Changes in the environment continuously challenge living organisms during their lifetime. A cell’s survival depends on its ability to coordinate a rapid and successful stress response when exposed to acute doses of damaging agents. Oxidative stress caused by an excess of reactive oxygen species, is known to damage cellular components. In humans, redox imbalance is associated withaging, cancer, atherosclerosis, Alzheimer’s and Parkinson’s disease among others. Therefore, studies investigating the cellular mechanisms employed in response to oxidative stress have markedly increased in recent years, especially using model organisms. The fission yeast Schizosaccharomyces pombe is a unicellular eukaryotic organism that possesses genome features and molecular pathways that are highly conserved in humans. Moreover, the limited redundancy of its genome make S. pombe well suited for phenotypic studies and the investigation of stress responses in particular. Notably, the fission yeast stress-activated protein kinase pathway is activated in multiple stress conditions including oxidative stress, and it activates transcription factors that are conserved in humans (Chapter 4). Several gene expression studies have uncovered the transcriptional program of fission yeast cells in response to oxidative stimuli. Thus, a solid basis of stress response data is available for this yeast and constitutes a valuable framework for further studies on gene expression in stress. Post-transcriptional control of gene expression is, however, less well understood. Only few reports describing the correlation between mRNA and protein levels in different species exist. Also, the vast majority of these studies were conducted at low time-resolution and primarily in normal growth conditions. We were interested in investigating the correlation between mRNA and protein expression and define their time dynamics in the oxidative stress response. Towards this goal, we measured the mRNA and protein levels in samples collected from exponentially growing fission yeast cultures at multiple points after cellular treatment with hydrogen peroxide (HP, 0.5 mM). The applied experimental design allowed us to measure both the activation and recovery phases of the response at a sufficiently high time resolution to model transcription and translation dynamics. Absolute expression levels (copies per cell) and time-resolved expression profiles for 4,972 mRNAs and 2,310 proteins were determined, and a web application for profile visualization was developed. We found a high correlation between mRNA and protein levels both at the steady state and the time of the maximum expression response. In most cases increase in protein abundance was concomitant with transcript induction, while the mRNA and protein levels of repressed genes were not correlated. Changes only at the level of mRNA (futile transcription) or protein (translational regulation) were common. For coherently induced proteins the time of maximum production rate was reached when mRNA levels peaked. Accordingly, for coherently repressed proteins the maximum degradation rate was often observed at the time of the minimum mRNA response (Chapter 5). To date, gene expression in the stress response of fission yeast cells to HP has been studied in batch cultures of different starting cell densities, while HP decay profiles in these populations have not been described. Also, contrary to the budding yeast, high-throughput growth profiling studies in normal and stress conditions are missing for fission yeast. Using time-resolved mRNA profiles of low and high cell density populations in stress, we found a close relationship between cell density and HP depletion rate, and thus, the exposure time and mRNA response dynamics. Based on the dependence of response times (or other time dynamics features) of mRNA profiles on cell density, we defined different transcriptional responses. We observed that most mRNAs of commonly regulated genes in stress reached the time of the maximum (or minimum) response earlier in cultures of higher densities (negative cell concentration-dependent response, CCDRn). Similar response dynamics in high and low cell populations (cell concentrationindependent response, CCIR) were often observed. Finally, core oxidative stress genes showed a positive correlation between the response time and cell density (positive cell concentration-dependent response, CCDRp). We also determined the rapid response of fission yeast cells to oxidative stress by assessing the maximum response times of all significant mRNA responses. We found gene functions associated with cellular redox homeostasis and the fate of the cell to become rapidly regulated in response to HP (Chapter 6). Moreover, we characterized the growth physiology of yeast cells in multiple conditions of HP stress and resolved individual growth variables with high precision for hundreds of segregants in a high-throughput setting. The extent of the dosage dependent, negative-effects on growth of segregant strains was observed to be non-trivially related to the stress phenotypes of the parental strains. Accordingly, segregants could be grouped by the extent of stress effects exerted on each growth variable. These findings are currently under investigation in a larger quantitative trait loci (QTL) study.