Matching Items (105)
Description
There is a need in the ecology literature to have a discussion about the fundamental theories from which population dynamics arises. Ad hoc model development is not uncommon in the field often as a result of a need to publish rapidly and frequently. Ecologists and statisticians like Robert J. Steidl and Kenneth P Burnham have called for a more deliberative approach they call "hard thinking". For example, the phenomena of population growth can be captured by almost any sigmoid function. The question of which sigmoid function best explains a data set cannot be answered meaningfully by statistical regression since that can only speak to the validity of the shape. There is a need to revisit enzyme kinetics and ecological stoichiometry to properly justify basal model selection in ecology. This dissertation derives several common population growth models from a generalized equation. The mechanistic validity of these models in different contexts is explored through a kinetic lens. The behavioral kinetic framework is then put to the test by examining a set of biologically plausible growth models against the 1968-1995 elk population count data for northern Yellowstone. Using only this count data, the novel Monod-Holling growth model was able to accurately predict minimum viable population and life expectancy despite both being exogenous to the model and data set. Lastly, the elk/wolf data from Yellowstone was used to compare the validity of the Rosenzweig-MacArthur and Arditi-Ginzburg models. They both were derived from a more general model which included both predator and prey mediated steps. The Arditi-Ginzburg model was able to fit the training data better, but only the Rosenzweig-MacArthur model matched the validation data. Accounting for animal sexual behavior allowed for the creation of the Monod-Holling model which is just as simple as the logistic differential equation but provides greater insights for conservation purposes. Explicitly acknowledging the ethology of wolf predation helps explain the differences in predictive performances by the best fit Rosenzweig-MacArthur and Arditi-Ginzburg models. The behavioral kinetic framework has proven to be a useful tool, and it has the ability to provide even further insights going forward.
ContributorsPringle, Jack Andrew McCracken (Author) / Anderies, John M (Thesis advisor) / Kuang, Yang (Committee member) / Milner, Fabio (Committee member) / Arizona State University (Publisher)
Created2022
Description
Ecology has been an actively studied topic recently, along with the rapid development of human microbiota-based technology. Scientists have made remarkable progress using bioinformatics tools to identify species and analyze composition. However, a thorough understanding of interspecies interactions of microbial ecosystems is still lacking, which has been a significant obstacle in the further development of related technologies. In this work, a genetic circuit design principle with synthetic biology approaches is developed to form two-strain microbial consortia with different inter-strain interactions. The microbial systems are well-defined and inducible. Co-culture experiment results show that our microbial consortia behave consistently with previous ecological knowledge and thus serves as excellent model systems to simulate ecosystems with similar interactions. Colony patterns also emerge when co-culturing multiple species on solid media. With the engineered microbial consortia, image-processing based methods were developed to quantify the shape of co-culture colonies and distinguish microbial consortia with different interactions. Factors that affect the population ratios were identified through induction and variations in the inoculation process. Further time-lapse experiments revealed the basic rules of colony growth, composition variation, patterning, and how spatial factors impact the co-culture colony.
ContributorsChen, Xingwen (Author) / Wang, Xiao (Thesis advisor) / Kuang, Yang (Committee member) / Tian, Xiaojun (Committee member) / Brafman, David (Committee member) / Plaisier, Christopher (Committee member) / Arizona State University (Publisher)
Created2022
Description
Transitioning into civilian life after military service is a challenging prospect. It can be difficult to find employment and maintain good mental health, and up to 70 percent of veterans experience homelessness or alcoholism. Upon discharge, many veterans pursue higher education as a way to reintegrate into civilian society. However, many studies have shown that veterans encounter multiple challenges during their attempt to reintegrate into civilian life, including anxiety, a lack of relevant skills, post-traumatic stress disorder (PTSD), and other issues that may lead to communication and interaction challenges in the higher education environment. Student veterans also face challenges in the lack of common language and culture clashes due to differences between military and college culture. This study used a mixed-methods approach to examine the challenges military veterans face related to language use in civilian life. The data was collected from 149 student veterans who completed a questionnaire and 11 student veterans who participated in interviews. Detailed analysis of collected data showed that student veterans experienced some challenges in language use, especially when they initially enrolled in their courses, but they seemed to have overcome challenges after spending time in the university setting. The veterans who had prior college education before joining the military seemed to have a slight advantage, having had experience using the academic language. The study also explored how student veterans chose to share their veteran status with other people in their university community. The findings showed that they strongly identified with their veteran identity and was comfortable sharing their status with others, but they also sometimes were reluctant to share their military experience in details because they were afraid that their peers would not understand.
ContributorsObaid, Naji (Author) / Matsuda, Aya (Thesis advisor) / Smith, David (Committee member) / James, Mark (Committee member) / Arizona State University (Publisher)
Created2022
Description
The mutual inhibition between synthetic gene circuits and cell growth produces growth feedback in the host-circuit system. Previous studies have demonstrated that the growth feedback has an marked impact on the molecular dynamics of the host-circuit system. However, the complexity of the growth feedback effect is not fully understood. A theoretical framework was developed to study the dynamics of the coupling between growth feedback and synthetic gene circuits. The study’s results reveal three major points about the impact of growth feedback. First, a nonlinear emergent behavior mediated by growth feedback. The unexpected behavior depends on the dynamic ribosome allocation between gene circuit expression and host cell growth. Second, the emergence and loss of unexpected qualitative states on the host-circuit system generated by ultrasensitive growth feedback. Third, the growth feedback-induced cooperativity behavior in synthetic gene modules competing for resources. In addition, growth feedback attenuated the winner-takes-all rules on resource competition between the two self-activating modules. These results demonstrate that growth feedback plays an important role in the host-circuit system’s molecular dynamics. Characterizing general principles from the effect of growth facilitates the ability to minimize or even harness unexpected gene expression behaviors derived from the effect of growth feedback.
ContributorsMelendez-Alvarez, Juan Ramon (Author) / Tian, Xiaojun (Thesis advisor) / Wang, Xiao (Committee member) / Kuang, Yang (Committee member) / Arizona State University (Publisher)
Created2022
Description
Studying the so-called ”hidden” phases of quantum materials—phases that do not exist under equilibrium conditions, but can be accessed with light—reveals new insights into the broader field of structural phase transitions. Using terahertz irradiation as well as hard x-ray probes made available by x-ray free electron lasers (XFELs) provides unique capabilities to study phonon dispersion in these materials. Here, we study the cubic peak of the quantum paraelectric strontium titanate (SrTiO3, STO) below the 110 K cubic-to-tetragonal tran- sition. Our results reveal a temperature and field strength dependence of the transverse acoustic mode in agreement with previous work on the avoided crossing occurring at finite wavevector, as well as evidence of anharmonic coupling between transverse optical phonons and a fully symmetric A1g phonon. These results elucidate previous optical studies on STO and hold promise for future studies on the hidden metastable phases of quantum materials.
ContributorsStanton, Jade (Author) / Teitelbaum, Samuel (Thesis director) / Smith, David (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Physics (Contributor)
Created2023-05
Description
The representation of a patient’s characteristics as the parameters of a model is a key component in many studies of personalized medicine, where the underlying mathematical models are used to describe, explain, and forecast the course of treatment. In this context, clinical observations form the bridge between the mathematical frameworks and applications. However, the formulation and theoretical studies of the models and the clinical studies are often not completely compatible, which is one of the main obstacles in the application of mathematical models in practice. The goal of my study is to extend a mathematical framework to model prostate cancer based mainly on the concept of cell-quota within an evolutionary framework and to study the relevant aspects for the model to gain useful insights in practice. Specifically, the first aim is to construct a mathematical model that can explain and predict the observed clinical data under various treatment combinations. The second aim is to find a fundamental model structure that can capture the dynamics of cancer progression within a realistic set of data. Finally, relevant clinical aspects such as how the patient's parameters change over the course of treatment and how to incorporate treatment optimization within a framework of uncertainty quantification, will be examined to construct a useful framework in practice.
ContributorsPhan, Tin (Author) / Kuang, Yang (Thesis advisor) / Kostelich, Eric J (Committee member) / Crook, Sharon (Committee member) / Maley, Carlo (Committee member) / Bryce, Alan (Committee member) / Arizona State University (Publisher)
Created2021
Description
Synthetic biology (SB) has become an important field of science focusing on designing and engineering new biological parts and systems, or re-designing existing biological systems for useful purposes. The dramatic growth of SB throughout the past two decades has not only provided us numerous achievements, but also brought us more timely and underexplored problems. In SB's entire history, mathematical modeling has always been an indispensable approach to predict the experimental outcomes, improve experimental design and obtain mechanism-understanding of the biological systems. \textit{Escherichia coli} (\textit{E. coli}) is one of the most important experimental platforms, its growth dynamics is the major research objective in this dissertation. Chapter 2 employs a reaction-diffusion model to predict the \textit{E. coli} colony growth on a semi-solid agar plate under multiple controls. In that chapter, a density-dependent diffusion model with non-monotonic growth to capture the colony's non-linear growth profile is introduced. Findings of the new model to experimental data are compared and contrasted with those from other proposed models. In addition, the cross-sectional profile of the colony are computed and compared with experimental data. \textit{E. coli} colony is also used to perform spatial patterns driven by designed gene circuits. In Chapter 3, a gene circuit (MINPAC) and its corresponding pattern formation results are presented. Specifically, a series of partial differential equation (PDE) models are developed to describe the pattern formation driven by the MINPAC circuit. Model simulations of the patterns based on different experimental conditions and numerical analysis of the models to obtain a deeper understanding of the mechanisms are performed and discussed. Mathematical analysis of the simplified models, including traveling wave analysis and local stability analysis, is also presented and used to explore the control strategies of the pattern formation. The interaction between the gene circuit and the host \textit{E. coli} may be crucial and even greatly affect the experimental outcomes. Chapter 4 focuses on the growth feedback between the circuit and the host cell under different nutrient conditions. Two ordinary differential equation (ODE) models are developed to describe such feedback with nutrient variation. Preliminary results on data fitting using both two models and the model dynamical analysis are included.
ContributorsHe, Changhan (Author) / Kuang, Yang (Thesis advisor) / Wang, Xiao (Committee member) / Kostelich, Eric (Committee member) / Tian, Xiaojun (Committee member) / Gumel, Abba (Committee member) / Arizona State University (Publisher)
Created2021
Description
In this dissertation, the surface interactions of fluorine were studied during atomic layer deposition (ALD) and atomic layer etching (ALE) of wide band gap materials. To enable this research two high vacuum reactors were designed and constructed for thermal and plasma enhanced ALD and ALE, and they were equipped for in-situ process monitoring. Fluorine surface interactions were first studied in a comparison of thermal and plasma enhanced ALD (TALD and PEALD) of AlF3 thin films prepared using hydrogen fluoride (HF), trimethylaluminum (TMA), and H2-plasma. The ALD AlF3 films were compared ¬in-situ using ellipsometry and X-ray photoelectron spectroscopy (XPS). Ellipsometry showed a growth rate of 1.1 Å/ cycle and 0.7 Å/ cycle, at 100°C, for the TALD and PEALD AlF3 processes, respectively. XPS indicated the presence of Al-rich clusters within the PEALD film. The formation of the Al-rich clusters is thought to originate during the H2-plasma step of the PEALD process. The Al-rich clusters were not detected in the TALD AlF3 films. This study provided valuable insight on the role of fluorine in an ALD process.
Reactive ion etching is a common dry chemical etch process for fabricating GaN devices. However, the use of ions can induce various defects, which can degrade device performance. The development of low-damage post etch processes are essential for mitigating plasma induced damage. As such, two multistep ALE methods were implemented for GaN based on oxidation, fluorination, and ligand exchange. First, GaN surfaces were oxidized using either water vapor or O2-plasma exposures to produce a thin oxide layer. The oxide layer was addressed using alternating exposures of HF and TMG, which etch Ga2O3 films. Each ALE process was characterized using in-situ using ellipsometry and XPS and ex-situ transmission electron microscopy (TEM). XPS indicated F and O impurities remained on the etched surfaces. Ellipsometry and TEM showed a slight reduction in thickness. The very low ALE rate was interpreted as the inability of the Ga2O3 ALE process to fluorinate the ordered surface oxide on GaN (0001).
Overall, these results indicate HF is effective for the ALD of metal fluorides and the ALE of metal oxides.
ContributorsMessina, Daniel C (Author) / Nemanich, Robert J (Thesis advisor) / Goodnick, Stephen (Committee member) / Ponce, Fernando A (Committee member) / Smith, David (Committee member) / Arizona State University (Publisher)
Created2021
Description
Since the 20th century, Arizona has undergone shifts in agricultural practices, driven by urban expansion and crop irrigation regulations. These changes present environmental challenges, altering atmospheric processes and influencing climate dynamics. Given the potential threats of climate change and drought on water availability for agriculture, further modifications in the agricultural landscape are expected. To understand these land use changes and their impact on carbon dynamics, our study quantified aboveground carbon storage in both cultivated and
abandoned agricultural fields. To accomplish this, we employed Python and various geospatial libraries in Jupyter Notebook files, for thorough dataset assembly and visual, quantitative analysis. We focused on nine counties known for high cultivation levels, primarily located in the lower latitudes of Arizona. Our analysis investigated carbon dynamics across not only abandoned and actively cultivated croplands but also neighboring uncultivated land, for which we estimated the extent. Additionally, we compared these trends with those observed in developed land areas.
The findings revealed a hierarchy in aboveground carbon storage, with currently cultivated lands having the lowest levels, followed by abandoned croplands and uncultivated wilderness. However, wilderness areas exhibited significant variation in carbon storage by county compared to cultivated and abandoned lands. Developed lands ranked highest in aboveground carbon storage, with the median value being the highest. Despite county-wide variations, abandoned croplands generally contained more carbon than currently cultivated areas, with adjacent wilderness lands containing even more than both. This trend suggests that cultivating croplands in the region reduces aboveground carbon stores, while abandonment allows for some replenishment, though only to a limited extent. Enhancing carbon stores in Arizona can be achieved through active restoration efforts on abandoned cropland. By promoting native plant regeneration and boosting aboveground carbon levels, these measures are crucial for improving carbon sequestration. We strongly advocate for implementing this step to facilitate the regrowth of native plants and enhance overall carbon storage in the region.
ContributorsGoodwin, Emily (Author) / Eikenberry, Steffen (Thesis director) / Kuang, Yang (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2024-05
Description
Glioblastoma Multiforme is a prevalent and aggressive brain tumor. It has an average 5-year survival rate of 6% and average survival time of 14 months. Using patient-specific MRI data from the Barrow Neurological Institute, this thesis investigates the impact of parameter manipulation on reaction-diffusion models for predicting and simulating glioblastoma growth. The study aims to explore key factors influencing tumor morphology and to contribute to enhancing prediction techniques for treatment.
ContributorsShayegan, Tara (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Barrett, The Honors College (Contributor) / School of Human Evolution & Social Change (Contributor)
Created2024-05