Matching Items (81)
Description
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19) that emerged from a zoonotic host at the end of 2019 and caused a public health crisis. In this collection of studies, Nicotiana benthamiana plants are used to set the foundation for producing monoclonal antibodies (mAbs) with homogeneous glycosylation to neutralize SARS-CoV-2 and potentially address the immunopathology often observed with severe COVID-19. Specifically, a mAb against the human interleukin (IL)-6 receptor (sarilumab) was generated and evaluated in vitro for its potential to reduce IL-6 signaling that has been shown to be associated with more severe cases of COVID-19. Furthermore, multiple mAbs that bind to the receptor-binding domain (RBD) of SARS-CoV-2 and efficiently neutralize the virus were developed using plant-based expression. Several of these mAbs are from different classes of RBD-binding mAbs that have distinct binding sites from one another. Several mAbs from different classes showed synergy in neutralizing the ancestral strain of SARS-CoV-2 and a smaller subset showed synergy when tested against the highly mutated Omicron (B.1.1.529) variant. Of interest, a novel RBD-binding mAb, termed 11D7, that was raised against the ancestral strain and derived from a hybridoma, appears to have an epitope on the RBD that contributes more synergy to a mAb combination that efficiently neutralizes the B.1.1.529 variant of SARS-CoV-2. This epitope was partially mapped by competitive binding and shows that it overlaps with another known antibody that binds a cryptic, distal epitope, away from the receptor binding site, giving insight into the potential mechanism by which 11D7 neutralizes SARS-CoV-2, as well as potentially allowing it to resist SARS-CoV-2 immune evasion more efficiently. Furthermore, this mAb carries a highly homogeneous glycan pattern when expressed in N. benthamiana, that may contribute to enhanced effector function and provides a tool to elucidate the precise role of crystallizable fragment (Fc)-mediated protection in SARS-CoV-2 infection. Ultimately, these studies provide evidence of the utility of plant-made mAbs to be used as cocktail members, giving clarity to the use of less potent mAbs as valuable cocktail components which will spur further investigations into how mAbs with unique epitopes work together to efficiently neutralize SARS-CoV-2.
ContributorsJugler, Collin (Author) / Chen, Qiang (Thesis advisor) / Lake, Douglas (Committee member) / Steele, Kelly (Committee member) / Mason, Hugh (Committee member) / Arizona State University (Publisher)
Created2022
Description
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
Scientists are entrusted with developing novel molecular strategies for effective prophylactic and therapeutic interventions. Antivirals are indispensable tools that can be targeted at viral domains directly or at cellular domains indirectly to obstruct viral infections and reduce pathogenicity. Despite their transformative potential in healthcare, to date, antivirals have been clinically approved to treat only 10 out of the greater than 200 known pathogenic human viruses. Additionally, as obligate intracellular parasites, many virus functions are intimately coupled with host cellular processes. As such, the development of a clinically relevant antiviral is challenged by the limited number of clear targets per virus and necessitates an extensive insight into these molecular processes. Compounding this challenge, many viral pathogens have evolved to evade effective antivirals. Therefore, a means to develop virus- or strain-specific antivirals without detailed insight into each idiosyncratic biochemical mechanism may aid in the development of antivirals against a larger swath of pathogens. Such an approach will tremendously benefit from having the specific molecular recognition of viral species as the lowest barrier. Here, I modify a nanobody (anti-green fluorescent protein) that specifically recognizes non-essential epitopes (glycoprotein M-pHluorin chimera) presented on the extra virion surface of a virus (Pseudorabies virus strain 486). The nanobody switches from having no inhibitory properties (tested up to 50 μM) to ∼3 nM IC50 in in vitro infectivity assays using porcine kidney (PK15) cells. The nanobody modifications use highly reliable bioconjugation to a three-dimensional wireframe deoxyribonucleic acid (DNA) origami scaffold. Mechanistic studies suggest that inhibition is mediated by the DNA origami scaffold bound to the virus particle, which obstructs the internalization of the viruses into cells, and that inhibition is enhanced by avidity resulting from multivalent virus and scaffold interactions. The assembled nanostructures demonstrate negligible cytotoxicity (<10 nM) and sufficient stability, further supporting their therapeutic potential. If translatable to other viral species and epitopes, this approach may open a new strategy that leverages existing infrastructures – monoclonal antibody development, phage display, and in vitro evolution - for rapidly developing novel antivirals in vivo.
ContributorsPradhan, Swechchha (Author) / Hariadi, Rizal (Thesis advisor) / Hogue, Ian (Committee member) / Varsani, Arvind (Committee member) / Chen, Qiang (Committee member) / Arizona State University (Publisher)
Created2022
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
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
Heart disease is the leading cause of death in the developed world and often occurs following myocardial infarction. Apelin is an endogenous prepropeptide that has been studied for its role in improving cardiac contractility and vasodilation but suffers from a short half-life in the body. By encasing apelin in a nanoparticle patch, we were able to slowly release apelin to cardiac tissue and observe its effects for one month following induced myocardial infarction surgery in mice. This study demonstrates that the apelin nanoparticles can protect the heart from myocardial-induced heart failure, observing overall improved cardiac function and reduction of fibrotic scarring associated with post-myocardial infarction compared to a nontreated group.
ContributorsHenderson, Adam (Author) / Chen, Qiang (Thesis director) / Zhu, Wuqiang (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor)
Created2022-05