Time-Series Transcriptomic Analysis of a Systematically Perturbed Arabidopsis thaliana Liquid Culture System: A Systems Biology Perspective

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dc.contributor.advisorKlapa, Maria Ien_US
dc.contributor.authorDutta, Bhaskaren_US
dc.contributor.departmentChemical Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2007-06-22T05:36:50Z
dc.date.available2007-06-22T05:36:50Z
dc.date.issued2007-05-16
dc.description.abstractRevealing the gene regulation network has been one of the main objectives of biological research. Studying such a complex, multi-scale and multi-parametric problem requires educated fingerprinting of cellular physiology at different molecular levels under systematically designed perturbations. Conventional biology lacked the means for holistic analysis of biological systems. In the post-genomic era, advances in robotics and biology lead to the development of high-throughput molecular fingerprinting technologies. Transcriptional profiling analysis using DNA microarrays has been the most widely used among them. My Ph.D. thesis concerns the dynamic, transcriptional profiling analysis of a systematically perturbed plant system. Specifically, Arabidopsis thaliana liquid cultures were subjected to three different stresses, i.e. elevated CO2 stress, salt (NaCl) stress and sugar (trehalose) applied individually, while the latter two stresses were also applied in combination with the CO2 stress. The transcriptional profiling of these conditions involved carrying out 320 microarray hybridizations, generating thus a vast amount of transcriptomic data for Arabidopsis thaliana liquid culture system. To upgrade the dynamic information content in the data, I developed a statistical analysis strategy that enables at each time point of a time-series the identification of genes whose expression changes in statistically significant amount due to the applied stress. Additional algorithms allow for further exploration of the dynamic significance analysis results to extract biologically relevant conclusions. All algorithms have been incorporated in a software suite called MiTimeS, written in C++. MiTimeS can be applied accordingly to analyze time-series data from any other high-throughput molecular fingerprint. The experimental design combined with existing multivariate statistical analysis techniques and MiTimeS revealed a wealth of biologically relevant dynamic information that had been unobserved before. Due to the high-throughput nature of the analysis, the study enabled the simultaneous identification and correlation of parallel-occurring phenomena induced by the applied stress. Stress responses comparisons indicated that transcriptional response of the biological system to combined stresses is usually not the cumulative effect of individual responses. In addition to the significance of the study for the analysis of the particular plant system, the experimental and analytical strategies used provide a systems biology methodological framework for any biological system, in general.en_US
dc.format.extent3454113 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/6902
dc.language.isoen_US
dc.subject.pqcontrolledEngineering, Chemicalen_US
dc.subject.pqcontrolledBiology, Bioinformaticsen_US
dc.subject.pqcontrolledBiology, Biostatisticsen_US
dc.subject.pquncontrolledSystems Biologyen_US
dc.subject.pquncontrolledDNA microarrayen_US
dc.subject.pquncontrolledtime-series analysisen_US
dc.subject.pquncontrolledBioinformaticsen_US
dc.subject.pquncontrolledArabidopsis thalianaen_US
dc.subject.pquncontrolledGene expressionen_US
dc.titleTime-Series Transcriptomic Analysis of a Systematically Perturbed Arabidopsis thaliana Liquid Culture System: A Systems Biology Perspectiveen_US
dc.typeDissertationen_US

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