Chemical and Biomolecular Engineering Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2751
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Item Mapping metabolic fluxes in plant cells to understand carbon-nitrogen interactions and nitrogen storage and cycling(2012) Nargund, Shilpa; Sriram, Ganesh; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Plants provide commodities like food, fiber, fuel and chemicals. Understanding plant metabolism will help find genetic engineering targets that enhance production of these commodities. Interactions between the macronutrients - carbon (C) and nitrogen (N) determine growth and developmental functions in plants (Nunes-Nesi, Fernie, and Stitt 2010; Sakakibara, Takei, and Hirose 2006) and are regulated by complex mechanisms that need systems-level analyses. Metabolic fluxes, the rates of C flow in metabolic pathways, provide a system-wide view of metabolism and are quantified by steady state metabolic flux analysis (MFA) wherein isotopic tracers (13C, 15N) are fed to the cells and the resulting labeling patterns of biomass components are used to fit the fluxes. In this study we i) statistically designed isotope labeling experiments (ILEs) in silico to enhance accuracy of flux estimates through the pentose phosphate pathway (PPP) ii) conducted MFA on heterotrophic cell suspensions of Arabidopsis thaliana (Arabidopsis), a model plant, to investigate regulatory role of light in cell metabolism and iii) conducted MFA on cell suspensions of poplar (Populus tremula × Populus alba; clone N 717-B4), a potential biofuel crop, to understand C-N interactions. In silico label design studies determined that accuracy of flux estimates in the PPP improves by ILEs with 1,2-13C glucose and measuring labeling patterns of sugars, especially ribose. Metabolic fluxes, estimated by the designed ILEs on Arabidopsis cells, under continuous light or dark, showed negligible changes between treatments indicating that light does not regulate central carbon metabolism in heterotrophic Arabidopsis cells. The designed ILEs improved confidences of non-oxidative PPP flux estimates by 40-80% from previous studies (Masakapalli et al. 2009a). ILEs on poplar cell suspensions, grown in batch cultures, displayed unexpected back-mixing between unlabeled seed biomass and newly synthesized labeled biomass. Novel metabolic network models were developed that successfully account for observed back-mixing. ILEs on poplar cells, subjected to different C-N supply treatments to understand C-N interactions showed significant differences in phenylalanine labeling which may implicate role of flavonoid biosynthesis pathway in C-N interactions. Design of ILEs and subsequent improvement in flux estimates and the improvements in modeling metabolic networks are the novel contributions of this work.Item Time-Series Transcriptomic Analysis of a Systematically Perturbed Arabidopsis thaliana Liquid Culture System: A Systems Biology Perspective(2007-05-16) Dutta, Bhaskar; Klapa, Maria I; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Revealing 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.Item TIME SERIES METABOLIC PROFILING ANALYSIS OF THE SHORT TERM Arabidopsis thaliana RESPONSE TO ELEVATED CO2 USING GAS CHROMATOGRAPHY MASS SPECTROMETRY.(2004-08-30) Kanani, Harin Haridas; Klapa, Maria I; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Metabolic profiling has emerged as a high throughput technique for the quantitative analysis of the cellular physiological state at the metabolic level. It allows for the simultaneous relative quantification of hundreds of low molecular weight intra cellular metabolites. In this analysis, the polar metabolic profiles of A. thaliana liquid cultures (grown for 12 days, under light and 23°C) throughout 1-day treatment with 1% CO2, were measured using gas chromatography-mass spectrometry. Despite the advantages of time series analysis, this is the first plant metabolic profiling study of this type reported in the literature. The time series metabolic profiles were analyzed using multivariate statistical techniques. Data analysis revealed repression of photorespiration, repression of nitrogen assimilation and increase in structural carbohydrates. It is for the first time that the latter phenomenon is observed as a result of elevated CO2 in the plant environment.Item TIME SERIES TRANSCRIPTIONAL PROFILING ANALYSIS OF THE Arabidopsis thaliana USING FULL GENOME DNA MICROARRAY AND METABOLIC INFORMATION(2004-08-26) Dutta, Bhaskar; Klapa, Maria I; Quackenbush, John; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)With the advent of the DNA microarray technology, it became possible to study the expression of entire cellular genomes. Tanscriptional profiling alone can not provide a comprehensive picture of the cellular physiological state and it should be complemented by other cellular fingerprints. Transcriptional profiling combined with metabolic information of a systematically perturbed system can unravel the relationship between gene and metabolic regulation. In this context the transcriptional response of Arabidopsis thaliana liquid cultures (grown for 12 days under light and 23 sup oC) to 1-day treatment with 1% CO sub 2 was measured by full genome cDNA microarrays. The Time series gene expression profiles were analyzed in the context of the known Arabidopsis thaliana physiology using multivariate statistics. Data analysis revealed an increase in the rate of CO sub 2 fixation, biomass production and cell wall growth. The breadth of the information obtained from a single experiment validated the significance of the high throughput transcriptional profiling.