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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
Cities in the Global South face rapid urbanization challenges and often suffer an acute lack of infrastructure and governance capacities. Smart Cities Mission, in India, launched in 2015, aims to offer a novel approach for urban renewal of 100 cities following an area‐based development approach, where the use of ICT and digital technologies is particularly emphasized. This article presents a critical review of the design and implementation framework of this new urban renewal program across selected case‐study cities. The article examines the claims of the so‐called “smart cities” against actual urban transformation on‐ground and evaluates how “inclusive” and “sustainable” these developments are. We quantify the scale and coverage of the smart city urban renewal projects in the cities to highlight who the program includes and excludes. The article also presents a statistical analysis of the sectoral focus and budgetary allocations of the projects under the Smart Cities Mission to find an inherent bias in these smart city initiatives in terms of which types of development they promote and the ones it ignores. The findings indicate that a predominant emphasis on digital urban renewal of selected precincts and enclaves, branded as “smart cities,” leads to deepening social polarization and gentrification. The article offers crucial urban planning lessons for designing ICT‐driven urban renewal projects, while addressing critical questions around inclusion and sustainability in smart city ventures.`
ContributorsPraharaj, Sarbeswar (Author)
Created2021-05-07
Description
Transient protein-protein and protein-molecule interactions fluctuate between associated and dissociated states. They are widespread in nature and mediate most biological processes. These interactions are complex and are strongly influenced by factors such as concentration, structure, and environment. Understanding and utilizing these types of interactions is useful from both a fundamental and design perspective. In this dissertation, transient protein interactions are used as the sensing element of a biosensor for small molecule detection. This is done by using a transcription factor-small molecule pair that mediates the activation of a CRISPR/Cas12a complex. Activation of the Cas12a enzyme results in an amplified readout mechanism that is either fluorescence or paper based. This biosensor can successfully detect 9 different small molecules including antibiotics with a tuneable detection limit ranging from low µM to low nM. By combining protein and nucleic acid-based systems, this biosensor has the potential to report on almost any protein-molecule interaction, linking this to the intrinsic amplification that is possible when working with nucleic acid-based technologies. The second part of this dissertation focuses on understanding protein-molecule interactions at a more fundamental level, and, in so doing, exploring design rules required to generalize sensors like the ones described above. This is done by training a neural network algorithm with binding data from high density peptide micro arrays incubated with specific protein targets. Because the peptide sequences were chosen simply to evenly, though sparsely, represent all sequence space, the resulting network provides a comprehensive sequence/binding relationship for a given target protein. While past work had shown that this works well on the arrays, here I have explored how well the neural networks thus trained, predict sequence-dependent binding in the context of protein-protein and peptide-protein interactions. Amino acid sequences, either free in solution or embedded in protein structure, will display somewhat different binding properties than sequences affixed to the surface of a high-density array. However, the neural network trained on array sequences was able to both identify binding regions in between proteins and predict surface plasmon resonance-based binding propensities for peptides with statistically significant levels of accuracy.
ContributorsSwingle, Kirstie Lynn (Author) / Woodbury, Neal W (Thesis advisor) / Green, Alexander A (Thesis advisor) / Stephanopoulos, Nicholas (Committee member) / Borges, Chad (Committee member) / Arizona State University (Publisher)
Created2022
Description
Attitudes and habits are extremely resistant to change, but a disruption of the magnitude of the COVID-19 pandemic has the potential to bring long-term, massive societal changes. During the pandemic, people are being compelled to experience new ways of interacting, working, learning, shopping, traveling, and eating meals. Going forward, a critical question is whether these experiences will result in changed behaviors and preferences in the long term. This paper presents initial findings on the likelihood of long-term changes in telework, daily travel, restaurant patronage, and air travel based on survey data collected from adults in the United States in Spring 2020. These data suggest that a sizable fraction of the increase in telework and decreases in both business air travel and restaurant patronage are likely here to stay. As for daily travel modes, public transit may not fully recover its pre-pandemic ridership levels, but many of our respondents are planning to bike and walk more than they used to. These data reflect the responses of a sample that is higher income and more highly educated than the US population. The response of these particular groups to the COVID-19 pandemic is perhaps especially important to understand, however, because their consumption patterns give them a large influence on many sectors of the economy.
ContributorsConway, Matthew Wigginton (Author) / Salon, Deborah (Author) / da Silva, Denise Capasso (Author) / Mirtich, Laura Christine (Author)
Created2020-09-03
Description
For cold chain tracking systems, precision and versatility across varying time intervals and temperature ranges remain integral to effective application in clinical, commercial, and academic settings. Therefore, while electronic and chemistry/physics based cold chain tracking mechanisms currently exist, both have limitations that affect their application across various biospecimens and commercial products, providing the initiative to develop a time temperature visual indicator system that resolves challenges with current cold chain tracking approaches. As a result, a permanganate/oxalic acid time temperature visual indicator system for cold chain tracking has been proposed. At thawing temperatures, the designed permanganate/oxalic acid reaction system undergoes a pink to colorless transition as permanganate, Mn(VII), is reduced to auto-catalytic Mn(II), while oxalate is oxidized to CO2. Therefore, when properly stored and vitrified or frozen, the proposed visual indicator remains pink, whereas exposure to thawing conditions will result in an eventual, time temperature dependent, designed color transition that characterizes compromised biospecimen integrity. To design visual indicator systems for targeted times at specific temperatures, absorbance spectroscopy was utilized to monitor permanganate kinetic curves by absorbance at 525 nm. As a result, throughout the outlined research, the following aims were demonstrated: (i) Design and functionality of 1x (0.5 mM KMnO4) visual indicator systems across various time intervals at temperatures ranging from 25°C to -20°C, (ii) Design and functionality of high concentration, 5x, visual indicator systems across varying targeted time intervals at temperatures ranging from 25°C to 0°C, (iii) Pre-activation stability and long-term stability of the proposed visual indicator systems.
ContributorsLjungberg, Emil (Author) / Borges, Chad (Thesis advisor) / Levitus, Marcia (Committee member) / Williams, Peter (Committee member) / Arizona State University (Publisher)
Created2024
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
Description
Insulator-based dielectrophoresis (iDEP) has attracted considerable attention due to its ability to precisely capture and manipulate nanoparticles and biomolecules. A distinctive approach for effective manipulation of nanometer-sized proteins employing iDEP technique by generating higher electric field (E) and gradient (??2) in the iDEP microfluidic devices is delineated. Strategies to generate higher ??2 in the iDEP devices were outlined using numerical simulations. Intriguingly, the numerical simulation results demonstrated that by decreasing the post-to-post gap in the iDEP microfluidic devices, the ??2 was increased by ⁓12 fold. Furthermore, the inclusion of channel constrictions, such as rectangular constriction or curved constriction into the straight channel iDEP microfluidic device led to a significant increase in ??2. In addition, the inclusion of rectangular constrictions in the straight channel iDEP microfluidic device resulted in a greater increase in ??2 compared to the incorporation of curved constrictions in the same device. Moreover, the straight channel device with horizontal post-to-post gap of 20 μm and vertical post-to-post gap of 10 μm generated the lowest ??2 and the ??2 was uniform across the device. The rectangular constriction device with horizontal and vertical post-to-post gap of 5 μm generated the highest ??2 and the ??2 was non-uniform across the device. Subsequently, suitable candidate devices were fabricated using soft lithography as well as high resolution 3D printing and the DEP behavior of ferritin examined under various experimental conditions. Positive streaming DEP could be observed for ferritin at low frequency in the device generating the lowest ??2, whereas at higher frequency of 10 kHz no DEP trapping characteristics were apparent in the same device. Importantly, in the device geometry resulting in the highest ??2 at 10 kHz, labeled ferritin exhibited pDEPtrapping characteristics. This is an indication that the DEP force superseded diffusion and became the dominant force.
ContributorsMAHMUD, SAMIRA (Author) / Ros, Alexandra (Thesis advisor) / Borges, Chad (Committee member) / Mills, Jeremy (Committee member) / Arizona State University (Publisher)
Created2024
Description
Background: Eosinophilic esophagitis (EoE) is an increasingly prevalent allergic disease characterized by eosinophilic inflammation and symptoms of esophageal dysfunction. Diagnosis and monitoring require repeated, invasive endoscopic esophageal biopsies to assess levels of eosinophilic inflammation. Recently, the minimally invasive esophageal string test (EST) has been used collect protein in mucosal secretions as a surrogate for tissue biopsies in monitoring disease activity. From the string, assessment of the eosinophil-associated proteins major basic protein-1 (MBP-1) and eotaxin-3 (Eot3) is used to assess disease activity; however, this requires measurement in a reference laboratory, for which the turnaround time for results exceeds the time required for histopathologic assessment of endoscopic biopsies. In addition, MBP-1 and Eot3 are not markers unique to eosinophils. These obstacles can be overcome by targeting eosinophil peroxidase (EPX), an eosinophil-specific protein, using a rapid point-of-care test. Currently, EPX is measured by a labor-intensive enzyme-linked immunosorbent assay (ELISA), but we sought to optimize a rapid point-of-care test to measure EPX in EST segments.
Methods: We extracted protein from residual EST segments and measured EPX levels by ELISA and a lateral flow assay (LFA).
Results: EPX levels measured by LFA strongly correlated with those quantified by ELISA
(rs = 0.90 {95% CI: 0.8283, 0.9466}). The EPX LFA is comparable to ELISA for measuring EPX levels in ESTs.
Conclusions: The EPX LFA can provide a way to rapidly test EPX levels in ESTs in clinical settings and may serve as a valuable tool to facilitate diagnosis and monitoring of EoE.
ContributorsDao, Adelyn (Author) / Lake, Douglas (Thesis director) / Borges, Chad (Committee member) / Wright, Benjamin (Committee member) / Barrett, The Honors College (Contributor) / School of Molecular Sciences (Contributor) / School of Life Sciences (Contributor)
Created2024-05
Description
Alzheimer’s Disease (AD) is the most common form of dementia affecting the population over the age of 65. AD is characterized clinically by increasing difficulty with memory and language, resulting in a loss of independence. This is due to the presence of two characteristic protein aggregates in the brain: extracellular amyloid plaques and intracellular neurofibrillary tangles (NFTs). Utilizing multiplexed immunofluorescence and dimensional reduction analysis the types of cells present in the hippocampus, the region of the brain most affected by AD, can be explored. Understanding the kinds of cell subtypes present, the mechanism behind how AD develops can be explored. Multiplexed IF was performed on human hippocampus FFPE tissues to detect a total of 37 proteins. Dimensional reduction analysis was performed to identify the four major cell types in the brain: neurons, oligodendrocytes, astrocytes, and microglia. After identifying each cell type, further dimensional reduction analysis was performed within each cell type to identify cell subtypes. A total of 21 neuron, 41 oligodendrocyte, 20 astrocyte, and 22 microglia subtypes were identified. The location of cell subtypes in each region of the hippocampal formation was found to match previous reports, further validating the findings of this project.
ContributorsEllison, Mischa A (Author) / Guo, Jia (Thesis advisor) / Borges, Chad (Committee member) / Mastroeni, Diego (Committee member) / Arizona State University (Publisher)
Created2024