This chapter describes the use of genetic programming to evolve a fuzzy rule base to model gene expression. We describe the problem of genetic regulation in details and offer some reasons as to why many computational methods have difficulties in modeling it. We describe how a fuzzy rule base can be applied to this problem as well as how genetic programming can be used to evolve a fuzzy rule base to extract explanatory rules from microarray data obtained in the real experiments, which give us data sets that have thousands of features, but only a limited number of measurements in time. The algorithm allows for the insertion of prior knowledge, making it possible to find sets of rules that include the relationships between genes that are already known.