Chemical and Biomolecular Engineering Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2751
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Item AN INTEGRATIVE EXPERIMENTAL AND COMPUTATIONAL FRAMEWORK FOR THE GENOME-SCALE FLUX ANALYSIS OF ANTIBIOTIC RESISTANCE(2020) Mack, Sean; Dwyer, Daniel J; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The prevalence of antibiotic-resistant bacterial pathogens demands the development of novel therapeutic approaches. An attractive target is metabolism, where the impact of antibiotics and resistance remains poorly understood. Numerous omics-driven studies have identified a metabolic response due to antibiotic stress and suggested that metabolism adjusts to accommodate the genetic burden of resistance. Arising from these data is the hypothesis that context-specific modification of metabolism is a key component of antibiotic resistance and stress. Further exploration of the relationship between metabolism, antibiotic stress, and resistance is clearly needed. To elucidate metabolic signatures of antibiotic resistance, we analyzed the metabolic behaviors of wild-type and resistant strains of Escherichia coli through a combined transcriptomic and fluxomic analysis. Specifically, we compared wild-type E. coli to isogenic strains expressing integrated copies of tetRA and dhfr resistance genes, respectively under normal and antibiotic stress conditions. From comprehensive genome-scale (GS) flux predictions, we observed a resistance-associated metabolic phenotype as well as mechanism- and target-specific metabolic shifts. Furthermore, we identified a distinct metabolic response to antibiotic stress in both resistant strains. To improve our computational framework, we developed NetRed, NetRed-MFA, and NetFlow, each designed to reduce complexity of GS flux analysis. Through lossless reduction of genome-scale models (GSMs), NetRed generated a comprehensive minimal model for aerobic and anaerobic growth in E. coli and rapidly elucidated the mechanism driving artemisinin production in yeast. NetRed-MFA extended the original algorithm by incorporating full carbon mapping to generate reduced models for 13C metabolic flux analysis. NetFlow leveraged GS carbon mapping to isolate the major carbon flows through a core network and GSM; from the GSM subnetwork, we identified a mechanistic relationship between a triple-knockout and increased lycopene production in E. coli. Our resistance work represents the first application of quantitative flux analysis to study the metabolism of resistant bacteria and should provide significant insight into the role of metabolic adaptation in antibiotic resistance. The developed tools each dramatically improve the interpretation of GS flux predictions and the mechanistic understanding of metabolic perturbations. Taken together, this dissertation describes a comprehensive framework for the prediction, comparison, and interpretation of altered metabolic states.Item EFFECT OF LIPID-PROTEIN INTERACTIONS ON THE CONDUCTANCE OF THE TRANSMEMBRANE PROTEIN ALPHA HEMOLYSIN USING MOLECULAR DYNAMICS SIMULATIONS(2019) Tammareddy, Tejaswi; Klauda, Jeffery; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Alpha-hemolysin (aHL) is a transmembrane ion-conducting channel which finds application in single molecule sensing using nanopore technology. Biomolecules are allowed to pass through the pore of the protein and, as a result, there is a change in the ion current, which is monitored to quantify single-molecule sensing. However, it has been established that the change in current is also affected by the lipid membrane in which the protein is present. It is also known that cholesterol has a concentration-dependent reduction in the current through the pore, experimentally. The understanding of current reduction at a single-molecule level and theoretical models replicating these conditions are lacking. In the current thesis, molecular dynamics simulations are performed on aHL inserted into a 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-choline (POPC) lipid bilayer with varying concentrations of cholesterol to investigate the effect on ionic current. Effect of lipid interactions, especially of cholesterol, on the protein structure and hence functioning of the ion channel is investigatedItem Investigating the metabolic landscape alterations in poplar cells induced by carbon and nitrogen deficiency via improved 13C metabolic flux analysis methodology(2015) Zhang, Xiaofeng; Sriram, Ganesh; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Plants are considered biological factories with their ability of converting solar energy into chemical energy in the form of various commercially valuable products, such as food, biofuel and pharmaceuticals. The yields of these products are directly influenced by the level of nitrogen nutrient supply. However, both biological and industrial nitrogen fixation are energetically expensive and thus managing the nitrogen cycle has been identified as one of the 14 grand challenges by the National Academy of Engineering (NAE). Therefore it is desirable to investigate how plants themselves adapt to nitrogen deficient environment and improve their nitrogen use efficiency (NUE). A powerful tool to study metabolism is isotope-assisted metabolic flux analysis (isotopic MFA), which quantifies intracellular chemical reaction rates (fluxes) via isotopic labeling experiments (ILEs) and subsequent mathematical modeling. In ILEs the labeling patterns of the metabolites can be measured at either isotopic steady state or isotopic instationary state. Between these two methods, collecting data during isotopic instationary state saves experimental time, but is computationally more challenging due to that instationary MFA involves solving ordinary differential equations (ODEs). In this study, we firstly developed an approach that combined the concept of "originomer" with an analytical based solution method to improve computational efficiency of instationary MFA. Simulation results showed that this approach reduced computational time by 23-fold for certain realistic metabolic network. Secondly, we managed to solve an intrinsic problem that affect steady state MFA in fed-batch cell culture environment - the influence of unlabeled biomass that are present before applying isotopic tracers in an ILE. We proposed a full "reflux" metabolic network model that significantly improved the accuracy of evaluated fluxes when compared to the models without "reflux". Finally, we investigated the ability of adapting nutrient deficiencies and the NUE-improving mechanisms in suspension cells of poplar, a woody perennial tree capable of efficiently managing its nitrogen reserves. Through (i) steady-state 13C MFA and (ii) transcripomic profiling via microarray on poplar cells growing under different carbon (C) and nitrogen (N) supply levels, we found a plastidic localization of oxidative pentose phosphate pathway (oxPPP), as well as a lower oxPPP flux under low nitrogen supply. Gene expression data also points to possible NUE improving mechanisms employed by poplar cells. We hope this study will shed light on potential metabolic engineering directions to improve NUE in plants.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 High-Throughput Time Series Metabolomic Analysis of a Systematically Perturbed Plant System(2007-04-27) Kanani, Harin H; Klapa, Maria I; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In the post-genomic era, availability of high-throughput profiling techniques enabled the measurement of entire cellular molecular fingerprints. Major characteristics of the high-throughput revolution were that (a) studying biological problems did not have to rely on prior hypotheses, while (b) parallel occurring phenomena, previously assumed disconnected, could now be simultaneously observed. Metabolomics is the newest of the "omics" techniques. It enables the quantification of hundreds of free metabolite pools, providing a metabolic fingerprint. Considering the importance of cellular metabolism, which is the net effect of changes at the genomic, transcriptomic and proteomic levels and of the cell with its environment, the metabolomic profile, is a fundamental determinant of cellular physiology. Obtaining accurate and complete metabolomic profiles is thus of great importance. However, being recent technology, metabolomics is currently at its standardization phase. As part of my PhD thesis research, I focused on addressing several current challenges in metabolomics technology development. Specifically a novel data correction, validation and normalization strategy for gas chromatography-mass spectrometry (GC-MS) metabolomic profiling analysis was developed, which dramatically increased the accuracy and reliability of GC-MS metabolomic profiles. The optimized metabolomics protocol was applied to study the short-term dynamic response of systematically perturbed Arabidopsis thaliana liquid culture system to study regulation of its primary metabolism. The biological system was studied under conditions of elevated CO2 stress, salt (NaCl) stress, sugar (trehalose) signal, and hormone (ethylene) signal, applied individually; the latter three stresses also applied in combination with the CO2 stress. Analysis of the obtained results required the appropriate application of multivariate statistical analysis techniques, which are developed mainly in transcriptomic analysis, into metabolomics analysis for the first time. The acquired results identified important new regulatory information about the biological systems resulting in new targets for metabolic engineering of plants. The large number of dynamic perturbation allowed re-construction of metabolic networks to identify possible novel metabolic pathways based on correlations between metabolic profiles. In addition, it demonstrates the advantages of dynamic, multiple-stress "omic" analysis for the elucidation of plant systems function. In this sense, it contributes in further advancing the computational and experimental metabolic engineering and systems biology toolbox.