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dc.contributor.authorPevzner, Samuel Jen_US
dc.date.accessioned2015-04-27T14:19:18Z
dc.date.available2015-04-27T14:19:18Z
dc.date.issued2013
dc.date.submitted2013
dc.identifier.other
dc.identifier.urihttps://hdl.handle.net/2144/11023
dc.descriptionThesis (Ph.D.)--Boston Universityen_US
dc.description.abstractHow cellular elements coordinate their function is a fundamental question in biology. A crucial step towards understanding cellular systems is the mapping of physical interactions between protein, DNA, RNA and other macromolecules or metabolites. Genome-scale technologies have yielded protein-protein interaction networks for several eukaryotic species and have provided insight into biological processes and evolution, but many of the currently available networks are biased. Towards a true human protein-protein interaction network, we examined literature-based aggregations of lowthroughput experiments, high-throughput experimental networks validated using different strategies, and predicted interaction networks to infer how the underlying interactome may differ from current maps. Using systematically mapped interactome networks, which appear to be the least biased, we explored the functional organization of Arabidopsis thaliana and characterize the asymmetric divergence of duplicated paralogous proteins through their interaction profiles. To further dissect the relationship between interactions and function enforced by evolution, we investigated a first-of-its-kind systematic crossspecies human-yeast hybrid interactome network. Although the cross-species network is topologically similar to conventional intra-species networks, we found signatures of dynamic changes in interaction propensities due to countervailing evolutionary forces. Collectively, these analyses of human, plant and yeast interactome networks bridge separate experiments to characterize bias, function and evolution across eukaryotic kingdoms.en_US
dc.language.isoen_US
dc.publisherBoston Universityen_US
dc.titleProtein interactions across and between eukaryotic kingdoms: networks, inference strategies, integration of functional data and evolutionary dynamicsen_US
dc.typeThesis/Dissertationen_US
etd.degree.nameDoctor of Philosophyen_US
etd.degree.leveldoctoralen_US
etd.degree.disciplineBiomedical Engineeringen_US
etd.degree.grantorBoston Universityen_US


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