Eukaryotic cells are divided into subcellular organelles that generate appropriate molecular environments for the functions they harbour. As such, subcellular localization is a key characteristic that provides valuable clues regarding protein function and, when studied globally, a better understanding of cellular processes. The organelles of the secretory pathway are responsible for the processing of all proteins destined for secretion, the plasma membrane as well as their own resident proteins. This group of organelles is difficult to study experimentally because they are difficult to purify to homogeneity. / To facilitate the investigation of the endoplasmic reticulum (ER) and more generally, the secretory pathway, we have created Hera, a publicly accessible protein localization database. Originally designed to house characteristics of ER proteins, it currently contains tens of thousands of proteins from different organisms and subcellular compartments. Hera was originally used to investigate features of ER proteins, providing insight into the extent of usage of various localization mechanisms, including both well-studied but also non-classical and novel mechanisms. / Hera was subsequently used to create Bayesian network type localization predictors. By considering the combinatorial presence of motifs, domains, targeting signals and using in some cases, protein interaction information, our predictors achieve high accuracy and coverage. When our predictions are compared with localization annotations from high-throughput studies in both human and yeast, we find that disagreements mainly involve proteins in the secretory pathway. Our predictors can be used to independently validate these large-scale studies. We further refined the localization prediction of the whole yeast proteome by distinguishing proteins localized to the lumen or membrane of various organelles from cytosolic proteins peripherally associated with these organelles. / Hera was also used to investigate efficient and informative approaches to interrogate interaction networks in order to gain insight into the relationship between proteins/genes of interest. By combining interaction and refined localization information, we constructed localizome-interactome networks of whole organelles. Such models provide insight into global organellar characteristics and inter-organellar mechanisms of communication. / The research presented in this thesis demonstrates that the integration, in an appropriate framework such as Bayesian networks, of widely available information such as localization and interaction data allows to gain deep insights into cellular processes.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.102170 |
| Date | January 2005 |
| Creators | Scott, Michelle. |
| Publisher | McGill University |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Format | application/pdf |
| Coverage | Doctor of Philosophy (Department of Biochemistry.) |
| Rights | © Michelle Scott, 2005 |
| Relation | alephsysno: 002326173, proquestno: AAINR25251, Theses scanned by UMI/ProQuest. |
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