Glycosylation of proteins is one of the most crucial post-translational modifications. In order to access system-level and state-dependent data related to the regulation of glycosylation events, we cultivated yeast cell strains each harboring a selected conditional knockdown construct for a gene (either SEC53, VRG4 or DPM1) related to GDP-mannose synthesis or its utilization in glycan biosynthesis. In order to carry this out efficiently, we developed automated sampling from bioreactor cultivations, a collection of in silico workflows for data analysis as well as their integration into a large data warehouse. Using the above-mentioned approaches, we could show that conditional knocking down of transcripts related to GDP-mannose synthesis or transportation led to altered levels of over 300 transcripts. These transcripts and their corresponding proteins were characterized by their gene ontology (GO) annotations, and their putative transcriptional regulation was analyzed. Furthermore, novel pathways were generated indicating interactions between GO categories with common proteins, putative transcriptional regulators of such induced GO categories, and the large protein–protein interaction network among the proteins whose transcripts indicated altered expression levels. When these results are always added to an ever-expanding data warehouse as annotations, they will incrementally increase the knowledge of biological systems.