System identification of dynamic patterns of genome-wide gene expression

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dc.contributor.advisorMarkey, Mia Kathleenen
dc.contributor.advisorArapostathis, Ari, 1954-en
dc.contributor.committeeMemberVishwanath, Sriramen
dc.contributor.committeeMemberAziz, Adnanen
dc.contributor.committeeMemberWilke, Claus O.en
dc.creatorWang, Daifengen
dc.date.accessioned2012-01-31T17:57:25Zen
dc.date.accessioned2017-05-11T22:23:59Z
dc.date.available2012-01-31T17:57:25Zen
dc.date.available2017-05-11T22:23:59Z
dc.date.issued2011-12en
dc.date.submittedDecember 2011en
dc.date.updated2012-01-31T17:57:36Zen
dc.descriptiontexten
dc.description.abstractHigh-throughput methods systematically measure the internal state of the entire cell, but powerful computational tools are needed to infer dynamics from their raw data. Therefore, we have developed a new computational method, Eigen-genomic System Dynamic-pattern Analysis (ESDA), which uses systems theory to infer dynamic parameters from a time series of gene expression measurements. As many genes are measured at a modest number of time points, estimation of the system matrix is underdetermined and traditional approaches for estimating dynamic parameters are ineffective; thus, ESDA uses the principle of dimensionality reduction to overcome the data imbalance. We identify degradation dynamic patterns of a genomic system using ESDA. We also combine ESDA and Principal-oscillation-pattern (POP) analysis, which has been widely used in geosciences, to identify oscillation patterns. We demonstrate the first application of POP analysis to genome-wide time-series gene-expression data. Both simulation data and real-world data are used in this study to demonstrate the applicability of ESDA to genomic data. The biological interpretations of dynamic patterns are provided. We also show that ESDA not only compares favorably with previous experimental methods and existing computational methods, but that it also provides complementary information relative to other approaches.en
dc.description.departmentElectrical and Computer Engineeringen
dc.format.mimetypeapplication/pdfen
dc.identifier.slug2152/ETD-UT-2011-12-4532en
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2011-12-4532en
dc.language.isoengen
dc.subjectEigenvalues and eigenvectorsen
dc.subjectGenome-wide gene expressionen
dc.subjectSystems theoryen
dc.subjectSingular value decompositionen
dc.titleSystem identification of dynamic patterns of genome-wide gene expressionen
dc.type.genrethesisen

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