This thesis describes the development of the application of modular regulation analysis, a subset of metabolic control analysis to microarray data. Microarray experiments measure complex changes in the abundance of many mRNAs under different conditions. Current analysis methods, such as clustering, cannot distinguish between direct and indirect effects on expression, or calculate the relative importance of mRNAs in effecting responses. Modular regulation analysis of microarray data reveals and quantifies which mRNA changes are important for cellular responses.  The mRNAs are clustered, then how perturbations alter each cluster (integrated response co-efficients) and how strongly those clusters affect an output response is calculated (elasticity co-efficients). The product of these values quantifies how an input changes a response through each cluster (partial response co-efficients). Once identified, important clusters that mediate a large proportion of the response may suggest targets for investigation of, for example, disease mechanisms, and way of modifying that response, such as potential knockout, overexpression or drug targets. Two published datasets were used throughout the development of the method. This determined the requirements of a suitable dataset, and involved the creation of a test to exclude problematic experiments from the dataset. Analyses of the two datasets using the final method reveal that two mRNA clusters transmit most of the response of yeast doubling time to galactose; one contains mainly galactose metabolic genes, and the other a regulatory gene. Analysis of the response of yeast relative fitness to 2-deocy-D-glucose reveals that control is distributed between several mRNA clusters. Monte Carlo analysis revealed that the co-efficients were not statistically significant, due to the large amount of experimental error in the dataset. However, modular regulation analysis should become more applicable in practice as microarray technology is improving rapidly.