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Reconstructing Signaling Pathways Using Regular-Language Constrained Paths

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dc.contributor.authorWagner, Mitchell Jamesen
dc.contributor.committeechairMurali, T. M.en
dc.contributor.committeememberHeath, Lenwood S.en
dc.contributor.committeememberPrakash, B. Adityaen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2018-09-19T08:00:43Zen
dc.date.available2018-09-19T08:00:43Zen
dc.date.issued2018-09-18en
dc.description.abstractSignaling pathways are widely studied in systems biology. Several databases catalog our knowledge of these pathways, including the proteins and interactions that comprise them. However, high-quality curation of this information is slow and painstaking. As a result, many interactions still lack annotation concerning the pathways they participate in. A natural question that arises is whether or not it is possible to automatically leverage existing annotations to identify new interactions for inclusion in a given pathway. Here, we present RegLinker, an algorithm that achieves this purpose by computing multiple short paths from pathway receptors to transcription factors (TFs) within a background interaction network. The key idea underlying RegLinker is the use of regular-language constraints to control the number of non-pathway edges present in the computed paths. We systematically evaluate RegLinker and alternative approaches against a comprehensive set of 15 signaling pathways and demonstrate that RegLinker exhibits superior recovery of withheld pathway proteins and interactions. These results show the promise of our approach for prioritizing candidates for experimental study and the broader potential of automated analysis to attenuate difficulties of traditional manual inquiry.en
dc.description.abstractgeneralCells in the human body are constantly receiving signals that inform their response to a variety of conditions. These signals serve as cues to a cell, allowing it to make informed decisions that impact cellular processes such as movement, growth, and death. Cells employ proteins and the interactions between them to achieve these capabilities. Signals manifest as molecules that interact with proteins bound to membrane of a cell. When this happens, a cascade of interactions between the proteins inside the cell will be set off. Ultimately, this cascade activate or inhibit the cell’s production of new proteins, constituting a response to the signal received. The proteins and interactions involved in such a cascade together form what is known as a signaling pathway. Experiments have uncovered the interactions that are present in many signaling pathways, and researchers have carefully cataloged this information in publicly available databases. However, high-quality curation is slow and painstaking, and many known interactions have not been annotated as belonging to any pathway. A natural question that arises is whether or not it is possible to leverage existing annotations to automatically determine which new interactions to include in a given pathway. In this thesis, we present an efficient algorithm, RegLinker, for this purpose. We evaluate this method and alternative approaches on a comprehensive set of 15 signaling pathways and demonstrate that RegLinker is better at recovering interactions withheld from these pathways. In particular, we show RegLinker’s superior ability to identify interactions that utilize proteins that were not previously considered part of a pathway. These results underscore the promise of our approach for prioritizing candidates for experimental study and the broader potential of automated analysis to attenuate difficulties of traditional manual inquiry.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:17143en
dc.identifier.urihttp://hdl.handle.net/10919/85044en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectRegular Languagesen
dc.subjectShortest Pathsen
dc.subjectSignaling Networksen
dc.titleReconstructing Signaling Pathways Using Regular-Language Constrained Pathsen
dc.typeThesisen
thesis.degree.disciplineComputer Science and Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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