superposition logic, whose semantics is defined over the quad-tree representation of a partitioned image. We demonstrate how to use rule-based classifiers to efficiently learn spatial superposition logic formulas for several types of patterns from positive and negative examples. We implement pattern detection as a model-checking algorithm and we show that it achieves very good results on test data sets which are different from the training sets. We provide a quantitative semantics for our logic and we develop computational framework where our quantitative model-checking algorithm works in synergy with a particle swarm optimization technique to synthesize the parameters leading to the formation of desired patterns in reaction diffusion networks.