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Description
Dielectrophoresis has been shown in the recent past to successfully separate bioparticles of very subtle differences at high resolutions using biophysical forces. In this study, we test the biophysical differences of methicillin resistant and susceptible Staph. aureus that are known to have very similar genomes by using a modified gradient insulator-based dielectrophoresis device (g-iDEP). MRSA is commonly seen in hospitals and is the leading killer of infectious bacteria, claiming the lives of around 10,000 people annually. G-iDEP improves many capabilities within the DEP field including sample size, cost, ease of use and analysis time. This is a promising foundation to creating a more clinically optimized diagnostic tool for both separation and detection of bacteria in the healthcare field. The capture on-set potential for fluorescently tagged MRSA (801 ± 34V) is higher than fluorescently tagged MSSA (610 ± 32V), resulting in a higher electrokinetic to dielectrophoretic mobility ratio for MRSA. Since the strains have proven to be genomically similar through sequencing, it is reasonable to attribute this significant biophysical difference to the added PBP2a enzyme in MRSA. These results are consistent with other bacterial studied within in this device and have proven to be reproducible.
ContributorsSmithers, Jared (Author) / Hayes, Mark (Thesis director) / Woodbury, Neal (Committee member) / School of Criminology and Criminal Justice (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Diabesity is a global epidemic affecting millions worldwide. Diabesity is the term given to the link between obesity and Type II diabetes. It is estimated that ~90% of patients diagnosed with Type II diabetes are overweight or have struggled with excess body fat in the past. Type II diabetes is characterized by insulin resistance which is an impaired response of the body to insulin that leads to high blood glucose levels. Adipose tissue, previously thought of as an inert tissue, is now recognized as a major endocrine organ with an important role in the body's immune response and the development of chronic inflammation. It is speculated that adipose tissue inflammation is a major contributor to insulin resistance particular to Type II diabetes. This literature review explores the popular therapeutic targets and marketed drugs for the treatment of Type II diabetes and their role in decreasing adipose tissue inflammation. rAGE is currently in pre-clinical studies as a possible target to combat adipose tissue inflammation due to its relation to insulin resistance. Metformin and Pioglitazone are two drugs already being marketed that use unique chemical pathways to increase the production of insulin and/or decrease blood glucose levels. Sulfonylureas is one of the first FDA approved drugs used in the treatment of Type II diabetes, however, it has been discredited due to its life-threatening side effects. Bariatric surgery is a form of invasive surgery to rid the body of excess fat and has shown to normalize blood glucose levels. These treatments are all secondary to lifestyle changes, such as diet and exercise which can help halt the progression of Type II diabetes patients.
ContributorsRobles, Alondra Maria (Author) / Woodbury, Neal (Thesis director) / Redding, Kevin (Committee member) / Allen, James (Committee member) / Hendrickson, Kirstin (Committee member) / Sanford School of Social and Family Dynamics (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Recently, we have demonstrated that a novel RNA origami (RNA-OG) nanostructure functions as a TLR3 agonist both in vitro and in vivo. This RNA nanostructure could induce effective antitumor immunity in a CT26-OVA-iRFP tumor model that expresses both ovalbumin (OVA) and near infrared protein (iRFP), rendering a significant delay in tumor growth or complete tumor-regression. However, in a similar tumor line that expresses iRFP but not OVA, i.e. a CT26-Neo-iRFP model, RNA-OG induced responses that were consistently inferior to those observed in CT26-OVA-iRFP. Interestingly, the antitumor immunity initially generated against CT26-OVA-iRFP was found to render the mice immune to a challenge with the more malignant CT26-Neo-iRFP line. In addition to OVA expression, the two cell lines also showed different levels of MHC-I. Ongoing research has been focused on deciphering the molecular nature of the different responses. Then, we can search for strategies that increase the tumor immunogenicity, and therefore improve the therapeutic efficacy of RNA-OG for inducing long-term tumor-regression.
ContributorsMatiski, Lawrence Theodore Mazzei (Author) / Chang, Yung (Thesis director) / Yan, Hao (Committee member) / School of Molecular Sciences (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The Heliobacterial Reaction Center (HbRC) is the simplest Type I Reaction Center (RC) known today. However, upon illumination it has been found to produce menaquinol, and this has led to experiments investigating the function of this reduction scheme. The goal of the experiment was to investigate the mechanisms of menaquinol production through the use of Photosystem II (PSII) herbicides that are known to inhibit the QB quinone site in Type II RCs. Seven herbicides were chosen, and out of all of them terbuthylazine showed the greatest effect on the RC in isolated membranes when Transient Absorption Spectroscopy was used. In addition, terbuthylazine decreased menaquinone reduction to menaquinol by ~72%, slightly more than the reported effect of teburtryn (68%)1. In addition, terbuthylazine significantly impacted growth of whole cells under high light more than terbutryn.
ContributorsOdeh, Ahmad Osameh (Author) / Redding, Kevin (Thesis director) / Woodbury, Neal (Committee member) / Allen, James (Committee member) / School of Molecular Sciences (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Background
The transmission dynamics of Tuberculosis (TB) involve complex epidemiological and socio-economical interactions between individuals living in highly distinct regional conditions. The level of exogenous reinfection and first time infection rates within high-incidence settings may influence the impact of control programs on TB prevalence. The impact that effective population size and the distribution of individuals’ residence times in different patches have on TB transmission and control are studied using selected scenarios where risk is defined by the estimated or perceive first time infection and/or exogenous re-infection rates.
Methods
This study aims at enhancing the understanding of TB dynamics, within simplified, two patch, risk-defined environments, in the presence of short term mobility and variations in reinfection and infection rates via a mathematical model. The modeling framework captures the role of individuals’ ‘daily’ dynamics within and between places of residency, work or business via the average proportion of time spent in residence and as visitors to TB-risk environments (patches). As a result, the effective population size of Patch i (home of i-residents) at time t must account for visitors and residents of Patch i, at time t.
Results
The study identifies critical social behaviors mechanisms that can facilitate or eliminate TB infection in vulnerable populations. The results suggest that short-term mobility between heterogeneous patches contributes to significant overall increases in TB prevalence when risk is considered only in terms of direct new infection transmission, compared to the effect of exogenous reinfection. Although, the role of exogenous reinfection increases the risk that come from large movement of individuals, due to catastrophes or conflict, to TB-free areas.
Conclusions
The study highlights that allowing infected individuals to move from high to low TB prevalence areas (for example via the sharing of treatment and isolation facilities) may lead to a reduction in the total TB prevalence in the overall population. The higher the population size heterogeneity between distinct risk patches, the larger the benefit (low overall prevalence) under the same “traveling” patterns. Policies need to account for population specific factors (such as risks that are inherent with high levels of migration, local and regional mobility patterns, and first time infection rates) in order to be long lasting, effective and results in low number of drug resistant cases.
The transmission dynamics of Tuberculosis (TB) involve complex epidemiological and socio-economical interactions between individuals living in highly distinct regional conditions. The level of exogenous reinfection and first time infection rates within high-incidence settings may influence the impact of control programs on TB prevalence. The impact that effective population size and the distribution of individuals’ residence times in different patches have on TB transmission and control are studied using selected scenarios where risk is defined by the estimated or perceive first time infection and/or exogenous re-infection rates.
Methods
This study aims at enhancing the understanding of TB dynamics, within simplified, two patch, risk-defined environments, in the presence of short term mobility and variations in reinfection and infection rates via a mathematical model. The modeling framework captures the role of individuals’ ‘daily’ dynamics within and between places of residency, work or business via the average proportion of time spent in residence and as visitors to TB-risk environments (patches). As a result, the effective population size of Patch i (home of i-residents) at time t must account for visitors and residents of Patch i, at time t.
Results
The study identifies critical social behaviors mechanisms that can facilitate or eliminate TB infection in vulnerable populations. The results suggest that short-term mobility between heterogeneous patches contributes to significant overall increases in TB prevalence when risk is considered only in terms of direct new infection transmission, compared to the effect of exogenous reinfection. Although, the role of exogenous reinfection increases the risk that come from large movement of individuals, due to catastrophes or conflict, to TB-free areas.
Conclusions
The study highlights that allowing infected individuals to move from high to low TB prevalence areas (for example via the sharing of treatment and isolation facilities) may lead to a reduction in the total TB prevalence in the overall population. The higher the population size heterogeneity between distinct risk patches, the larger the benefit (low overall prevalence) under the same “traveling” patterns. Policies need to account for population specific factors (such as risks that are inherent with high levels of migration, local and regional mobility patterns, and first time infection rates) in order to be long lasting, effective and results in low number of drug resistant cases.
ContributorsMoreno Martinez, Victor (Author) / Espinoza Cortes, Baltazar (Author) / Barley, Kamal (Author) / Paredes Gutierrez, Marlio (Author) / Bichara, Derdei (Author) / Mubayi, Anuj (Author) / Castillo-Chavez, Carlos (Author) / Simon M. Levin Mathematical, Computational and Modeling Sciences Center (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Human Evolution and Social Change (Contributor)
Created2017-01-11
Description
Predicting the binding sites of proteins has historically relied on the determination of protein structural data. However, the ability to utilize binding data obtained from a simple assay and computationally make the same predictions using only sequence information would be more efficient, both in time and resources. The purpose of this study was to evaluate the effectiveness of an algorithm developed to predict regions of high-binding on proteins as it applies to determining the regions of interaction between binding partners. This approach was applied to tumor necrosis factor alpha (TNFα), its receptor TNFR2, programmed cell death protein-1 (PD-1), and one of its ligand PD-L1. The algorithms applied accurately predicted the binding region between TNFα and TNFR2 in which the interacting residues are sequential on TNFα, however failed to predict discontinuous regions of binding as accurately. The interface of PD-1 and PD-L1 contained continuous residues interacting with each other, however this region was predicted to bind weaker than the regions on the external portions of the molecules. Limitations of this approach include use of a linear search window (resulting in inability to predict discontinuous binding residues), and the use of proteins with unnaturally exposed regions, in the case of PD-1 and PD-L1 (resulting in observed interactions which would not occur normally). However, this method was overall very effective in utilizing the available information to make accurate predictions. The use of the microarray to obtain binding information and a computer algorithm to analyze is a versatile tool capable of being adapted to refine accuracy.
ContributorsBrooks, Meilia Catherine (Author) / Woodbury, Neal (Thesis director) / Diehnelt, Chris (Committee member) / Ghirlanda, Giovanna (Committee member) / Department of Psychology (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The free-base tetra-tolyl-porphyrin and the corresponding cobalt and iron porphyrin complexes were synthesized and characterized to show that this class of compound can be promising, tunable catalysts for carbon dioxide reduction. During cyclic voltammetry experiments, the iron porphyrin showed an on-set of ‘catalytic current’ at an earlier potential than the cobalt porphyrin’s in organic solutions gassed with carbon dioxide. The cobalt porphyrin yielded larger catalytic currents, but at the same potential as the electrode. This difference, along with the significant changes in the porphyrin’s electronic, optical and redox properties, showed that its capabilities for carbon dioxide reduction can be controlled by metal ions, allotting it unique opportunities for applications in solar fuels catalysis and photochemical reactions.
ContributorsSkibo, Edward Kim (Author) / Moore, Gary (Thesis director) / Woodbury, Neal (Committee member) / School of Molecular Sciences (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
ABSTRACT Peptide microarrays may prove to be a powerful tool for proteomics research and clinical diagnosis applications. Fodor et al. and Maurer et al. have shown proof-of-concept methods of light- and electrochemically-directed peptide microarray fabrication on glass and semiconductor microchips respectively. In this work, peptide microarray fabrication based on the abovementioned techniques were optimized. In addition, MALDI mass spectrometry based peptide synthesis characterization on semiconductor microchips was developed and novel applications of a CombiMatrix (CBMX) platform for electrochemically controlled synthesis were explored. We have investigated performance of 2-(2-nitrophenyl)propoxycarbonyl (NPPOC) derivatives as photo-labile protecting group. Specifically, influence of substituents on 4 and 5 positions of phenyl ring of NPPOC group on the rate of photolysis and the yield of the amine was investigated. The results indicated that substituents capable of forming a π-network with the nitro group enhanced the rate of photolysis and yield. Once such properly substituted NPPOC groups were used, the rate of photolysis/yield depended on the nature of protected amino group indicating that a different chemical step during the photo-cleavage process became the rate limiting step. We also focused on electrochemically-directed parallel synthesis of high-density peptide microarrays using the CBMX technology referred to above which uses electrochemically generated acids to perform patterned chemistry. Several issues related to peptide synthesis on the CBMX platform were studied and optimized, with emphasis placed on the reactions of electro-generated acids during the deprotection step of peptide synthesis. We have developed a MALDI mass spectrometry based method to determine the chemical composition of microarray synthesis, directly on the feature. This method utilizes non-diffusional chemical cleavage from the surface, thereby making the chemical characterization of high-density microarray features simple, accurate, and amenable to high-throughput. CBMX Corp. has developed a microarray reader which is based on electro-chemical detection of redox chemical species. Several parameters of the instrument were studied and optimized and novel redox applications of peptide microarrays on CBMX platform were also investigated using the instrument. These include (i) a search of metal binding catalytic peptides to reduce overpotential associated with water oxidation reaction and (ii) an immobilization of peptide microarrays using electro-polymerized polypyrrole.
ContributorsKumar, Pallav (Author) / Woodbury, Neal (Thesis advisor) / Allen, James (Committee member) / Johnston, Stephen (Committee member) / Arizona State University (Publisher)
Created2013
Description
Membrane proteins act as sensors, gatekeepers and information carriers in the cell membranes. Functional engineering of these proteins is important for the development of molecular tools for biosensing, therapeutics and as components of artificial cells. However, using protein engineering to modify existing protein structures is challenging due to the limitations of structural changes and difficulty in folding polypeptides into defined protein structures. Recent studies have shown that nanoscale architectures created by DNA nanotechnology can be used to mimic various protein functions, including some membrane proteins. However, mimicking the highly sophisticated structural dynamics of membrane proteins by DNA nanostructures is still in its infancy, mainly due to lack of transmembrane DNA nanostructures that can mimic the dynamic behavior, ubiquitous to membrane proteins. Here, I demonstrate design of dynamic DNA nanostructures to mimic two important class of membrane proteins. First, I describe a DNA nanostructure that inserts through lipid membrane and dynamically reconfigures upon sensing a membrane-enclosed DNA or RNA target, thereby transducing biomolecular information across the lipid membrane similar to G-protein coupled receptors (GPCR’s). I use the non-destructive sensing property of our GPCR-mimetic nanodevice to sense cancer associated micro-RNA biomarkers inside exosomes without the need of RNA extraction and amplification. Second, I demonstrate a fully reversibly gated DNA nanopore that mimics the ligand mediated gating of ion channel proteins. The 20.4 X 20.4 nm-wide channel of the DNA nanopore allows timed delivery of folded proteins across synthetic and biological membranes. These studies represent early examples of dynamic DNA nanostructures in mimicking membrane protein functions. I envision that they will be used in synthetic biology to create artificial cells containing GPCR-like and ion channel-like receptors, in site-specific drug or vaccine delivery and highly sensitive biosensing applications.
ContributorsDey, Swarup (Author) / Yan, Hao (Thesis advisor) / Hariadi, Rizal F (Thesis advisor) / Liu, Yan (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2021
Description
Traditional Reinforcement Learning (RL) assumes to learn policies with respect to reward available from the environment but sometimes learning in a complex domain requires wisdom which comes from a wide range of experience. In behavior based robotics, it is observed that a complex behavior can be described by a combination of simpler behaviors. It is tempting to apply similar idea such that simpler behaviors can be combined in a meaningful way to tailor the complex combination. Such an approach would enable faster learning and modular design of behaviors. Complex behaviors can be combined with other behaviors to create even more advanced behaviors resulting in a rich set of possibilities. Similar to RL, combined behavior can keep evolving by interacting with the environment. The requirement of this method is to specify a reasonable set of simple behaviors. In this research, I present an algorithm that aims at combining behavior such that the resulting behavior has characteristics of each individual behavior. This approach has been inspired by behavior based robotics, such as the subsumption architecture and motor schema-based design. The combination algorithm outputs n weights to combine behaviors linearly. The weights are state dependent and change dynamically at every step in an episode. This idea is tested on discrete and continuous environments like OpenAI’s “Lunar Lander” and “Biped Walker”. Results are compared with related domains like Multi-objective RL, Hierarchical RL, Transfer learning, and basic RL. It is observed that the combination of behaviors is a novel way of learning which helps the agent achieve required characteristics. A combination is learned for a given state and so the agent is able to learn faster in an efficient manner compared to other similar approaches. Agent beautifully demonstrates characteristics of multiple behaviors which helps the agent to learn and adapt to the environment. Future directions are also suggested as possible extensions to this research.
ContributorsVora, Kevin Jatin (Author) / Zhang, Yu (Thesis advisor) / Yang, Yezhou (Committee member) / Praharaj, Sarbeswar (Committee member) / Arizona State University (Publisher)
Created2021