Investigating metabolomic differences in selected yeast strains via data integration from multiple sources

Multi-omics represents the cutting-edge frontier in molecular research, with the ultimate goal of integrating diverse information derived from various -omics datasets (genomics, transcriptomics, proteomics, metabolomics, etc.) collected from the same organism (or even an ecological community). The primary challenge lies in coherently integrating these multi-source datasets. While integrating genomic and transcriptomic data could be considered relatively straightforward, connecting genomic data with metabolomic data proves far more complex, since a metabolic reaction can be controlled by several genes, making it very difficult to forecast the expected concentration of each metabolite. This thesis explores various approaches for integrating genomics and metabolomics data in selected yeast strains, analyzing their strengths and weaknesses while addressing potential future developments. Specifically, both top-down and bottom-up approaches were implemented. The top-down approach identifies key metabolites using network theory-based indices applied to suitably reconstructed metabolic maps. Conversely, the bottom-up approach tries to map significant genomic differences onto a general metabolic map, aiming to infer potential connections to the experimentally observed metabolic variations. Unfortunately, both approaches are hindered by missing data, suggesting that the linkage between genomics and metabolomics could be far more complex than expected.