The article discusses the use of systems-biology approaches to understand the relationship between nutrient conditions and cell growth in budding yeast. It highlights the challenge of identifying molecular pathways that underlie the translation of genotypic perturbations into phenotypic outputs. The study uses a chemostat to control nutrient levels, cell population size, and waste products, allowing for the analysis of transcriptomic, proteomic, and metabolomic data under varying growth conditions. The research focuses on the effects of different nutrients being limiting and how they influence cell growth. The study reveals that carbon limitation has the most dramatic effect on gene expression, with a large number of genes up- or downregulated. The analysis also shows that growth-rate-dependent effects are more robust, with a greater number of genes involved and a wider range of responses. The integration of transcriptomic, proteomic, and metabolomic data provides a more comprehensive view of the cell state. However, the metabolomic data are limited, and there is little correlation between enzyme levels and corresponding metabolite levels. The study suggests that metabolite levels are controlled by systems-level properties of metabolic pathways. The research contributes to efforts to understand how eukaryotic cells alter their state in response to external conditions. The study also highlights the importance of integrating datasets related to processes that are well understood, such as the cell cycle and primary metabolism. The findings suggest that systems-biology approaches are essential for understanding the complex relationships between nutrient conditions and cell growth. The study is part of a broader effort to integrate data across multiple 'omes' to gain a systems-level understanding of cellular responses.The article discusses the use of systems-biology approaches to understand the relationship between nutrient conditions and cell growth in budding yeast. It highlights the challenge of identifying molecular pathways that underlie the translation of genotypic perturbations into phenotypic outputs. The study uses a chemostat to control nutrient levels, cell population size, and waste products, allowing for the analysis of transcriptomic, proteomic, and metabolomic data under varying growth conditions. The research focuses on the effects of different nutrients being limiting and how they influence cell growth. The study reveals that carbon limitation has the most dramatic effect on gene expression, with a large number of genes up- or downregulated. The analysis also shows that growth-rate-dependent effects are more robust, with a greater number of genes involved and a wider range of responses. The integration of transcriptomic, proteomic, and metabolomic data provides a more comprehensive view of the cell state. However, the metabolomic data are limited, and there is little correlation between enzyme levels and corresponding metabolite levels. The study suggests that metabolite levels are controlled by systems-level properties of metabolic pathways. The research contributes to efforts to understand how eukaryotic cells alter their state in response to external conditions. The study also highlights the importance of integrating datasets related to processes that are well understood, such as the cell cycle and primary metabolism. The findings suggest that systems-biology approaches are essential for understanding the complex relationships between nutrient conditions and cell growth. The study is part of a broader effort to integrate data across multiple 'omes' to gain a systems-level understanding of cellular responses.