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Climate change is one of the most pressing issues affecting the world today. One of the impacts of climate change is on the transmission of mosquito-borne diseases (MBDs), such as West Nile Virus (WNV). Climate is known to influence vector and host demography as well as MBD transmission. This dissertation addresses the questions of how vector and host demography impact WNV dynamics, and how expected and likely climate change scenarios will affect demographic and epidemiological processes of WNV transmission. First, a data fusion method is developed that connects non-autonomous logistic model parameters to mosquito time series data. This method captures the inter-annual and intra-seasonal variation of mosquito populations within a geographical location. Next, a three-population WNV model between mosquito vectors, bird hosts, and human hosts with infection-age structure for the vector and bird host populations is introduced. A sensitivity analysis uncovers which parameters have the most influence on WNV outbreaks. Finally, the WNV model is extended to include the non-autonomous population model and temperature-dependent processes. Model parameterization using historical temperature and human WNV case data from the Greater Toronto Area (GTA) is conducted. Parameter fitting results are then used to analyze possible future WNV dynamics under two climate change scenarios. These results suggest that WNV risk for the GTA will substantially increase as temperature increases from climate change, even under the most conservative assumptions. This demonstrates the importance of ensuring that the warming of the planet is limited as much as possible.
ContributorsMancuso, Marina (Author) / Milner, Fabio A (Thesis advisor) / Kuang, Yang (Committee member) / Kostelich, Eric (Committee member) / Eikenberry, Steffen (Committee member) / Manore, Carrie (Committee member) / Arizona State University (Publisher)
Created2023
Description
Over the past 20 years, the fields of synthetic biology and synthetic biosystems engineering have grown into mature disciplines, leading to significant breakthroughs in cancer research, diagnostics, cell-based medicines, biochemical production, etc. Application of mathematical modelling to biological and biochemical systems have not only given great insight into how these systems function, but also have lent enough predictive power to aid in the forward-engineering of synthetic constructs. However, progress has been impeded by several modes of context-dependence unique to biological and biochemical systems that are not seen in traditional engineering disciplines, resulting in the need for lengthy design-build-test cycles before functional prototypes are generated.In this work, two of these universal modes of context dependence – resource competition and growth feedback –their effects on synthetic gene circuits and potential control mechanisms, are studied and characterized. Results demonstrate that a novel competitive control architecture can be utilized to mitigate the effects of winner-take-all resource competition (a form of context dependence where distinct gene modules influence each other by competing over a shared pool of transcriptional/translational resources) in synthetic gene circuits and restore circuits to their intended function. Application of the fluctuation-dissipation theorem and rigorous stochastic simulations demonstrate that realistic resource constraints present in cells at the transcriptional and translational levels influence noise in gene circuits in a nonmonotonic fashion, either increasing or decreasing noise depending on the transcriptional/translational capacity.
Growth feedback on the other hand links circuit function to cellular growth rate via increased protein dilution rate during exponential growth phase. This in turn can result in the collapse of bistable gene circuits as the accelerated dilution rate forces switches in a high stable state to fall to a low stable state. Mathematical modelling and experimental data demonstrate that application of repressive links can insulate sensitive parts of gene circuits against growth-fluctuations and can in turn increase the robustness of multistable circuits in growth contexts.
The results presented in this work aid in the accumulation of understanding of biological and biochemical context dependence, and corresponding control strategies and design principles engineers can utilize to mitigate these effects.
ContributorsStone, Austin (Author) / Tian, Xiao-jun (Thesis advisor) / Wang, Xiao (Committee member) / Smith, Barbara (Committee member) / Kuang, Yang (Committee member) / Cheng, Albert (Committee member) / Arizona State University (Publisher)
Created2023
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
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
Annually, approximately 1.7 million people suffer a traumatic brain injury (TBI) in the United States. After initial insult, a TBI persists as a series of molecular and cellular events that lead to cognitive and motor deficits which have no treatment. In addition, the injured brain activates the regenerative niches of the adult brain presumably to reduce damage. The subventricular zone (SVZ) niche contains neural progenitor cells (NPCs) that generate astrocytes, oligodendrocyte, and neuroblasts. Following TBI, the injury microenvironment secretes signaling molecules like stromal cell derived factor-1a (SDF-1a). SDF-1a gradients from the injury contribute to the redirection of neuroblasts from the SVZ towards the lesion which may differentiate into neurons and integrate into existing circuitry. This repair mechanism is transient and does not lead to complete recovery of damaged tissue. Further, the mechanism by which SDF-1a gradients reach SVZ cells is not fully understood. To prolong NPC recruitment to the injured brain, exogenous SDF-1a delivery strategies have been employed. Increases in cell recruitment following stroke, spinal cord injury, and TBI have been demonstrated following SDF-1a delivery. Exogenous delivery of SDF-1a is limited by its 28-minute half-life and clearance from the injury microenvironment. Biomaterials-based delivery improves stability of molecules like SDF-1a and offer control of its release.
This dissertation investigates SDF-1a delivery strategies for neural regeneration in three ways: 1) elucidating the mechanisms of spatiotemporal SDF-1a signaling across the brain, 2) developing a tunable biomaterials system for SDF-1a delivery to the brain, 3) investigating SDF-1a delivery on SVZ-derived cell migration following TBI. Using in vitro, in vivo, and in silico analyses, autocrine/paracrine signaling was necessary to produce SDF-1a gradients in the brain. Native cell types engaged in autocrine/paracrine signaling. A microfluidics device generated injectable hyaluronic-based microgels that released SDF-1a peptide via enzymatic cleavage. Microgels (±SDF-1a peptide) were injected 7 days post-TBI in a mouse model and evaluated for NPC migration 7 days later using immunohistochemistry. Initial staining suggested complex presence of astrocytes, NPCs, and neuroblasts throughout the frontoparietal cortex.
Advancement of chemokine delivery was demonstrated by uncovering endogenous chemokine propagation in the brain, generating new approaches to maximize chemokine-based neural regeneration.
ContributorsHickey, Kassondra (Author) / Stabenfeldt, Sarah E (Thesis advisor) / Holloway, Julianne (Committee member) / Caplan, Michael (Committee member) / Brafman, David (Committee member) / Newbern, Jason (Committee member) / Arizona State University (Publisher)
Created2021
Description
The consumption of food, energy, and water (FEW) resources in U.S. households is very carbon-intensive. However, these negative climate change impacts are often invisible due to insufficient awareness and knowledge. Serious games (SGs) can potentially address this issue through an experiential and rigorous approach to simulate household actions and impacts in a playful but realistic setting. This dissertation focuses on: (a) the design and testing of an SG called HomeRUN (Role-play for Understanding Nexus); (b) the effectiveness of gameplay in advancing player knowledge about the upfront costs, financial returns, and greenhouse gas (GHG) emissions of various household decisions; and (c) the effectiveness of intervention messages in increasing FEW conservation to reduce household GHG emissions. The results of gameplay sessions played by 150 university students show that HomeRUN is fun to play, creates a flow experience, and results in experiential learning. The majority of players agreed that the game experience will continue over time to influence their future consumption behaviors to conserve FEW resources. Female players tended to gain more knowledge about financial aspects of interventions, whereas male players were more likely to increase their understandings of GHG emissions and resource consumption after playing HomeRUN. Social comparison intervention messages about energy and food consumption led to the highest reductions in household carbon emissions. The messages associated with each FEW resource tended to be most likely to lead to FEW conservation actions with the game that most closely corresponded to the particular FEW resource addressed in the message. This dissertation advances understandings about the design and use of SGs to foster learning and promote sustainable household FEW consumption.
ContributorsHanif, Muhammad Adnan (Author) / Agusdinata, Datu Buyung (Thesis advisor) / Halvorsen, Kathleen (Committee member) / Janssen, Marco (Committee member) / Arizona State University (Publisher)
Created2021
Description
The RNA editing enzyme adenosine deaminase acting on double stranded RNA 2 (ADAR2) converts adenosine into inosine in regions of double stranded RNA. Here, it was discovered that this critical function of ADAR2 was dysfunctional in amyotrophic lateral sclerosis (ALS) mediated by the C9orf72 hexanucleotide repeat expansion, the most common genetic abnormality associated with ALS. Typically a nuclear protein, ADAR2 was localized in cytoplasmic accumulations in postmortem tissue from C9orf72 ALS patients. The mislocalization of ADAR2 was confirmed using immunostaining in a C9orf72 mouse model and motor neurons differentiated from C9orf72 patient induced pluripotent stem cells. Notably, the cytoplasmic accumulation of ADAR2 coexisted in neurons with cytoplasmic accumulations of TAR DNA binding protein 43 (TDP-43). Interestingly, ADAR2 overexpression in mammalian cell lines induced nuclear depletion and cytoplasmic accumulation of TDP-43, reflective of the pathology observed in ALS patients. The mislocalization of TDP-43 was dependent on the catalytic activity of ADAR2 and the ability of TDP-43 to bind directly to inosine containing RNA. In addition, TDP-43 nuclear export was significantly elevated in cells with increased RNA editing. Together these results describe a novel cellular mechanism by which alterations in RNA editing drive the nuclear export of TDP-43 leading to its cytoplasmic mislocalization. Considering the contribution of cytoplasmic TDP-43 to the pathogenesis of ALS, these findings represent a novel understanding of how the formation of pathogenic cytoplasmic TDP-43 accumulations may be initiated. Further research exploring this mechanism will provide insights into opportunities for novel therapeutic interventions.
ContributorsMoore, Stephen Philip (Author) / Sattler, Rita (Thesis advisor) / Zarnescu, Daniela (Committee member) / Brafman, David (Committee member) / Van Keuren-Jensen, Kendall (Committee member) / Mangone, Marco (Committee member) / Arizona State University (Publisher)
Created2021
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