The yeast Saccharomyces cerevisiae is a model organism in biology, being widely used in fundamental research, the first eukaryotic organism to be fully sequenced and the platform for the development of many genomics techniques. Therefore, it is not surprising that S. cerevisiae has also been widely used in the field of systems biology during the last decade. This thesis investigates S. cerevisiae growth physiology and DNA damage response by using a systems biology approach. Elucidation of the relationship between growth rate and gene expression is important to understand the mechanisms regulating cell growth. In order to study this relationship, we have grown S. cerevisiae cells in chemostat at defined growth rates and measured the transcriptional response. We have applied a complex experimental design, involving three factors: specific growth rate, oxygen availability and nutrient limitation. We have identified 268 growth rate dependent genes. These genes were used to identify key areas of the metabolism around which expression changes were significantly associated and we found nucleotide synthesis and ATP producing and consuming reactions. Moreover, by scoring the significance of overlap between growth rate dependent genes and known transcription factor (TF) target sets, we identified 13 TFs, involved in stress response, cell cycle and ribosome biogenesis, that appeared to coordinate the response at increasing growth rates. Therefore, in this study we have identified a more conservative set of growth dependent genes by using a multi-factorial experimental design. Moreover, new insights into the metabolic response and transcriptional regulation of these genes have been provided by using systems biology tools (Chapter 3). One of the prerequisite of systems biology should be the standardization and reproducibility of experimental and analytical techniques, in order to allow the comparison of data generated in different laboratories. With the aim of addressing this aspect, we have collaborated in a large study involving ten laboratories, constituting the Yeast Systems Biology Network (YSBN). S. cerevisiae cultivations were performed in a single laboratory and samples were sent to the other partners. The experimental design involved two factors: strain (CEN.PK113-7D and YSBN2) and growth condition (batch and chemostat). Transcriptome was measured with four different platforms (Affymetrix, Agilent, qPCR and TRAC), metabolome was analyzed in seven laboratories, using different protocols, and enzyme activities were determined in two different laboratories. The comparison of the analyses showed that reproducibility of the results was affected by the laboratory and the protocol used. Transcription and enzyme activity analyses gave consistent results, while metabolite level measurements showed some variability. Therefore, even though the source of biomass was unique, the reproducibility of data appeared to be a challenging task. Nevertheless, we were able to perform an integrative analysis and discover that the lower biomass yield of CEN.PK113-7D was due to higher protein turnover than YSBN2; this finding would not be achievable using a single omics dataset. Moreover, the generated datasets are a valuable resource for the yeast systems biology community (Chapter 4). Upon DNA damage, S. cerevisiae cells respond activating the so-called cell cycle checkpoints that promote damage repair and viability. The activation of these checkpoints depends on kinase cascades and regulation of transcription is one of the responses elicited by checkpoint activation. Therefore, we have decided to investigate the transcriptional and phenotypic responses to the alkylating agent methyl methanesulfonate (MMS) of mutant strains carrying deletions of genes encoding protein kinases (Mec1, Tel1, Rad53, Dun1, Chk1, Alk1) and protein phosphatases (Ptc3, Pph3, Oca1) involved in DNA damage response (DDR). We have discovered a prominent role for Rad53, Mec1 and Tel1 in transcriptional response. Moreover, we have shown for the first time the important role of Oca1 at the transcriptional level. We have built a comprehensive network of the central DDR pathway by integrating data from different cellular levels and identified regulatory circuits involving key players of this pathway. Integration of transcriptional and phenotypic data allowed us to discover sets of genes whose expression levels correlate with growth rates upon MMS treatment. Finally, we have also investigated the role of non protein-coding RNAs in DNA damage response (Chapter 5). When DNA damage is repaired, cells restart the cell cycle and resume growth. This process is called damage recovery. In S. cerevisiae, the molecular mechanism of recovery relies on dephosphorylation of Rad53 by protein phosphatases (PPs), that, in case of recovery from MMS-induced damage, are Ptc2, Ptc3 and Pph3. In order to elucidate the relationship between Rad53 and PPs, we have generated strains carrying mutations in Rad53 domains (SCD1 and FHA1) and deletion of genes encoding the PPs. Then, we have investigated the Rad53 phosphorylation status and the phenotype of these mutant strains. This study has allowed us to propose a role for thethreonine 8 of Rad53-SCD1 domain and its Ptc2/3-mediated dephosphorylationduring MMS recovery (Chapter 6).