We describe the use of model-driven analysis of multiple data types relevant to transcriptional regulation of metabolism to discover novel regulatory mechanisms in Saccharomyces cerevisiae. We have reconstructed the nutrient-controlled transcriptional regulatory network controlling metabolism in S. cerevisiae consisting of 55 transcription factors regulating 750 metabolic genes, based on information in the primary literature. This reconstructed regulatory network coupled with an existing genome-scale metabolic network model allows in silico prediction of growth phenotypes of regulatory gene deletions as well as gene expression profiles. We compared model predictions of gene expression changes in response to genetic and environmental perturbations to experimental data to identify potential novel targets for transcription factors. We then identified regulatory cascades connecting transcription factors to the potential targets through a systematic model expansion strategy using published genome-wide chromatin immunoprecipitation and binding-site-motif data sets. Finally, we show the ability of an integrated metabolic and regulatory network model to predict growth phenotypes of transcription factor knockout strains. These studies illustrate the potential of model-driven data integration to systematically discover novel components and interactions in regulatory and metabolic networks in eukaryotic cells.[Supplemental material is available online at www.genome.org.] (Giaever et al. 2002) data represent the states and outputs of these networks. Connecting large-scale component and interaction information to data on system states in order to facilitate the interpretation of both data types is a major challenge in systems biology. The data integration and interpretation task is made challenging by the incompleteness and noisiness of large-scale data sets (Grunenfelder and Winzeler 2002).Given these issues with large-scale data sets, systematic inclusion of literature-derived information on network structures into the analysis represents an appealing alternative to purely data-driven approaches. The widespread availability of component and biochemical interaction information in the primary literature has enabled the reconstruction of chemically and biologically consistent mathematical descriptions of biochemical networks in well-studied model organisms Price et al. 2004). These network models can then be used to predict changes in system states in response to genetic and environmental perturbations. Furthermore, model predictions can be directly compared with experimental data obtained, for example, by metabolic flux or gene expression profiling Price et al. 2004). As a result of these comparisons, modifications to the biochemical network model that would improve its ability to predict system states can be identified to iteratively improve the model.In the case of metabolic networks, the network reconstruction step can now be routinely done and has been accomplished for a number of key model organisms including Escherich...

The experiments reported in this research paper aimed to determine the effect of supplementing different forms of L-methionine (L-Met) and acetate on protein synthesis in immortalized bovine mammary epithelial cell line (MAC-T cells). Treatments were Control, L-Met, conjugated L-Met and acetate (CMA), and non-conjugated L-Met and Acetate (NMA). Protein synthesis mechanism was determined by omics method. NMA group had the highest protein content in the media and CSN2 mRNA expression levels (P < 0.05). The number of upregulated and downregulated proteins observed were 39 and 77 in L-Met group, 62 and 80 in CMA group and 50 and 81 in NMA group from 448 proteins, respectively (P < 0.05). L-Met, NMA and CMA treatments stimulated pathways related to protein and energy metabolism (P < 0.05). Metabolomic analysis also revealed that L-Met, CMA and NMA treatments resulted in increases of several metabolites (P < 0.05). In conclusion, NMA treatment increased protein concentration and expression level of CSN2 mRNA in MAC-T cells compared to control as well as L-Met and CMA treatments through increased expression of milk protein synthesis-related genes and production of the proteins and metabolites involved in energy and protein synthesis pathways.