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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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
Aboveground net primary production (ANPP) and belowground net primary production (BNPP) may not be influenced equally by the same factors in arid grasslands. Precipitation is known to affect ANPP and BNPP, while soil fauna such as nematodes affect the BNPP through herbivory and predation. This study on black grama grass (Bouteloua eriopoda) in the Chihuahuan Desert investigates the effects of precipitation and nematode presence or absence on net primary production (NPP) as well as the partitioning between the aboveground and belowground components, in this case, the fraction of total net primary production occurring belowground (fBNPP). I used a factorial experiment to investigate the effects of both precipitation and nematode presence on the components of NPP. I used rainout shelters and an irrigation system to alter precipitation totals, while I used defaunated and re-inoculated soil for the nematode treatments. Precipitation treatment and seasonal soil moisture had no effect on the BNPP and a nonsignificant positive effect on the ANPP. The fBNPP decreased with increasing precipitation and seasonal soil moisture, though without a significant effect. No predator nematodes were found in any of the microcosms at the end of the experiment, though other functional groups of nematodes, including herbivores, were found in the microcosms. Total nematode numbers did not vary significantly between nematode treatments, indicating that the inoculation process did not last for the whole experiment or that nematodes had little plant material to eat and resulted in low population density. Nematode presence did not affect the BNPP, ANPP, or the fBNPP. There were no significant interactions between precipitation and nematode treatment. The results are inconclusive, possibly as a result of ecosystem trends during an unusually high precipitation year, as well as the very low NPP values in the experiment that correlated with low nematode community numbers.
ContributorsWiedenfeld, Amy (Author) / Sala, Osvaldo (Thesis advisor) / Gerber, Leah (Committee member) / Hall, Sharon (Committee member) / Arizona State University (Publisher)
Created2018
Description
Vector control plays an important role in the prevention and control of mosquito-borne diseases (MBDs). As there are no (prophylactic) drugs and/or vaccines available for many arboviral diseases (such as zika, chikungunya, Saint Louis encephalitis, Ross River virus), the frontline approach to prevent or reduce disease morbidity and mortality is through the reduction of the mosquito vector population size and/or reducing vector-human contact using insecticides. Frontline tools in malaria (an MBD caused by a parasite) control and elimination have been drugs (targeting the malaria parasite) and insecticides (targeting the vectors) through indoor residual spraying (IRS) (spraying the internal walls and sometimes the roofs of dwellings with residual insecticides to kill adult mosquito vectors), and long-lasting insecticidal nets (LLINs), while arboviral vectors are frequently targeted using outdoor fogging and space spraying (indoor or outdoor spraying of insecticides to kill adult mosquito vectors). Integrative and novel vector control efforts are urgently needed since the aforementioned tools may not be as effective against those mosquito species that are resistant to insecticides and/or have a different (or changed) behavior allowing them to avoid existing tools. In Chapters 2 and 3, I investigate mosquito vector surveillance in Arizona by (i) discussing the species composition and public health implications of the State’s mosquito fauna, and (ii) comparing the effectiveness of 4 different carbon dioxide (CO2) sources in attracting different mosquito species on the Arizona State University Tempe Campus. In Chapters 4 and 5, I investigate a novel vector control tool by (i) completing a literature review on using electric fields (EFs) to control insects, and (ii) presenting novel data on using Insulated Conductor Wires (ICWs) to generate EFs that prevent host-seeking female Aedes aegypti from entering spaces. In Chapter 6, I discuss the non-target effects of chemical malaria control on other arthropods, including other biological and mechanical infectious disease vectors. Overall, this dissertation highlights the important role that the development of novel surveillance and vector control tools could play in improved mosquito control, which ultimately will reduce disease morbidity and mortality.
ContributorsJobe, Ndey Bassin (Author) / Paaijmans, Krijn (Thesis advisor) / Cease, Arianne (Committee member) / Hall, Sharon (Committee member) / Huijben, Silvie (Committee member) / Arizona State University (Publisher)
Created2024
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
Decay of plant litter represents an enormous pathway for carbon (C) into the atmosphere but our understanding of the mechanisms driving this process is particularly limited in drylands. While microbes are a dominant driver of litter decay in most ecosystems, their significance in drylands is not well understood and abiotic drivers such as photodegradation are commonly perceived to be more important. I assessed the significance of microbes to the decay of plant litter in the Sonoran Desert. I found that the variation in decay among 16 leaf litter types was correlated with microbial respiration rates (i.e. CO2 emission) from litter, and rates were strongly correlated with water-vapor sorption rates of litter. Water-vapor sorption during high-humidity periods activates microbes and subsequent respiration appears to be a significant decay mechanism. I also found that exposure to sunlight accelerated litter decay (i.e. photodegradation) and enhanced subsequent respiration rates of litter. The abundance of bacteria (but not fungi) on the surface of litter exposed to sunlight was strongly correlated with respiration rates, as well as litter decay, implying that exposure to sunlight facilitated activity of surface bacteria which were responsible for faster decay. I also assessed the response of respiration to temperature and moisture content (MC) of litter, as well as the relationship between relative humidity and MC. There was a peak in respiration rates between 35-40oC, and, unexpectedly, rates increased from 55 to 70oC with the highest peak at 70oC, suggesting the presence of thermophilic microbes or heat-tolerant enzymes. Respiration rates increased exponentially with MC, and MC was strongly correlated with relative humidity. I used these relationships, along with litter microclimate and C loss data to estimate the contribution of this pathway to litter C loss over 34 months. Respiration was responsible for 24% of the total C lost from litter – this represents a substantial pathway for C loss, over twice as large as the combination of thermal and photochemical abiotic emission. My findings elucidate two mechanisms that explain why microbial drivers were more significant than commonly assumed: activation of microbes via water-vapor sorption and high respiration rates at high temperatures.
ContributorsTomes, Alexander (Author) / Day, Thomas (Thesis advisor) / Garcia-Pichel, Ferran (Committee member) / Ball, Becky (Committee member) / Hall, Sharon (Committee member) / Roberson, Robert (Committee member) / Arizona State University (Publisher)
Created2020