Lipid metabolism, organization, and transport have been implicated in numerous human diseases including atherosclerosis, diabetes, and cancer, among others. Saccharamoyces cerevisiae is a model system for understanding the molecular background leading to possible human disease states. To globally map the organization and interactions of cellular networks that control lipid metabolism in S. cerevisiae, we applied a systems approach that integrated observations across multiple cellular component layers (mRNAs, lipids, metabolites, fluxes) and data from protein interaction databases with metabolic network topology. Correlation analysis revealed causal relationships and network motifs among significantly changing genes-lipids/metabolites and transcription factors with enriched target sets. Although biosynthetic metabolic genes were enriched relative to the genome, only 6 of 2351 significant correlations involve direct connections between genes and lipids/metabolites expected from a bipartite representation of a genome scale metabolic model. The correlation analysis was refined to answer the question of whether or not the biosynthetic genes connected to metabolites/lipids in the bipartite metabolic map are significantly correlated. We show that at a global level there is a high correlation between lipid levels in the cell and transcription of genes involved in lipid biosynthesis, pointing to a high degree of transcriptional regulation of metabolic fluxes towards biosynthesis of structural lipids. This is in contrast to the biosynthesis of amino acids where there is both transcriptional regulation and regulation at the metabolic level (both hierarchical and metabolic regulation). Our results contribute to the emerging view that the relationships between mRNA expression, metabolite and lipid levels, and fluxes are complex. Moreover, they demonstrate strategies for using metabolic network topology to upgrade the information content in high-throughput measurements.