Biological pathways usually become interrupted during disease states. Complete cognizance of the proteins and genes, which participate in pathways, will allow researchers to learn more about human disease and identify molecular targets for therapeutic intervention. However, a significant number of pathways remain unidentified. Moreover, current knowledge about existing pathways is incomplete at a time when researchers are beginning to implement the "pathway approach" to engineer better pharmaceutical drugs and prevention strategies to treat diseases. Therefore, computational methods have been applied to probabilistic protein-protein interaction (PPI) networks to reveal candidate proteins, which may be members of partially-known protein complexes. One of the computational methods was also extended to pathways. The methods provide plausible solutions for protein complexes, which are usually highly connected sub-graphs. Unlike protein complexes, biological pathways form directed sub-graphs where not all proteins are connected to each other. Subsequently, a crucial challenge emerges which is to develop new approaches for pathways that leverage the possible locations for insertion of the candidate proteins in an incomplete pathway. We call this challenge the pathway completion problem, and we propose to address pathway completion through utilizing computational methods and network motifs. In this work, we develop the Fit and Complete algorithm, which is a framework for conducting searches on probabilistic PPI networks, to extract potential protein candidate members and their locations in a given incomplete pathway. Taking advantage of network motifs to uncover both membership and location information for a candidate protein in an incomplete pathway will render the laboratory experimental verification step more efficient.
|Advisers||Michael Hahsler; Margaret Dunham|
|School||SOUTHERN METHODIST UNIVERSITY|
|Subjects||Bioinformatics; Computer science|
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