ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: Biology
- Creators: Anderson, Karen S
- Creators: Krajmalnik-Brown, Rosa
Description
Photosynthesis converts sunlight to biomass at a global scale. Among the photosynthetic organisms, cyanobacteria provide an excellent model to study how photosynthesis can become a practical platform of large-scale biotechnology. One novel approach involves metabolically engineering the cyanobacterium Synechocystis sp. PCC 6803 to excrete laurate, which is harvested directly.
This work begins by defining a working window of light intensity (LI). Wild-type and laurate-excreting Synechocystis required an LI of at least 5 µE/m2-s to sustain themselves, but are photo-inhibited by LI of 346 to 598 µE/m2-s.
Fixing electrons into valuable organic products, e.g., biomass and excreted laurate, is critical to success. Wild-type Synechocystis channeled 75% to 84% of its fixed electrons to biomass; laurate-excreting Synechocystis fixed 64 to 69% as biomass and 6.6% to 10% as laurate. This means that 16 to 30% of the electrons were diverted to non-valuable soluble products, and the trend was accentuated with higher LI.
How the Ci concentration depended on the pH and the nitrogen source was quantified by the proton condition and experimentally validated. Nitrate increased, ammonium decreased, but ammonium nitrate stabilized alkalinity and Ci. This finding provides a mechanistically sound tool to manage Ci and pH independently.
Independent evaluation pH and Ci on the growth kinetics of Synechocystis showed that pH 8.5 supported the fastest maximum specific growth rate (µmax): 2.4/day and 1.7/day, respectively, for the wild type and modified strains with LI of 202 µE/m2-s. Half-maximum-rate concentrations (KCi) were less than 0.1 mM, meaning that Synechocystis should attain its µmax with a modest Ci concentration (≥1.0 mM).
Biomass grown with day-night cycles had a night endogenous decay rate of 0.05-1.0/day, with decay being faster with higher LI and the beginning of dark periods. Supplying light at a fraction of daylight reduced dark decay rate and improved overall biomass productivity.
This dissertation systematically evaluates and synthesizes fundamental growth factors of cyanobacteria: light, inorganic carbon (Ci), and pH. LI remains the most critical growth condition to promote biomass productivity and desired forms of biomass, while Ci and pH now can be managed to support optimal productivity.
This work begins by defining a working window of light intensity (LI). Wild-type and laurate-excreting Synechocystis required an LI of at least 5 µE/m2-s to sustain themselves, but are photo-inhibited by LI of 346 to 598 µE/m2-s.
Fixing electrons into valuable organic products, e.g., biomass and excreted laurate, is critical to success. Wild-type Synechocystis channeled 75% to 84% of its fixed electrons to biomass; laurate-excreting Synechocystis fixed 64 to 69% as biomass and 6.6% to 10% as laurate. This means that 16 to 30% of the electrons were diverted to non-valuable soluble products, and the trend was accentuated with higher LI.
How the Ci concentration depended on the pH and the nitrogen source was quantified by the proton condition and experimentally validated. Nitrate increased, ammonium decreased, but ammonium nitrate stabilized alkalinity and Ci. This finding provides a mechanistically sound tool to manage Ci and pH independently.
Independent evaluation pH and Ci on the growth kinetics of Synechocystis showed that pH 8.5 supported the fastest maximum specific growth rate (µmax): 2.4/day and 1.7/day, respectively, for the wild type and modified strains with LI of 202 µE/m2-s. Half-maximum-rate concentrations (KCi) were less than 0.1 mM, meaning that Synechocystis should attain its µmax with a modest Ci concentration (≥1.0 mM).
Biomass grown with day-night cycles had a night endogenous decay rate of 0.05-1.0/day, with decay being faster with higher LI and the beginning of dark periods. Supplying light at a fraction of daylight reduced dark decay rate and improved overall biomass productivity.
This dissertation systematically evaluates and synthesizes fundamental growth factors of cyanobacteria: light, inorganic carbon (Ci), and pH. LI remains the most critical growth condition to promote biomass productivity and desired forms of biomass, while Ci and pH now can be managed to support optimal productivity.
ContributorsNguyen, Binh Thanh (Author) / Rittmann, Bruce E. (Thesis advisor) / Krajmalnik-Brown, Rosa (Committee member) / Westerhoff, Paul (Committee member) / Arizona State University (Publisher)
Created2015
Description
The immune system plays a dual role during neoplastic progression. It can suppress tumor growth by eliminating cancer cells, and also promote neoplastic expansion by either selecting for tumor cells that are fitter to survive in an immunocompetent host or by establishing the right conditions within the tumor microenvironment. First, I present a model to study the dynamics of subclonal evolution of cancer. I model selection through time as an epistatic process. That is, the fitness change in a given cell is not simply additive, but depends on previous mutations. Simulation studies indicate that tumors are composed of myriads of small subclones at the time of diagnosis. Because some of these rare subclones harbor pre-existing treatment-resistant mutations, they present a major challenge to precision medicine. Second, I study the question of self and non-self discrimination by the immune system, which is fundamental in the field in cancer immunology. By performing a quantitative analysis of the biochemical properties of thousands of MHC class I peptides, I find that hydrophobicity of T cell receptors contact residues is a hallmark of immunogenic epitopes. Based on these findings, I further develop a computational model to predict immunogenic epitopes which facilitate the development of T cell vaccines against pathogen and tumor antigens. Lastly, I study the effect of early detection in the context of Ebola. I develope a simple mathematical model calibrated to the transmission dynamics of Ebola virus in West Africa. My findings suggest that a strategy that focuses on early diagnosis of high-risk individuals, caregivers, and health-care workers at the pre-symptomatic stage, when combined with public health measures to improve the speed and efficacy of isolation of infectious individuals, can lead to rapid reductions in Ebola transmission.
ContributorsChowell-Puente, Diego (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Anderson, Karen S (Thesis advisor) / Maley, Carlo C (Committee member) / Wilson Sayres, Melissa A (Committee member) / Blattman, Joseph N (Committee member) / Arizona State University (Publisher)
Created2016
Description
Phytoplankton comprise the base of the marine food web, and, along with heterotrophic protists, they are key players in the biological pump that transports carbon from the surface to the deep ocean. In the world's subtropical oligotrophic gyres, plankton communities exhibit strong seasonality. Winter storms vent deep water into the euphotic zone, triggering a surge in primary productivity in the form of a spring phytoplankton bloom. Although the hydrographic trends of this "boom and bust" cycle have been well studied for decades, community composition and its seasonal and annual variability remains an integral subject of research. It is hypothesized here that proportions of different phytoplankton and protistan taxa vary dramatically between seasons and years, and that picoplankton represent an important component of this community and contributor to carbon in the surface ocean. Monthly samples from the Bermuda Atlantic Time-series Study (BATS) site were analyzed by epifluorescence microscopy, which permits classification by morphology, size, and trophic type. Epifluorescence counts were supplemented with flow cytometric quantification of Synechococcus, Prochlorococcus, and autotrophic pico- and nanoeukaryotes. Results from this study indicate Synechococcus and Prochlorococcus, prymnesiophytes, and hetero- and mixotrophic nano- and dinoflagellates were the major players in the BATS region plankton community. Ciliates, cryptophytes, diatoms, unidentified phototrophs, and other taxa represented rarer groups. Both flow cytometry and epifluorescence microscopy revealed Synechococcus to be most prevalent during the spring bloom. Prymnesiophytes likewise displayed distinct seasonality, with the highest concentrations again being noted during the bloom. Heterotrophic nano- and dinoflagellates, however, were most common in fall and winter. Mixotrophic dinoflagellates, while less abundant than their heterotrophic counterparts, displayed similar seasonality. A key finding of this study was the interannual variability revealed between the two years. While most taxa were more abundant in the first year, prymnesiophytes experienced much greater abundance in the second year bloom. Analyses of integrated carbon revealed further stark contrasts between the two years, both in terms of total carbon and the contributions of different groups. Total integrated carbon varied widely in the first study year but displayed less fluctuation after June 2009, and values were noticeably reduced in the second year.
ContributorsHansen, Amy (Author) / Neuer, Susanne (Thesis advisor) / Krajmalnik-Brown, Rosa (Committee member) / Sommerfeld, Milton (Committee member) / Arizona State University (Publisher)
Created2010
Description
Resistance to existing anti-cancer drugs poses a key challenge in the field of medical oncology, in that it results in the tumor not responding to treatment using the same medications to which it responded previously, leading to treatment failure. Adaptive therapy utilizes evolutionary principles of competitive suppression, leveraging competition between drug resistant and drug sensitive cells, to keep the population of drug resistant cells under control, thereby extending time to progression (TTP), relative to standard treatment using maximum tolerated dose (MTD). Development of adaptive therapy protocols is challenging, as it involves many parameters, and the number of parameters increase exponentially for each additional drug. Furthermore, the drugs could have a cytotoxic (killing cells directly), or a cytostatic (inhibiting cell division) mechanism of action, which could affect treatment outcome in important ways. I have implemented hybrid agent-based computational models to investigate adaptive therapy, using either a single drug (cytotoxic or cytostatic), or two drugs (cytotoxic or cytostatic), simulating three different adaptive therapy protocols for treatment using a single drug (dose modulation, intermittent, dose-skipping), and seven different treatment protocols for treatment using two drugs: three dose modulation (DM) protocols (DM Cocktail Tandem, DM Ping-Pong Alternate Every Cycle, DM Ping-Pong on Progression), and four fixed-dose (FD) protocols (FD Cocktail Intermittent, FD Ping-Pong Intermittent, FD Cocktail Dose-Skipping, FD Ping-Pong Dose-Skipping). The results indicate a Goldilocks level of drug exposure to be optimum, with both too little and too much drug having adverse effects. Adaptive therapy works best under conditions of strong cellular competition, such as high fitness costs, high replacement rates, or high turnover. Clonal competition is an important determinant of treatment outcome, and as such treatment using two drugs leads to more favorable outcome than treatment using a single drug. Switching drugs every treatment cycle (ping-pong) protocols work particularly well, as well as cocktail dose modulation, particularly when it is feasible to have a highly sensitive measurement of tumor burden. In general, overtreating seems to have adverse survival outcome, and triggering a treatment vacation, or stopping treatment sooner when the tumor is shrinking seems to work well.
ContributorsSaha, Kaushik (Author) / Maley, Carlo C (Thesis advisor) / Forrest, Stephanie (Committee member) / Anderson, Karen S (Committee member) / Cisneros, Luis H (Committee member) / Arizona State University (Publisher)
Created2023
Characterization and Manipulation of Microbiomes From Arid Landfills for Improved Methane Production
Description
Environmentally harmful byproducts from solid waste’s decomposition, including methane (CH4) emissions, are managed through standardized landfill engineering and gas-capture mechanisms. Yet only a limited number of studies have analyzed the development and composition of Bacteria and Archaea involved in CH4 production from landfills. The objectives of this research were to compare microbiomes and bioactivity from CH4-producing communities in contrasting spatial areas of arid landfills and to tests a new technology to biostimulate CH4 production (methanogenesis) from solid waste under dynamic environmental conditions controlled in the laboratory. My hypothesis was that the diversity and abundance of methanogenic Archaea in municipal solid waste (MSW), or its leachate, play an important role on CH4 production partially attributed to the group’s wide hydrogen (H2) consumption capabilities. I tested this hypothesis by conducting complementary field observations and laboratory experiments. I describe niches of methanogenic Archaea in MSW leachate across defined areas within a single landfill, while demonstrating functional H2-dependent activity. To alleviate limited H2 bioavailability encountered in-situ, I present biostimulant feasibility and proof-of-concepts studies through the amendment of zero valent metals (ZVMs). My results demonstrate that older-aged MSW was minimally biostimulated for greater CH4 production relative to a control when exposed to iron (Fe0) or manganese (Mn0), due to highly discernable traits of soluble carbon, nitrogen, and unidentified fluorophores found in water extracts between young and old aged, starting MSW. Acetate and inhibitory H2 partial pressures accumulated in microcosms containing old-aged MSW. In a final experiment, repeated amendments of ZVMs to MSW in a 600 day mesocosm experiment mediated significantly higher CH4 concentrations and yields during the first of three ZVM injections. Fe0 and Mn0 experimental treatments at mesocosm-scale also highlighted accelerated development of seemingly important, but elusive Archaea including Methanobacteriaceae, a methane-producing family that is found in diverse environments. Also, prokaryotic classes including Candidatus Bathyarchaeota, an uncultured group commonly found in carbon-rich ecosystems, and Clostridia; All three taxa I identified as highly predictive in the time-dependent progression of MSW decomposition. Altogether, my experiments demonstrate the importance of H2 bioavailability on CH4 production and the consistent development of Methanobacteriaceae in productive MSW microbiomes.
ContributorsReynolds, Mark Christian (Author) / Cadillo-Quiroz, Hinsby (Thesis advisor) / Krajmalnik-Brown, Rosa (Thesis advisor) / Wang, Xuan (Committee member) / Kavazanjian, Edward (Committee member) / Arizona State University (Publisher)
Created2022
Description
Emerging pathogens present several challenges to medical diagnostics. Primarily, the exponential spread of a novel pathogen through naïve populations require a rapid and overwhelming diagnostic response at the site of outbreak. While point-of-care (PoC) platforms have been developed for detection of antigens, serologic responses, and pathogenic genomes, only nucleic acid diagnostics currently have the potential to be developed and manufactured within weeks of an outbreak owing to the speed of next-generation sequencing and custom DNA synthesis. Among nucleic acid diagnostics, isothermal amplification strategies are uniquely suited for PoC implementation due to their simple instrumentation and lack of thermocycling requirement. Unfortunately, isothermal strategies are currently prone to spurious nonspecific amplification, hindering their specificity and necessitating extensive empirical design pipelines that are both time and resource intensive. In this work, isothermal amplification strategies are extensively compared for their feasibility of implementation in outbreak response scenarios. One such technology, Loop-mediated Amplification (LAMP), is identified as having high-potential for rapid development and PoC deployment. Various approaches to abrogating nonspecific amplification are described including a novel in silico design tool based on coarse-grained simulation of interactions between thermophilic DNA polymerase and DNA strands in isothermal reaction conditions. Nonspecific amplification is shown to be due to stabilization of primer secondary structures by high concentrations of Bst DNA polymerase and a mechanism of micro-complement-mediated cross-priming is demonstrated as causal via nanopore sequencing of nonspecific reaction products. The resulting computational model predicts primer set background in 64% of 67 test assays and its usefulness is illustrated further by determining problematic primers in a West Nile Virus-specific LAMP primer set and optimizing primer 3’ nucleotides to eliminate micro-complements within the reaction, resulting in inhibition of background accumulation. Finally, the emergence of Orthopox monkeypox (MPXV) as a recurring threat is discussed and SimCycle is utilized to develop a novel technique for clade-specific discrimination of MPXV based on bridging viral genomic rearrangements (Bridging LAMP). Bridging LAMP is implemented in a 4-plex microfluidic format and demonstrates 100% sensitivity in detection of 100 copies of viral lysates and 45 crude MPXV-positive patient samples collected during the 2022 Clade IIb outbreak.
ContributorsKnappenberger, Mark Daniel (Author) / Anderson, Karen S (Thesis advisor) / LaBaer, Joshua (Committee member) / Roberson, Robert (Committee member) / Lindsay, Stuart (Committee member) / Arizona State University (Publisher)
Created2023
Cancer autoantibody biomarker discovery and validation using nucleic acid programmable protein array
Description
Currently in the US, many patients with cancer do not benefit from the population-based screening, due to challenges associated with the existing cancer screening scheme. Blood-based diagnostic assays have the potential to detect diseases in a non-invasive way. Proteins released from small early tumors may only be present intermittently and get diluted to tiny concentrations in the blood, making them difficult to use as biomarkers. However, they can induce autoantibody (AAb) responses, which can amplify the signal and persist in the blood even if the antigen is gone. Circulating autoantibodies is a promising class of molecules that have potential to serve as early detection biomarkers for cancers. This Ph.D thesis aims to screen for autoantibody biomarkers for the early detection of two deadly cancer, basal-like breast cancer and lung adenocarcinoma. First, a method was developed to display proteins in both native and denatured conformation on protein array. This method adopted a novel protein tag technology, called HaloTag, to covalently immobilize proteins on glass slide surface. The covalent attachment allowed these proteins to endure harsh treatment without getting dissociated from slide surface, which enabled the profiling of antibody responses against both conformational and linear epitopes. Next, a plasma screening protocol was optimized to significantly increase signal to noise ratio of protein array based AAb detection. Following this, the AAb responses in basal-like breast cancer were explored using nucleic acid programmable protein arrays (NAPPA) containing 10,000 full-length human proteins in 45 cases and 45 controls. After verification in a large sample set (145 basal-like breast cancer cases / 145 controls / 70 non-basal breast cancer) by ELISA, a 13-AAb classifier was developed to differentiate patients from controls with a sensitivity of 33% at 98% specificity. Similar approach was also applied to the lung cancer study to identify AAbs that distinguished lung cancer patients from computed-tomography positive benign pulmonary nodules (137 lung cancer cases, 127 smoker controls, 170 benign controls). In this study, two panels of AAbs were discovered that showed promising sensitivity and specificity. Six out of eight AAb targets were also found to have elevated mRNA level in lung adenocarcinoma patients using TCGA data. These projects as a whole provide novel insights on the association between AAbs and cancer, as well as general B cell antigenicity against self-proteins.
ContributorsWang, Jie (Author) / LaBaer, Joshua (Thesis advisor) / Anderson, Karen S (Committee member) / Lake, Douglas F (Committee member) / Chang, Yung (Committee member) / Arizona State University (Publisher)
Created2015