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: Cease, Arianne
- 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
Land management practices such as domestic animal grazing can alter plant communities via changes in soil structure and chemistry, species composition, and plant nutrient content. These changes can affect the abundance and quality of plants consumed by insect herbivores with consequent changes in population dynamics. These population changes can translate to massive crop damage and pest control costs. My dissertation focused on Oedaleus asiaticus, a dominant Asian locust, and had three main objectives. First, I identified morphological, physiological, and behavioral characteristics of the migratory ("brown") and non-migratory ("green") phenotypes. I found that brown morphs had longer wings, larger thoraxes and higher metabolic rates compared to green morphs, suggesting that developmental plasticity allows greater migratory capacity in the brown morph of this locust. Second, I tested the hypothesis of a causal link between livestock overgrazing and an increase in migratory swarms of O. asiaticus. Current paradigms generally assume that increased plant nitrogen (N) should enhance herbivore performance by relieving protein-limitation, increasing herbivorous insect populations. I showed, in contrast to this scenario, that host plant N-enrichment and high protein artificial diets decreased the size and viability of O. asiaticus. Plant N content was lowest and locust abundance highest in heavily livestock-grazed fields where soils were N-depleted, likely due to enhanced erosion and leaching. These results suggest that heavy livestock grazing promotes outbreaks of this locust by reducing plant protein content. Third, I tested for the influence of dietary imbalance, in conjunction with high population density, on migratory plasticity. While high population density has clearly been shown to induce the migratory morph in several locusts, the effect of diet has been unclear. I found that locusts reared at high population density and fed unfertilized plants (i.e. high quality plants for O. asiaticus) had the greatest migratory capacity, and maintained a high percent of brown locusts. These results did not support the hypothesis that poor-quality resources increased expression of migratory phenotypes. This highlights a need to develop new theoretical frameworks for predicting how environmental factors will regulate migratory plasticity in locusts and perhaps other insects.
ContributorsCease, Arianne (Author) / Harrison, Jon (Thesis advisor) / Elser, James (Thesis advisor) / DeNardo, Dale (Committee member) / Quinlan, Michael (Committee member) / Sabo, John (Committee member) / Arizona State University (Publisher)
Created2012
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
Understanding how and why animals choose what to eat is one of the fundamental goals of nutritional and behavioral biology. This question can be scaled to animals that live in social groups, including eusocial insects. One of the factors that plays an important role in foraging decisions is the prevalence of specific nutrients and their relative balance. This dissertation explores the role of relative nutrient content in the food selection decisions of a species that is eusocial and also agricultural, the desert leafcutter ant Acromyrmex versicolor. A dietary choice assay, in which the relative amount of protein and carbohydrates in the available diets was varied, demonstrated that A. versicolor colonies regulate relative collection of protein and carbohydrates. Tracking the foraging behavior of individual workers revelaed that foragers vary in their relative collection of experimental diets and in their foraging frequency, but that there is no relationship between these key factors of foraging behavior. The high proportion of carbohydrates preferred by lab colonies suggests that they forage to nutritionally support the fungus rather than brood and workers. To test this, the relative amounts of 1) fungus, and 2) brood (larvae) was manipulated and foraging response was measured. Changing the amount of brood had no effect on foraging. Although decreasing the size of fungus gardens did not change relative P:C collection, it produced significant increases in caloric intake, supporting the assertion that the fungus is the main driver of colony nutrient regulation. The nutritional content of naturally harvested forage material collected from field colonies was measured, as was recruitment to experimental diets with varying relative macronutrient content. Field results confirmed a strong colony preference for high carbohydrate diets. They also indicated that this species may, at times, be limited in its ability to collect sufficiently high levels of carbohydrates to meet optimal intake. This dissertation provides important insights about fundamental aspects of leafcutter ant biology and extends our understanding of the role of relative nutrient content in foraging decisions to systems that span multiple trophic levels.
ContributorsSmith, Nathan Edward (Author) / Fewell, Jennifer H (Thesis advisor) / Harrison, Jon F (Committee member) / Pavlic, Ted (Committee member) / Cease, Arianne (Committee member) / Hoelldobler, Bert (Committee member) / Arizona State University (Publisher)
Created2023
Description
The migratory grasshopper (Melanoplus sanguinipes) is one of the most economically important grasshoppers in the western rangelands of the United States (US), capable of causing incredible amounts of damage to crops and rangelands. While M. sanguinipes has been the focus of many research studies, areas like field nutritional physiology and ecology, and interactions between nutritional physiology and biopesticide resistance have very little research. This dissertation presents a multifaceted approach through three research-driven chapters that examine the nutritional physiology of M. sanguinipes and how it interacts with an entomopathogenic fungus for grasshopper management, as well as the challenges of using biopesticides for grasshopper management. Using the Geometric Framework for Nutrition (GFN), I established baseline macronutrient intake for M. sanguinipes, both in laboratory and field populations. Through this work, I found that field and lab populations can exhibit different protein (p) to carbohydrate (c) ratios, or Intake Targets (ITs), but that the field populations had ITs that matched the nutrients available in their environment. I also used the GFN to show that infections with the fungal entomopathogen Metarhizium robertsii DWR2009 did not alter ITs in M. sanguinipes. Although, when confined to carbohydrate- or protein-biased diets, infected grasshoppers had a slightly extended lifespan relative to grasshoppers fed balanced protein:carbohydrate diets. Interestingly, in a postmortem for the grasshopper, the fungus was only able to effectively sporulate on grasshoppers fed the 1p:1c diets, suggesting that grasshopper diet can have substantial impacts on the spread of fungal biopesticides throughout a population, in the absence of any inhibitory abiotic factors. Lastly, I examined the major barriers to fungal and microsporidian biopesticide usage in the United States, including low efficacy, thermal and environmental sensitivity, non-target effects, unregistered or restricted use, and economic or accessibility barriers. I also explored potential solutions to these challenges. This dissertation's focus on Melanoplus sanguinipes and Metarhizium roberstii Strain DWR2009, generates new information about how nutritional physiology and immunology intersect to impact M. sanguinipes performance. The methodology in each of the experimental chapters provides a framework for examining other problematic grasshopper species, by determining baseline nutritional physiology, and coupling nutrition with immunology to maximize the effectiveness of biological pesticides.
ContributorsZembrzuski, Deanna (Author) / Cease, Arianne (Thesis advisor) / Harrison, Jon (Committee member) / Angilletta, Michael (Committee member) / Jaronski, Stefan (Committee member) / Arizona State University (Publisher)
Created2023
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
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
Description
Eusocial insect colonies have often been imagined as “superorganisms” exhibiting tight homeostasis at the colony level. However, colonies lack the tight spatial and organizational integration that many multicellular, unitary organisms exhibit. Precise regulation requires rapid feedback, which is often not possible when nestmates are distributed across space, making decisions asynchronously. Thus, one should expect poorer regulation in superorganisms than unitary organisms.Here, I investigate aspects of regulation in collective foraging behaviors that involve both slow and rapid feedback processes. In Chapter 2, I examine a tightly coupled system with near-instantaneous signaling: teams of weaver ants cooperating to transport massive prey items back to their nest. I discover that over an extreme range of scenarios—even up vertical surfaces—the efficiency per transporter remains constant. My results suggest that weaver ant colonies are maximizing their total intake rate by regulating the allocation of transporters among loads. This is an exception that “proves the rule;” the ant teams are recapitulating the physical integration of unitary organisms.
Next, I focus on a process with greater informational constraints, with loose temporal and spatial integration. In Chapter 3, I measure the ability of solitarily foraging Ectatomma ruidum colonies to balance their collection of protein and carbohydrates given different nutritional environments. Previous research has found that ant species can precisely collect a near-constant ratio between these two macronutrients, but I discover these studies were using flawed statistical approaches. By developing a quantitative measure of regulatory effect size, I show that colonies of E. ruidum are relatively insensitive to small differences in food source nutritional content, contrary to previously published claims.
In Chapter 4, I design an automated, micro-RFID ant tracking system to investigate how the foraging behavior of individuals integrates into colony-level nutrient collection. I discover that spatial fidelity to food resources, not individual specialization on particular nutrient types, best predicts individual forager behavior. These findings contradict previously published experiments that did not use rigorous quantitative measures of specialization and confounded the effects of task type and resource location.
ContributorsBurchill, Andrew Taylor (Author) / Pavlic, Theodore P (Thesis advisor) / Pratt, Stephen C (Thesis advisor) / Hölldobler, Bert (Committee member) / Cease, Arianne (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2022