A. James Clark School of Engineering
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The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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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 Analysis and refinement of pulse rate variability(2019) Li, Yiqi; Wu, Min; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Heart rate variability (HRV), calculated from the cardiac intervals of electrocardiogram (ECG), is a promising marker of the cardiovascular system status and fitness. However, ECG signal is not always available and photoplethysmogram (PPG) is easier to obtain, and more widely used in clinical is running HRV analysis on pulse-to-pulse intervals of PPG signal, which is usually referred to as pulse rate variability (PRV). Thus, whether PRV can be used as a substitution of HRV is of substantial interest to researchers. In this thesis, two issues about PRV are discussed. The first issue is the selection of characteristic point, which determines the length and location of the pulse-to-pulse interval and will affect the agreement between PRV and HRV. Six characteristic points of PPG pulse are extracted and the agreement between HRV and corresponding PRV is calculated and compared, in two situations, subjects with cardiovascular diseases (CVD) and subjects without cardiovascular diseases (non-CVD). The result indicates that pulse peak is most suitable for CVD subjects, and 50% max amplitude point and 75% max amplitude point on pulse slope are most suitable for non-CVD subjects. The second issue studied in this thesis is the PRV refinement using arterial blood pressure (ABP) information. The relationship between systolic blood pressure extracted from ABP signal and pulse transit time (PTT) is modeled using linear kernel support vector regression (SVR) and RBF kernel SVR, respectively. Estimated PTT is used to adjust the location of PPG pulse-to-pulse intervals. PRV after adjustment is calculated, and its agreement to HRV is compared with the original PRV. For CVD subjects, the improvement to the agreement is limited, and only the agreement for variables representing long-term variability is improved. For non-CVD subjects, there is a relatively large improvement for approximately all variables after refinement and linear kernel outperforms RBF kernel in this situation.Item Optics And Computer Vision For Biomedical Applications(2018) Wang, Bohan; Chen, Yu; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Bioengineering is at the cross sections of biology, clinical technology, electrical engineering, computer science and many other domains. The smooth translation of domain technologies to clinics is not just about accuracy and practicality of the technology. It also has to take into account the accessibility (cost and portability), the patients’ comfort and the ease to adapt into the workflow of medical professionals. The dissertation will explore three projects, (1) portable and low-cost near infrared florescence imaging system on mobile phone platform, (2) computer aided diagnosis software for diagnosing chronical kidney disease based on optical coherence tomography (OCT) images and (3) the tracking and localization of hand-held medical imaging probe. These projects aim to translate and adapt modern computation hardware, data analysis models and computer vision technologies to solve and refine clinical diagnosis applications. The dissertation will discuss how the translation, tradeoffs and refinement of those technologies can bring a positive impact on the accuracy, ease of conduct, accessibility and patients’ comfort to the clinical applications.Item SYSTEMS-LEVEL MODELING AND VALIDATION OF CARDIOVASCULAR SYSTEM RESPONSES TO FLUID AND VASOPRESSOR INFUSION FOR AUTOMATED CRITICAL CARE SYSTEMS(2017) Bighamian, Ramin; Hahn, Jin-Oh; Reisner, Andrew T; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Effective treatment of critically ill patients requires adequate administration of drugs to resuscitate and stabilize the patient by maintaining the volume of blood against bleeding and preserving the blood circulation to the body tissues. In today’s clinical practice, drug dose is adjusted by human clinicians. Therefore, treatment is often subjective and ad-hoc depending on the style and experience of the clinician. Thus, in theory, it is anticipated that well-designed automated critical care systems can help clinicians make superior adjustments to drug doses while they are always vigilant and never distracted by other obligations. Yet, automated critical care systems developed by researchers are ad-hoc, because they determine the control law, i.e., drug infusion rate, using input-output observations rather than the insights on the patient’s physiological states gained from rigorous data-based analysis of mathematical models. Thus, it is worth developing model-based automated systems relating the fluid and vasopressor dose input to the underlying physiological states. This necessitates dose-response mathematical models capable of reproducing realistic physiological and dose-mediated states with reasonable computational load. However, most of existing models are too simplistic to reflect physiological reality, while others are too complicated with thousands of parameters to tune. To address these challenges, we believe that a hybrid physiologic-phenomenological modeling paradigm is effective in developing mathematical models for automated systems: low-order phenomenological models with adaptive personalization capability are suited to develop control algorithms, while physiological models can provide high-fidelity patterns with physiological transparency suited to interpret the underlying physiological states. In this study, hybrid physiologic-phenomenological models of blood volume and cardiovascular responses to fluid and vasopressor infusion are successfully developed and validated using experimental data. It is shown that the models can adequately reproduce the underlying physiological states and endpoints to fluid and vasopressor infusion. The main contributions of this research are lined in the following three folds. First, the models are robust against inter-individual variability, in which they can be adapted to each patient with a small number of tunable parameters. Second, they are physiologically transparent where the underlying physiological states not measured in the standard clinical setting can be interpreted and streamlined during an intervention. And eventually the interpreted underlying states can be employed as direct endpoints to monitor the patient and guide the treatment in a closed-loop or decision-support platform.Item Using Many-Core Computing to Speed Up De Novo Transcriptome Assembly(2016) O'Brien, Sean; Vishkin, Uzi; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The central dogma of molecular biology implies that DNA holds the blueprint which determines an organism's structure and functioning. However, this blueprint can be read in different ways to accommodate various needs, depending on a cell's location in the body, its environment, or other external factors. This is accomplished by first transcribing DNA into messenger RNA (mRNA), and then translating mRNA into proteins. The cell regulates how much each gene is transcribed into mRNA, and even which parts of each gene is transcribed. A single gene may be transcribed in different ways by splicing out different parts of the sequence. Thus, one gene may be transcribed into many different mRNA sequences, and eventually into different proteins. The set of mRNA sequences found in a cell is known as its transcriptome, and it differs between tissues and with time. The transcriptome gives a biologist a snapshot of the cell's state, and can help them track the progression of disease, etc. Some modern methods of transcriptome sequencing give only short reads of the mRNA, up to 100 nucleotides. In order to reconstruct the mRNA sequences, one must use an assembly algorithm to stitch these short reads back into full length transcripts. De novo transcriptome assemblers are an important family of transcriptome assemblers. Such assemblers reconstruct the transcriptome without using a reference genome to align to and are, therefore, computationally intensive. We present here a de novo transcriptome assembler designed for a parallel computer architecture, the XMT architecture. With this assembler we produce speedups over existing de novo transcriptome assemblers without sacrificing performance on traditional quality metrics.Item 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 CONTEXTUALIZATION OF THE E. COLI LSR SYSTEM: RELATIVE ORTHOLOGY, RELATIVE QS ACTIVITY, AND EMERGENT BEHAVIOR(2015) Quan, David Nathan; Bentley, William E.; Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Within bacterial consortia there exist innumerable combinatorial circumstances, some of which may tip the scale toward pathogenicity, some of which may favor asymptomatic phenotypes. Indeed, the lines and intersections between commensal, pathogenic, and opportunistic bacteria are not always clean. As a foothold to mediate pathogenicity arising from consortia, many have puzzled at communication between bacteria. Primary among such considerations is quorum sensing (QS). Analogous to autocrine signaling in multicellular organisms, QS is a self-signaling process involving small molecules. Generally, QS activation is believed to have pleiotropic effects, and has been associated with numerous pathogenic phenotypes. The research herein focuses on autoinducer-2 (AI-2) based QS signaling transduced through the Lsr system. Produced by over 80 species of bacteria, AI-2 is believed to be an interspecies signaling molecule. Outside of the marine bacteria genera Vibrio and Marinomonas, the only known AI-2 based QS transduction pathway is the Lsr system. We sought to deepen the characterization of the Lsr system in contexts outside of the batch cultures in which it was originally defined. First, we interrogated E. coli K-12 W3110 Lsr system orthologs relative to the same strain's lac system. Both systems are induced by the molecule which they import and catabolize. We searched for homologs by focusing on the gene order along a genome, as gene arrangement can bear signaling consequences for autoregulatory circuits. We found that the Lsr system signal was phylogenetically dispersed if not particularly deep, especially outside of Enterobacteriales and Pasteurellaceaes, indicating that the system has generally been conferred horizontally. This contrasts with the lac system, whose signal is strong but limited to a select group of highly related enterobacteria. We then modeled the Lsr system with ODEs, revealing bimodality in silico, bolstering preliminary experimental evidence. This bifurcated expression was seen to depend upon nongenetic heterogeneity, which we modeled as a variation of a single compound parameter, basal, representing the basal rate of AI-2 flux into the cell through a low flux pathway. Moreover, in our finite difference-agent based models, bimodal expression could not arise from spatial stochasticity alone. This lies in contrast with the canonical LuxIR QS system, which employs an intercellular positive feedback loop to activate the entire population. We examined the consequences of this contrast, by modeling both systems under conditions of colony growth using finite difference-agent based methods. We additionally investigated the confluence of Lsr signaling with chemotactic sensitivity to AI-2, which has been demonstrated in E. coli. Finally, the consequences of bimodality in interspecies interactions were assessed by posing two populations containing different Lsr systems against each other. While few natural consortia consist of only two interacting bacteria, these studies indicate that AI-2 based Lsr signaling may mediate a multitude of transitional intraspecies and interspecies bacterial dynamics, the specifics of which will vary with the context and the homologs involved.Item Nucleic Acid Extraction and Detection Across Two-Dimensional Tissue Samples(2010) Armani, Michael Daniel; Shapiro, Benjamin; Smela, Elisabeth; Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Visualizing genetic changes throughout tissues can explain basic biological functions and molecular pathways in disease. However, over 90% of mammalian messenger RNA (mRNA) is in low abundance (<15 copies per cell) making them hard to see with existing techniques, such as in-situ hybridization (ISH). In the example of diagnosing cancer, a disease caused by genetic mutations, only a few cancer-associated mRNAs can be visualized in the clinic due to the poor sensitivity of ISH. To improve the detection of low-abundance mRNA, many researchers combine the cells across a tissue sample by taking a scrape. Mixing cells provides only one data point and masks the inherent heterogeneity of tissues. To address these challenges, we invented a sensitive method for mapping nucleic acids across tissues called 2D-PCR. 2D-PCR transfers a tissue section into an array of wells, confining and separating the tissue into subregions. Chemical steps are then used to free nucleic acids from the tissues subregions. If the freed genetic material is mRNA, a purification step is also performed. One or more nucleic acids are then amplified using PCR and detected across the tissue to produce a map. As an initial proof of concept, a DNA map was made from a frozen tissue section using 2D-PCR at the resolution of 1.6 mm per well. The technique was improved to perform the more challenging task of mapping three mRNA molecules from a frozen tissue section. Because the majority of clinical tissues are stored using formalin fixation and not freezing, 2D-PCR was improved once more to detect up to 24 mRNAs from formalin-fixed tissue microarrays. This last approach was used to validate genetic profiles in human normal and tumor prostate samples faster than with existing techniques. In conclusion, 2D-PCR is a robust method for detecting genetic changes across tissues or from many tissue samples. 2D-PCR can be used today for studying differences in nucleic acids between tumor and normal specimens or differences in subregions of the brain.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.