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dc.contributor.authorLasher, Christopher Donalden_US
dc.date.accessioned2017-04-06T15:43:51Z
dc.date.available2017-04-06T15:43:51Z
dc.date.issued2011-09-12en_US
dc.identifier.otheretd-09212011-145623en_US
dc.identifier.urihttp://hdl.handle.net/10919/77217
dc.description.abstractHearkening to calls from life scientists for aid in interpreting rapidly-growing repositories of data, the fields of bioinformatics and computational systems biology continue to bear increasingly sophisticated methods capable of summarizing and distilling pertinent phenomena captured by high-throughput experiments. Techniques in analysis of genome-wide gene expression (e.g., microarray) data, for example, have moved beyond simply detecting individual genes perturbed in treatment-control experiments to reporting the collective perturbation of biologically-related collections of genes, or "processes". Recent expression analysis methods have focused on improving comprehensibility of results by reporting concise, non-redundant sets of processes by leveraging statistical modeling techniques such as Bayesian networks. Simultaneously, integrating gene expression measurements with gene interaction networks has led to computation of response networks--subgraphs of interaction networks in which genes exhibit strong collective perturbation or co-expression. Methods that integrate process annotations of genes with interaction networks identify high-level connections between biological processes, themselves. To identify context-specific changes in these inter-process connections, however, techniques beyond process-based expression analysis, which reports only perturbed processes and not their relationships, response networks, composed of interactions between genes rather than processes, and existing techniques in process connection detection, which do not incorporate specific biological context, proved necessary. We present two novel methods which take inspiration from the latest techniques in process-based gene expression analysis, computation of response networks, and computation of inter-process connections. We motivate the need for detecting inter-process connections by identifying a collection of processes exhibiting significant differences in collective expression in two liver tissue culture systems widely used in toxicological and pharmaceutical assays. Next, we identify perturbed connections between these processes via a novel method that integrates gene expression, interaction, and annotation data. Finally, we present another novel method that computes non-redundant sets of perturbed inter-process connections, and apply it to several additional liver-related data sets. These applications demonstrate the ability of our methods to capture and report biologically relevant high-level trends.
dc.language.isoen_USen_US
dc.publisherVirginia Techen_US
dc.rightsI 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.subjectmolecular interactionsen_US
dc.subjectgene expressionen_US
dc.subjectliveren_US
dc.subjectMarkov chain Monte Carloen_US
dc.subjectcomputational systems biologyen_US
dc.titleDiscovering contextual connections between biological processes using high-throughput dataen_US
dc.typeDissertationen_US
dc.contributor.departmentGenetics, Bioinformatics, and Computational Biologyen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineGenetics, Bioinformatics, and Computational Biologyen_US
dc.contributor.committeememberHelm, Richard Fredericken_US
dc.contributor.committeememberMarathe, Madhav V.en_US
dc.contributor.committeememberRamakrishnan, Narenen_US
dc.type.dcmitypeTexten_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-09212011-145623/en_US
dc.contributor.committeecochairRajagopalan, Padmavathyen_US
dc.contributor.committeecochairMurali, T. M.en_US
dc.date.sdate2011-09-21en_US
dc.date.rdate2016-10-07
dc.date.adate2011-10-21en_US


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