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: Wang, Xuan
- Creators: Kostelich, Eric
Description
In 1968, phycologist M.R. Droop published his famous discovery on the functional relationship between growth rate and internal nutrient status of algae in chemostat culture. The simple notion that growth is directly dependent on intracellular nutrient concentration is useful for understanding the dynamics in many ecological systems. The cell quota in particular lends itself to ecological stoichiometry, which is a powerful framework for mathematical ecology. Three models are developed based on the cell quota principal in order to demonstrate its applications beyond chemostat culture.
First, a data-driven model is derived for neutral lipid synthesis in green microalgae with respect to nitrogen limitation. This model synthesizes several established frameworks in phycology and ecological stoichiometry. The model demonstrates how the cell quota is a useful abstraction for understanding the metabolic shift to neutral lipid production that is observed in certain oleaginous species.
Next a producer-grazer model is developed based on the cell quota model and nutrient recycling. The model incorporates a novel feedback loop to account for animal toxicity due to accumulation of nitrogen waste. The model exhibits rich, complex dynamics which leave several open mathematical questions.
Lastly, disease dynamics in vivo are in many ways analogous to those of an ecosystem, giving natural extensions of the cell quota concept to disease modeling. Prostate cancer can be modeled within this framework, with androgen the limiting nutrient and the prostate and cancer cells as competing species. Here the cell quota model provides a useful abstraction for the dependence of cellular proliferation and apoptosis on androgen and the androgen receptor. Androgen ablation therapy is often used for patients in biochemical recurrence or late-stage disease progression and is in general initially effective. However, for many patients the cancer eventually develops resistance months to years after treatment begins. Understanding how and predicting when hormone therapy facilitates evolution of resistant phenotypes has immediate implications for treatment. Cell quota models for prostate cancer can be useful tools for this purpose and motivate applications to other diseases.
First, a data-driven model is derived for neutral lipid synthesis in green microalgae with respect to nitrogen limitation. This model synthesizes several established frameworks in phycology and ecological stoichiometry. The model demonstrates how the cell quota is a useful abstraction for understanding the metabolic shift to neutral lipid production that is observed in certain oleaginous species.
Next a producer-grazer model is developed based on the cell quota model and nutrient recycling. The model incorporates a novel feedback loop to account for animal toxicity due to accumulation of nitrogen waste. The model exhibits rich, complex dynamics which leave several open mathematical questions.
Lastly, disease dynamics in vivo are in many ways analogous to those of an ecosystem, giving natural extensions of the cell quota concept to disease modeling. Prostate cancer can be modeled within this framework, with androgen the limiting nutrient and the prostate and cancer cells as competing species. Here the cell quota model provides a useful abstraction for the dependence of cellular proliferation and apoptosis on androgen and the androgen receptor. Androgen ablation therapy is often used for patients in biochemical recurrence or late-stage disease progression and is in general initially effective. However, for many patients the cancer eventually develops resistance months to years after treatment begins. Understanding how and predicting when hormone therapy facilitates evolution of resistant phenotypes has immediate implications for treatment. Cell quota models for prostate cancer can be useful tools for this purpose and motivate applications to other diseases.
ContributorsPacker, Aaron (Author) / Kuang, Yang (Thesis advisor) / Nagy, John (Committee member) / Smith, Hal (Committee member) / Kostelich, Eric (Committee member) / Kang, Yun (Committee member) / Arizona State University (Publisher)
Created2014
Description
Predicting resistant prostate cancer is critical for lowering medical costs and improving the quality of life of advanced prostate cancer patients. I formulate, compare, and analyze two mathematical models that aim to forecast future levels of prostate-specific antigen (PSA). I accomplish these tasks by employing clinical data of locally advanced prostate cancer patients undergoing androgen deprivation therapy (ADT). I demonstrate that the inverse problem of parameter estimation might be too complicated and simply relying on data fitting can give incorrect conclusions, since there is a large error in parameter values estimated and parameters might be unidentifiable. I provide confidence intervals to give estimate forecasts using data assimilation via an ensemble Kalman Filter. Using the ensemble Kalman Filter, I perform dual estimation of parameters and state variables to test the prediction accuracy of the models. Finally, I present a novel model with time delay and a delay-dependent parameter. I provide a geometric stability result to study the behavior of this model and show that the inclusion of time delay may improve the accuracy of predictions. Also, I demonstrate with clinical data that the inclusion of the delay-dependent parameter facilitates the identification and estimation of parameters.
ContributorsBaez, Javier (Author) / Kuang, Yang (Thesis advisor) / Kostelich, Eric (Committee member) / Crook, Sharon (Committee member) / Gardner, Carl (Committee member) / Nagy, John (Committee member) / Arizona State University (Publisher)
Created2017
Description
The Multiple Antibiotic Resistance Regulator Family (MarR) are transcriptional regulators, many of which forms a dimer. Transcriptional regulation provides bacteria a stabilized responding system to ensure the bacteria is able to efficiently adapt to different environmental conditions. The main function of the MarR family is to create multiple antibiotic resistance from a mutated protein; this process occurs when the MarR regulates an operon. We hypothesized that different transcriptional regulator genes have interactions with each other. It is known that Salmonella pagC transcription is activated by three regulators, i.e., SlyA, MprA, and PhoP. Bacterial Adenylate Cyclase-based Two-Hybrid (BACTH) system was used to research the protein-protein interactions in SlyA, MprA, and PhoP as heterodimers and homodimers in vivo. Two fragments, T25 and T18, that lack endogenous adenylate cyclase activity, were used for construction of chimeric proteins and reconstruction of adenylate cyclase activity was tested. The significant adenylate cyclase activities has proved that SlyA is able to form homodimers. However, weak adenylate cyclase activities in this study has proved that MprA and PhoP are not likely to form homodimers, and no protein-protein interactions were detected in between SlyA, MprA and PhoP, which no heterodimers have formed in between three transcriptional regulators.
ContributorsTao, Zenan (Author) / Shi, Yixin (Thesis advisor) / Wang, Xuan (Committee member) / Bean, Heather (Committee member) / Arizona State University (Publisher)
Created2018
Description
Lignocellulosic biomass represents a renewable domestic feedstock that can support large-scale biochemical production processes for fuels and specialty chemicals. However, cost-effective conversion of lignocellulosic sugars into valuable chemicals by microorganisms still remains a challenge. Biomass recalcitrance to saccharification, microbial substrate utilization, bioproduct titer toxicity, and toxic chemicals associated with chemical pretreatments are at the center of the bottlenecks limiting further commercialization of lignocellulose conversion. Genetic and metabolic engineering has allowed researchers to manipulate microorganisms to overcome some of these challenges, but new innovative approaches are needed to make the process more commercially viable. Transport proteins represent an underexplored target in genetic engineering that can potentially help to control the input of lignocellulosic substrate and output of products/toxins in microbial biocatalysts. In this work, I characterize and explore the use of transport systems to increase substrate utilization, conserve energy, increase tolerance, and enhance biocatalyst performance.
ContributorsKurgan, Gavin (Author) / Wang, Xuan (Thesis advisor) / Nielsen, David (Committee member) / Misra, Rajeev (Committee member) / Nannenga, Brent (Committee member) / Arizona State University (Publisher)
Created2018
Description
Emergence of multidrug resistant (MDR) bacteria is a major concern to global health. One of the major MDR mechanisms bacteria employ is efflux pumps for the expulsion of drugs from the cell. In Escherichia coli, AcrAB-TolC proteins constitute the major chromosomally-encoded drug efflux system. AcrB, a trimeric membrane protein is well-known for its substrate promiscuity. It has the ability to efflux a broad spectrum of substrates alongside compounds such as dyes, detergent, bile salts and metabolites. Newly identified AcrB residues were shown to be functionally relevant in the drug binding and translocation pathway using a positive genetic selection strategy. These residues—Y49, V127, D153, G288, F453, and L486—were identified as the sites of suppressors of an alteration, F610A, that confers a drug hypersensitivity phenotype. Using site-directed mutagenesis (SDM) along with the real-time efflux and the classical minimum inhibitory concentration (MIC) assays, I was able to characterize the mechanism of suppression.
Three approaches were used for the characterization of these suppressors. The first approach focused on side chain specificity. The results showed that certain suppressor sites prefer a particular side chain property, such as size, to overcome the F610A defect. The second approach focused on the effects of efflux pump inhibitors. The results showed that though the suppressor residues were able to overcome the intrinsic defect of F610A, they were unable to overcome the extrinsic defect caused by the efflux pump inhibitors. This showed that the mechanism by which F610A imposes its effect on AcrB function is different than that of the efflux pump inhibitors. The final approach was to determine whether suppressors mapping in the periplasmic and trans-membrane domains act by the same or different mechanisms. The results showed both overlapping and distinct mechanisms of suppression.
To conclude, these approaches have provided a deeper understanding of the mechanisms by which novel suppressor residues of AcrB overcome the functional defect of the drug binding domain alteration, F610A.
Three approaches were used for the characterization of these suppressors. The first approach focused on side chain specificity. The results showed that certain suppressor sites prefer a particular side chain property, such as size, to overcome the F610A defect. The second approach focused on the effects of efflux pump inhibitors. The results showed that though the suppressor residues were able to overcome the intrinsic defect of F610A, they were unable to overcome the extrinsic defect caused by the efflux pump inhibitors. This showed that the mechanism by which F610A imposes its effect on AcrB function is different than that of the efflux pump inhibitors. The final approach was to determine whether suppressors mapping in the periplasmic and trans-membrane domains act by the same or different mechanisms. The results showed both overlapping and distinct mechanisms of suppression.
To conclude, these approaches have provided a deeper understanding of the mechanisms by which novel suppressor residues of AcrB overcome the functional defect of the drug binding domain alteration, F610A.
ContributorsBlake, Mellecha (Author) / Misra, Rajeev (Thesis advisor) / Stout, Valerie (Committee member) / Wang, Xuan (Committee member) / Arizona State University (Publisher)
Created2016
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
Biomass synthesis is a competing factor in biological systems geared towards generation of commodity and specialty chemicals, ultimately limiting maximum titer and yield; in this thesis, a widely generalizable, modular approach focused on decoupling biomass synthesis from the production of the phenylalanine in a genetically modified strain of E. coli BW25113 was explored with the use of synthetic trans-encoded small RNA (sRNA) to achieve greater efficiency. The naturally occurring sRNA MicC was used as a scaffold, and combined on a plasmid with a promoter for anhydrous tetracycline (aTc) and a T1/TE terminator. The coding sequence corresponding to the target binding site for fourteen potentially growth-essential gene targets as well as non-essential lacZ was placed in the seed region of the of the sRNA scaffold and transformed into BW25113, effectively generating a unique strain for each gene target. The BW25113 strain corresponding to each gene target was screened in M9 minimal media; decreased optical density and elongated cell morphology changes were observed and quantified in all induced sRNA cases where growth-essential genes were targeted. Six of the strains targeting different aspects of cell division that effectively suppressed growth and resulted in increased cell size were then screened for viability and metabolic activity in a scaled-up shaker flask experiment; all six strains were shown to be viable during stationary phase, and a metabolite analysis showed increased specific glucose consumption rates in induced strains, with unaffected specific glucose consumption rates in uninduced strains. The growth suppression, morphology and metabolic activity of the induced strains in BW25113 was compared to the bacteriostatic additives chloramphenicol, tetracycline, and streptomycin. At this same scale, the sRNA plasmid targeting the gene murA was transformed into BW25113 pINT-GA, a phenylalanine overproducer with the feedback resistant genes aroG and pheA overexpressed. Two induction times were explored during exponential phase, and while the optimal induction time was found to increase titer and yield amongst the BW25113 pINT-GA murA sRNA variant, overall this did not have as great a titer or yield as the BW25113 pINT-GA strain without the sRNA plasmid; this may be a result of the cell filamentation.
ContributorsHerschel, Daniel Jordan (Author) / Nielsen, David R (Thesis advisor) / Torres, César I (Committee member) / Wang, Xuan (Committee member) / Arizona State University (Publisher)
Created2016
Description
Efforts to treat prostate cancer have seen an uptick, as the world’s most commoncancer in men continues to have increasing global incidence. Clinically, metastatic
prostate cancer is most commonly treated with hormonal therapy. The idea behind
hormonal therapy is to reduce androgen production, which prostate cancer cells
require for growth. Recently, the exploration of the synergistic effects of the drugs
used in hormonal therapy has begun. The aim was to build off of these recent
advancements and further refine the synergistic drug model. The advancements I
implement come by addressing biological shortcomings and improving the model’s
internal mechanistic structure. The drug families being modeled, anti-androgens,
and gonadotropin-releasing hormone analogs, interact with androgen production in a
way that is not completely understood in the scientific community. Thus the models
representing the drugs show progress through their ability to capture their effect
on serum androgen. Prostate-specific antigen is the primary biomarker for prostate
cancer and is generally how population models on the subject are validated. Fitting
the model to clinical data and comparing it to other clinical models through the
ability to fit and forecast prostate-specific antigen and serum androgen is how this
improved model achieves validation. The improved model results further suggest that
the drugs’ dynamics should be considered in adaptive therapy for prostate cancer.
prostate cancer is most commonly treated with hormonal therapy. The idea behind
hormonal therapy is to reduce androgen production, which prostate cancer cells
require for growth. Recently, the exploration of the synergistic effects of the drugs
used in hormonal therapy has begun. The aim was to build off of these recent
advancements and further refine the synergistic drug model. The advancements I
implement come by addressing biological shortcomings and improving the model’s
internal mechanistic structure. The drug families being modeled, anti-androgens,
and gonadotropin-releasing hormone analogs, interact with androgen production in a
way that is not completely understood in the scientific community. Thus the models
representing the drugs show progress through their ability to capture their effect
on serum androgen. Prostate-specific antigen is the primary biomarker for prostate
cancer and is generally how population models on the subject are validated. Fitting
the model to clinical data and comparing it to other clinical models through the
ability to fit and forecast prostate-specific antigen and serum androgen is how this
improved model achieves validation. The improved model results further suggest that
the drugs’ dynamics should be considered in adaptive therapy for prostate cancer.
ContributorsReckell, Trevor (Author) / Kostelich, Eric (Thesis advisor) / Kuang, Yang (Committee member) / Mahalov, Alex (Committee member) / Arizona State University (Publisher)
Created2020
Description
Cancer is a disease involving abnormal growth of cells. Its growth dynamics is perplexing. Mathematical modeling is a way to shed light on this progress and its medical treatments. This dissertation is to study cancer invasion in time and space using a mathematical approach. Chapter 1 presents a detailed review of literature on cancer modeling.
Chapter 2 focuses sorely on time where the escape of a generic cancer out of immune control is described by stochastic delayed differential equations (SDDEs). Without time delay and noise, this system demonstrates bistability. The effects of response time of the immune system and stochasticity in the tumor proliferation rate are studied by including delay and noise in the model. Stability, persistence and extinction of the tumor are analyzed. The result shows that both time delay and noise can induce the transition from low tumor burden equilibrium to high tumor equilibrium. The aforementioned work has been published (Han et al., 2019b).
In Chapter 3, Glioblastoma multiforme (GBM) is studied using a partial differential equation (PDE) model. GBM is an aggressive brain cancer with a grim prognosis. A mathematical model of GBM growth with explicit motility, birth, and death processes is proposed. A novel method is developed to approximate key characteristics of the wave profile, which can be compared with MRI data. Several test cases of MRI data of GBM patients are used to yield personalized parameterizations of the model. The aforementioned work has been published (Han et al., 2019a).
Chapter 4 presents an innovative way of forecasting spatial cancer invasion. Most mathematical models, including the ones described in previous chapters, are formulated based on strong assumptions, which are hard, if not impossible, to verify due to complexity of biological processes and lack of quality data. Instead, a nonparametric forecasting method using Gaussian processes is proposed. By exploiting the local nature of the spatio-temporal process, sparse (in terms of time) data is sufficient for forecasting. Desirable properties of Gaussian processes facilitate selection of the size of the local neighborhood and computationally efficient propagation of uncertainty. The method is tested on synthetic data and demonstrates promising results.
Chapter 2 focuses sorely on time where the escape of a generic cancer out of immune control is described by stochastic delayed differential equations (SDDEs). Without time delay and noise, this system demonstrates bistability. The effects of response time of the immune system and stochasticity in the tumor proliferation rate are studied by including delay and noise in the model. Stability, persistence and extinction of the tumor are analyzed. The result shows that both time delay and noise can induce the transition from low tumor burden equilibrium to high tumor equilibrium. The aforementioned work has been published (Han et al., 2019b).
In Chapter 3, Glioblastoma multiforme (GBM) is studied using a partial differential equation (PDE) model. GBM is an aggressive brain cancer with a grim prognosis. A mathematical model of GBM growth with explicit motility, birth, and death processes is proposed. A novel method is developed to approximate key characteristics of the wave profile, which can be compared with MRI data. Several test cases of MRI data of GBM patients are used to yield personalized parameterizations of the model. The aforementioned work has been published (Han et al., 2019a).
Chapter 4 presents an innovative way of forecasting spatial cancer invasion. Most mathematical models, including the ones described in previous chapters, are formulated based on strong assumptions, which are hard, if not impossible, to verify due to complexity of biological processes and lack of quality data. Instead, a nonparametric forecasting method using Gaussian processes is proposed. By exploiting the local nature of the spatio-temporal process, sparse (in terms of time) data is sufficient for forecasting. Desirable properties of Gaussian processes facilitate selection of the size of the local neighborhood and computationally efficient propagation of uncertainty. The method is tested on synthetic data and demonstrates promising results.
ContributorsHan, Lifeng (Author) / Kuang, Yang (Thesis advisor) / Fricks, John (Thesis advisor) / Kostelich, Eric (Committee member) / Baer, Steve (Committee member) / Gumel, Abba (Committee member) / Arizona State University (Publisher)
Created2020
Description
The analysis focuses on a two-population, three-dimensional model that attempts to accurately model the growth and diffusion of glioblastoma multiforme (GBM), a highly invasive brain cancer, throughout the brain. Analysis into the sensitivity of the model to
changes in the diffusion, growth, and death parameters was performed, in order to find a set of parameter values that accurately model observed tumor growth for a given patient. Additional changes were made to the diffusion parameters to account for the arrangement of nerve tracts in the brain, resulting in varying rates of diffusion. In general, small changes in the growth rates had a large impact on the outcome of the simulations, and for each patient there exists a set of parameters that allow the model to simulate a tumor that matches observed tumor growth in the patient over a period of two or three months. Furthermore, these results are more accurate with anisotropic diffusion, rather than isotropic diffusion. However, these parameters lead to inaccurate results for patients with tumors that undergo no observable growth over the given time interval. While it is possible to simulate long-term tumor growth, the simulation requires multiple comparisons to available MRI scans in order to find a set of parameters that provide an accurate prognosis.
changes in the diffusion, growth, and death parameters was performed, in order to find a set of parameter values that accurately model observed tumor growth for a given patient. Additional changes were made to the diffusion parameters to account for the arrangement of nerve tracts in the brain, resulting in varying rates of diffusion. In general, small changes in the growth rates had a large impact on the outcome of the simulations, and for each patient there exists a set of parameters that allow the model to simulate a tumor that matches observed tumor growth in the patient over a period of two or three months. Furthermore, these results are more accurate with anisotropic diffusion, rather than isotropic diffusion. However, these parameters lead to inaccurate results for patients with tumors that undergo no observable growth over the given time interval. While it is possible to simulate long-term tumor growth, the simulation requires multiple comparisons to available MRI scans in order to find a set of parameters that provide an accurate prognosis.
ContributorsTrent, Austin Lee (Author) / Kostelich, Eric (Thesis advisor) / Gumel, Abba (Committee member) / Kuang, Yang (Committee member) / Arizona State University (Publisher)
Created2020