Automatic Reconstruction of the Building Blocks of Molecular Interaction Networks
|dc.contributor.author||Rivera, Corban G.||en_US|
|dc.description.abstract||High-throughput whole-genome biological assays
are highly intricate and difficult to interpret.
The molecular interaction networks generated from evaluation of those
experiments suggest that
cellular functions are carried out by modules of interacting
molecules. Reverse-engineering the modular structure of cellular
interaction networks has the promise of significantly easing their
We hypothesize that:
To address the first problem, we propose an approach that computes the most-perturbed subgraph of a curated pathway of molecular interactions in a disease state. Our method is based on a novel score for pathway perturbation that incorporates both differential gene expression and the interaction structure of the pathway. We apply our method to a compendium of cancer types. We show that the significance of the most perturbed sub-pathway is frequently larger than that of the entire pathway. We identify an association that suggests that IL-2 infusion may have a similar therapeutic effect in bladder cancer as it does in melanoma.
We propose two models to address the second problem. First, we formulate a Boolean model for constructing network legos from a set of active networks. We reduce the problem of computing network legos to that of constructing closed biclusters in a binary matrix. Applying this method to a compendium of 13 stresses on human cells, we automatically detect that about four to six hours after treatment with chemicals cause endoplasmic reticulum stress, fibroblasts shut down the cell cycle far more aggressively than fibroblasts or HeLa cells do in response to other treatments.
Our second model represents each active network as an additive combination of network legos. We formulate the problem as one of computing network legos that can be used to recover active networks in an optimal manner. We use existing methods for non-negative matrix approximation to solve this problem. We apply our method to a human cancer dataset including 190 samples from 18 cancers. We identify a network lego that associates integrins and matrix metalloproteinases in ovarian adenoma and other cancers and a network lego including the retinoblastoma pathway associated with multiple leukemias.
|dc.rights||I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.||en_US|
|dc.subject||Molecular Interaction Networks||en_US|
|dc.title||Automatic Reconstruction of the Building Blocks of Molecular Interaction Networks||en_US|
|thesis.degree.grantor||Virginia Polytechnic Institute and State University||en_US|
|dc.contributor.committeechair||Murali, T. M.||en_US|
|dc.contributor.committeemember||Tyler, Brett M.||en_US|
|dc.contributor.committeemember||Helm, Richard Frederick||en_US|
|dc.contributor.committeemember||Vullikanti, Anil Kumar S.||en_US|
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