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dc.contributor.advisorEllington, Andrew D.en
dc.contributor.advisorMarcotte, Edward M.en
dc.creatorNarayanaswamy, Rammohan, 1978-en
dc.date.accessioned2008-08-29T00:22:48Zen
dc.date.available2008-08-29T00:22:48Zen
dc.date.issued2008-05en
dc.identifierb70687377en
dc.identifier.urihttp://hdl.handle.net/2152/3967en
dc.descriptiontexten
dc.description.abstractThe past few decades have witnessed a revolution in recombinant DNA and nucleic acid sequencing technologies. Recently however, technologies capable of massively high-throughout, genome-wide data collection, combined with computational and statistical tools for data mining, integration and modeling have enabled the construction of predictive networks that capture cellular regulatory states, paving the way for ‘Systems biology’. Consequently, protein interactions can be captured in the context of a cellular interaction network and emergent ‘system’ properties arrived at, that may not have been possible by conventional biology. The ability to generate data from multiple, non-redundant experimental sources is one of the important facets to systems biology. Towards this end, we have established a novel platform called ‘spotted cell microarrays’ for conducting image-based genetic screens. We have subsequently used spotted cell microarrays for studying multidimensional phenotypes in yeast under different regulatory states. In particular, we studied the response to mating pheromone using a cell microarray comprised of the yeast non-essential deletion library and analyzed morphology changes to identify novel genes that were involved in mating. An important aspect of the mating response pathway is large-scale spatiotemporal changes to the proteome, an aspect of proteomics, still largely obscure. In our next study, we used an imaging screen and a computational approach to predict and validate the complement of proteins that polarize and change localization towards the mating projection tip. By adopting such hybrid approaches, we have been able to, not only study proteins involved in specific pathways, but also their behavior in a systemic context, leading to a broader comprehension of cell function. Lastly, we have performed a novel metabolic starvation-based screen using the GFP-tagged collection to study proteome dynamics in response to nutrient limitation and are currently in the process of rationalizing our observations through follow-up experiments. We believe this study to have implications in evolutionarily conserved cellular mechanisms such as protein turnover, quiescence and aging. Our technique has therefore been applied towards addressing several interesting aspects of yeast cellular physiology and behavior and is now being extended to mammalian cells.en
dc.format.mediumelectronicen
dc.language.isoengen
dc.rightsCopyright is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works.en
dc.subject.lcshGenomics--Databasesen
dc.subject.lcshGenomics--Data processingen
dc.subject.lcshDNA microarrays--Databasesen
dc.subject.lcshPhenotype--Data processingen
dc.subject.lcshSaccharomyces cerevisiae--Genetics--Data processingen
dc.subject.lcshPheromones--Data processingen
dc.subject.lcshProteomics--Data processingen
dc.subject.lcshProteins--Data processingen
dc.titleGenome-wide analyses of single cell phenotypes using cell microarraysen
dc.description.departmentCellular and Molecular Biology, Institute foren
dc.identifier.oclc244204512en
dc.type.genreThesisen
thesis.degree.departmentCellular and Molecular Biology, Institute foren
thesis.degree.disciplineCell and Molecular Biologyen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelDoctoralen
thesis.degree.nameDoctor of Philosophyen


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