MOLECULAR MECHANISMS OF NATURAL LIFESPAN VARIATION Which genes and gene networks are responsible of causing (or retarding) the aging process? Which of these components can be manipulated to maximize lifespan? Despite the fundamental nature of these questions, we have very limited understanding of the cellular mechanisms governing aging. From this perspective, our overarching goal is to apply systems biology approaches to construct a molecular network of longevity, based on multi-omics datasets from 87 natural yeast isolates. Yeast offers a rich resource of available genetic and molecular tools as well as established experimental paradigms for characterizing mechanisms of aging and longevity, and its interaction networks have been previously constructed. Towards this goal, we have generated three important sets of preliminary data. First, we evaluated replicative lifespan (RLS) of 87 natural isolates under three metabolic conditions and uncovered a wide diversity of natural variation in aging. Second, we sequenced the genomes of these strains to characterize their genetic diversity. Third, we found that dietary restriction and activation of mitochondrial respiration extend lifespan in some genotypes, while in others there is no response at all. Based on this data we will test the hypothesis that genetic variation and environmental factors coordinately regulate components of the transcriptional, translational and metabolic machinery, generating variation in dietary response and aging. Application of data integration methods to multiple omics resources will facilitate the discovery of underlying biological processes associated with longevity. We will test this hypothesis in the following Specific Aims: 1) Analyze transcriptomes, translatomes and metabolomes to link variation in these ‘endo-phenotypes’ to external ‘aging phenotypes’ under different metabolic conditions known to modulate lifespan. 2) Integrate the findings of -omics data and identify regulatory networks of lifespan control. This innovative approach of data aggregation and processing, multidimensional data analysis and network-based methods will aid in conceptualizing the molecular mechanisms that underlie natural variation of aging and aging-related perturbations at an unprecedented scale and level of detail. Finally, these studies will identify general trends in lifespan control by leveraging our extensive experience in systems approaches of life history of traits of more complex organisms. The application proposes a program for expanded training with established mentor and two co-mentors in network, systems, and computational biology to facilitate the PI's development into an independent investigator focusing on basic biology of aging. The experience, knowledge, and skills gained through the research plan and career development activities will carry the candidate forward towards a career as an independent investigator.