Genome-scale metabolic models (GMMs) have been recognized as being powerful tools for capturing system-wide metabolic phenomena and connecting those phenomena to underlying genetic and regulatory changes. By formalizing and codifying the relationship between the levels of gene expression, protein concentration, and reaction flux, metabolic models are able to translate changes in gene expression to their effects on the metabolic network. A number of methods are then available to interpret how those changes are manifest in the metabolic flux distribution. In addition to discussing how gene expression datasets can be interpreted in the context of a metabolic model, this chapter discusses two of the most common methods for analyzing the resulting metabolic network. The chapter begins by demonstrating how a typical microarray dataset can be processed for incorporation into a GMM of the yeast Saccharomyces cerevisiae . Once the expression states of the reactions in the model are available, the method of directly trimming the metabolic model by removing or constraining reactions with low expression states is demonstrated. This is the simplest and most direct approach to interpret gene expression states, but it is prone to overvaluing the effects of down regulation and it can propagate false negative errors. We therefore also include a more advanced method that uses a mixed-integer linear programming optimization to find a flux distribution that maximizes agreement with global gene expression states. Sample MATLAB code for use with the COBRA toolbox is provided for all methods used.