Matching Items (19)
Filtering by
- All Subjects: Biomedical Engineering
- Genre: Doctoral Dissertation
- Creators: Rege, Kaushal
- Creators: Wang, Xiao
Description
Cancer is the second leading cause of death in the United States and novel methods of treating advanced malignancies are of high importance. Of these deaths, prostate cancer and breast cancer are the second most fatal carcinomas in men and women respectively, while pancreatic cancer is the fourth most fatal in both men and women. Developing new drugs for the treatment of cancer is both a slow and expensive process. It is estimated that it takes an average of 15 years and an expense of $800 million to bring a single new drug to the market. However, it is also estimated that nearly 40% of that cost could be avoided by finding alternative uses for drugs that have already been approved by the Food and Drug Administration (FDA). The research presented in this document describes the testing, identification, and mechanistic evaluation of novel methods for treating many human carcinomas using drugs previously approved by the FDA. A tissue culture plate-based screening of FDA approved drugs will identify compounds that can be used in combination with the protein TRAIL to induce apoptosis selectively in cancer cells. Identified leads will next be optimized using high-throughput microfluidic devices to determine the most effective treatment conditions. Finally, a rigorous mechanistic analysis will be conducted to understand how the FDA-approved drug mitoxantrone, sensitizes cancer cells to TRAIL-mediated apoptosis.
ContributorsTaylor, David (Author) / Rege, Kaushal (Thesis advisor) / Jayaraman, Arul (Committee member) / Nielsen, David (Committee member) / Kodibagkar, Vikram (Committee member) / Dai, Lenore (Committee member) / Arizona State University (Publisher)
Created2013
Description
Sensitivity is a fundamental challenge for in vivo molecular magnetic resonance imaging (MRI). Here, I improve the sensitivity of metal nanoparticle contrast agents by strategically incorporating pure and doped metal oxides in the nanoparticle core, forming a soluble, monodisperse, contrast agent with adjustable T2 or T1 relaxivity (r2 or r1). I first developed a simplified technique to incorporate iron oxides in apoferritin to form "magnetoferritin" for nM-level detection with T2- and T2* weighting. I then explored whether the crystal could be chemically modified to form a particle with high r1. I first adsorbed Mn2+ ions to metal binding sites in the apoferritin pores. The strategic placement of metal ions near sites of water exchange and within the crystal oxide enhance r1, suggesting a mechanism for increasing relaxivity in porous nanoparticle agents. However, the Mn2+ addition was only possible when the particle was simultaneously filled with an iron oxide, resulting in a particle with a high r1 but also a high r2 and making them undetectable with conventional T1-weighting techniques. To solve this problem and decrease the particle r2 for more sensitive detection, I chemically doped the nanoparticles with tungsten to form a disordered W-Fe oxide composite in the apoferritin core. This configuration formed a particle with a r1 of 4,870mM-1s-1 and r2 of 9,076mM-1s-1. These relaxivities allowed the detection of concentrations ranging from 20nM - 400nM in vivo, both passively injected and targeted to the kidney glomerulus. I further developed an MRI acquisition technique to distinguish particles based on r2/r1, and show that three nanoparticles of similar size can be distinguished in vitro and in vivo with MRI. This work forms the basis for a new, highly flexible inorganic approach to design nanoparticle contrast agents for molecular MRI.
ContributorsClavijo Jordan, Maria Veronica (Author) / Bennett, Kevin M (Thesis advisor) / Kodibagkar, Vikram (Committee member) / Sherry, A Dean (Committee member) / Wang, Xiao (Committee member) / Yarger, Jeffery (Committee member) / Arizona State University (Publisher)
Created2012
Description
Gold nanoparticles have emerged as promising nanomaterials for biosensing, imaging, photothermal treatment and therapeutic delivery for several diseases, including cancer. We have generated poly(amino ether)-functionalized gold nanorods (PAE-GNRs) using a layer-by-layer deposition approach. Sub-toxic concentrations of PAE-GNRs were employed to deliver plasmid DNA to prostate cancer cells in vitro. PAE-GNRs generated using 1,4C-1,4Bis, a cationic polymer from our laboratory demonstrated significantly higher transgene expression and exhibited lower cytotoxicities when compared to similar assemblies generated using 25 kDa poly(ethylene imine) (PEI25k-GNRs), a current standard for polymer-mediated gene delivery. Additionally, sub-toxic concentrations of 1,4C-1,4Bis-GNR nanoassemblies were employed to deliver expression vectors that express shRNA ('shRNA plasmid') against firefly luciferase gene in order to knock down expression of the protein constitutively expressed in prostate cancer cells. The roles of poly(amino ether) chemistry and zeta-potential in determining transgene expression efficacies of PAE-GNR assemblies were investigated. The theranostic potential of 1,4C-1,4Bis-GNR nanoassemblies was demonstrated using live cell two-photon induced luminescence bioimaging. The PAE class of polymers was also investigated for the one pot synthesis of both gold and silver nanoparticles using a small library poly(amino ethers) derived from linear-like polyamines. Efficient nanoparticle synthesis dependent on concentration of polymers as well as polymer chemical composition is demonstrated. Additionally, the application of poly(amino ether)-gold nanoparticles for transgene delivery is demonstrated in 22Rv1 and MB49 cancer cell lines. Base polymer, 1,4C-1,4Bis and 1,4C-1,4Bis templated and modified gold nanoparticles were compared for transgene delivery efficacies. Differences in morphology and physiochemical properties were investigated as they relate to differences in transgene delivery efficacy. There were found to be minimal differences suggestion that 1,4C-1,4Bis efficacy is not lost following use for nanoparticle modification. These results indicate that poly(amino ether)-gold nanoassemblies are a promising theranostic platform for delivery of therapeutic payloads capable of simultaneous gene silencing and bioimaging.
ContributorsRamos, James (Author) / Rege, Kaushal (Thesis advisor) / Kodibagkar, Vikram (Committee member) / Caplan, Michael (Committee member) / Vernon, Brent (Committee member) / Garcia, Antonio (Committee member) / Arizona State University (Publisher)
Created2014
Description
Biological membranes are critical to cell sustainability by selectively permeating polar molecules into the intracellular space and providing protection to the interior organelles. Biomimetic membranes (model cell membranes) are often used to fundamentally study the lipid bilayer backbone structure of the biological membrane. Lipid bilayer membranes are often supported using inorganic materials in an effort to improve membrane stability and for application to novel biosensing platforms. Published literature has shown that a variety of dense inorganic materials with various surface properties have been investigated for the study of biomimetic membranes. However, literature does not adequately address the effect of porous materials or supports with varying macroscopic geometries on lipid bilayer membrane behavior. The objective of this dissertation is to present a fundamental study on the synthesis of lipid bilayer membranes supported by novel inorganic supports in an effort to expand the number of available supports for biosensing technology. There are two fundamental areas covered including: (1) synthesis of lipid bilayer membranes on porous inorganic materials and (2) synthesis and characterization of cylindrically supported lipid bilayer membranes. The lipid bilayer membrane formation behavior on various porous supports was studied via direct mass adsorption using a quartz crystal microbalance. Experimental results demonstrate significantly different membrane formation behaviors on the porous inorganic supports. A lipid bilayer membrane structure was formed only on SiO2 based surfaces (dense SiO2 and silicalite, basic conditions) and gamma-alumina (acidic conditions). Vesicle monolayer adsorption was observed on gamma-alumina (basic conditions), and yttria stabilized zirconia (YSZ) of varying roughness. Parameters such as buffer pH, surface chemistry and surface roughness were found to have a significant impact on the vesicle adsorption kinetics. Experimental and modeling work was conducted to study formation and characterization of cylindrically supported lipid bilayer membranes. A novel sensing technique (long-period fiber grating refractometry) was utilized to measure the formation mechanism of lipid bilayer membranes on an optical fiber. It was found that the membrane formation kinetics on the fiber was similar to its planar SiO2 counterpart. Fluorescence measurements verified membrane transport behavior and found that characterization artifacts affected the measured transport behavior.
ContributorsEggen, Carrie (Author) / Lin, Jerry Y.S. (Thesis advisor) / Dai, Lenore (Committee member) / Rege, Kaushal (Committee member) / Thornton, Trevor (Committee member) / Vogt, Bryan (Committee member) / Arizona State University (Publisher)
Created2011
Description
Gold nanoparticles as potential diagnostic, therapeutic and sensing systems have a long history of use in medicine, and have expanded to a variety of applications. Gold nanoparticles are attractive in biological applications due to their unique optical, chemical and biological properties. Particularly, gold nanorods (GNRs) are increasingly used due to superior optical property in the near infrared (NIR) window. Light absorbed by the nanorod can be dissipated as heat efficiently or re-emitted by the particle. However, the limitations for clinical translation of gold nanorods include low yields, poor stability, depth-restricted imaging, and resistance of cancer cells to hyperthermia, are severe. A novel high-throughput synthesis method was employed to significantly increase in yields of solid and porous gold nanorods/wires. Stable functional nanoassemblies and nanomaterials were generated by interfacing gold nanorods with a variety of polymeric and polypeptide-based coatings, resulting in unique properties of polymer-gold nanorod assemblies and composites. Here the use of these modified gold nanorods in a variety of applications including optical sensors, cancer therapeutics, and nanobiomaterials were described.
ContributorsHuang, Huang-Chiao (Author) / Rege, Kaushal (Thesis advisor) / Sierks, Michael (Committee member) / Dai, Lenore (Committee member) / Ramakrishna, B (Committee member) / Vogt, Bryan (Committee member) / Arizona State University (Publisher)
Created2012
Description
While techniques for reading DNA in some capacity has been possible for decades,
the ability to accurately edit genomes at scale has remained elusive. Novel techniques
have been introduced recently to aid in the writing of DNA sequences. While writing
DNA is more accessible, it still remains expensive, justifying the increased interest in
in silico predictions of cell behavior. In order to accurately predict the behavior of
cells it is necessary to extensively model the cell environment, including gene-to-gene
interactions as completely as possible.
Significant algorithmic advances have been made for identifying these interactions,
but despite these improvements current techniques fail to infer some edges, and
fail to capture some complexities in the network. Much of this limitation is due to
heavily underdetermined problems, whereby tens of thousands of variables are to be
inferred using datasets with the power to resolve only a small fraction of the variables.
Additionally, failure to correctly resolve gene isoforms using short reads contributes
significantly to noise in gene quantification measures.
This dissertation introduces novel mathematical models, machine learning techniques,
and biological techniques to solve the problems described above. Mathematical
models are proposed for simulation of gene network motifs, and raw read simulation.
Machine learning techniques are shown for DNA sequence matching, and DNA
sequence correction.
Results provide novel insights into the low level functionality of gene networks. Also
shown is the ability to use normalization techniques to aggregate data for gene network
inference leading to larger data sets while minimizing increases in inter-experimental
noise. Results also demonstrate that high error rates experienced by third generation
sequencing are significantly different than previous error profiles, and that these errors can be modeled, simulated, and rectified. Finally, techniques are provided for amending this DNA error that preserve the benefits of third generation sequencing.
the ability to accurately edit genomes at scale has remained elusive. Novel techniques
have been introduced recently to aid in the writing of DNA sequences. While writing
DNA is more accessible, it still remains expensive, justifying the increased interest in
in silico predictions of cell behavior. In order to accurately predict the behavior of
cells it is necessary to extensively model the cell environment, including gene-to-gene
interactions as completely as possible.
Significant algorithmic advances have been made for identifying these interactions,
but despite these improvements current techniques fail to infer some edges, and
fail to capture some complexities in the network. Much of this limitation is due to
heavily underdetermined problems, whereby tens of thousands of variables are to be
inferred using datasets with the power to resolve only a small fraction of the variables.
Additionally, failure to correctly resolve gene isoforms using short reads contributes
significantly to noise in gene quantification measures.
This dissertation introduces novel mathematical models, machine learning techniques,
and biological techniques to solve the problems described above. Mathematical
models are proposed for simulation of gene network motifs, and raw read simulation.
Machine learning techniques are shown for DNA sequence matching, and DNA
sequence correction.
Results provide novel insights into the low level functionality of gene networks. Also
shown is the ability to use normalization techniques to aggregate data for gene network
inference leading to larger data sets while minimizing increases in inter-experimental
noise. Results also demonstrate that high error rates experienced by third generation
sequencing are significantly different than previous error profiles, and that these errors can be modeled, simulated, and rectified. Finally, techniques are provided for amending this DNA error that preserve the benefits of third generation sequencing.
ContributorsFaucon, Philippe Christophe (Author) / Liu, Huan (Thesis advisor) / Wang, Xiao (Committee member) / Crook, Sharon M (Committee member) / Wang, Yalin (Committee member) / Sarjoughian, Hessam S. (Committee member) / Arizona State University (Publisher)
Created2017
Description
Synthetic biology is an emerging field which melds genetics, molecular biology, network theory, and mathematical systems to understand, build, and predict gene network behavior. As an engineering discipline, developing a mathematical understanding of the genetic circuits being studied is of fundamental importance. In this dissertation, mathematical concepts for understanding, predicting, and controlling gene transcriptional networks are presented and applied to two synthetic gene network contexts. First, this engineering approach is used to improve the function of the guide ribonucleic acid (gRNA)-targeted, dCas9-regulated transcriptional cascades through analysis and targeted modification of the RNA transcript. In so doing, a fluorescent guide RNA (fgRNA) is developed to more clearly observe gRNA dynamics and aid design. It is shown that through careful optimization, RNA Polymerase II (Pol II) driven gRNA transcripts can be strong enough to exhibit measurable cascading behavior, previously only shown in RNA Polymerase III (Pol III) circuits. Second, inherent gene expression noise is used to achieve precise fractional differentiation of a population. Mathematical methods are employed to predict and understand the observed behavior, and metrics for analyzing and quantifying similar differentiation kinetics are presented. Through careful mathematical analysis and simulation, coupled with experimental data, two methods for achieving ratio control are presented, with the optimal schema for any application being dependent on the noisiness of the system under study. Together, these studies push the boundaries of gene network control, with potential applications in stem cell differentiation, therapeutics, and bio-production.
ContributorsMenn, David J (Author) / Wang, Xiao (Thesis advisor) / Kiani, Samira (Committee member) / Haynes, Karmella (Committee member) / Nielsen, David (Committee member) / Marshall, Pamela (Committee member) / Arizona State University (Publisher)
Created2018
Description
Chromatin is the dynamic structure of proteins and nucleic acids into which eukaryotic genomes are organized. For those looking to engineer mammalian genomes, chromatin is both an opportunity and an obstacle. While chromatin provides another tool with which to control gene expression, regional density can lead to variability in genome editing efficiency by CRISPR/Cas9 systems. Many groups have attempted to de-silence chromatin to regulate genes and enhance DNA's accessibility to nucleases, but inconsistent results leave outstanding questions. Here, I test different types of activators, to analyze changes in chromatin features that result for chromatin opening, and to identify the critical biochemical features that support artificially generated open, transcriptionally active chromatin.
I designed, built, and tested a panel of synthetic pioneer factors (SPiFs) to open condensed, repressive chromatin with the aims of 1) activating repressed transgenes in mammalian cells and 2) reversing the inhibitory effects of closed chromatin on Cas9-endonuclease activity. Pioneer factors are unique in their ability to bind DNA in closed chromatin. In order to repurpose this natural function, I designed SPiFs from a Gal4 DNA binding domain, which has inherent pioneer functionality, fused with chromatin-modifying peptides with distinct functions.
SPiFs with transcriptional activation as their primary mechanism were able to reverse this repression and induced a stably active state. My work also revealed the active site from proto-oncogene MYB as a novel transgene activator. To determine if MYB could be used generally to restore transgene expression, I fused it to a deactivated Cas9 and targeted a silenced transgene in native heterochromatin. The resulting activator was able to reverse silencing and can be chemically controlled with a small molecule drug.
Other SPiFs in my panel did not increase gene expression. However, pretreatment with several of these expression-neutral SPiFs increased Cas9-mediated editing in closed chromatin, suggesting a crucial difference between chromatin that is accessible and that which contains genes being actively transcribed. Understanding this distinction will be vital to the engineering of stable transgenic cell lines for product production and disease modeling, as well as therapeutic applications such as restoring epigenetic order to misregulated disease cells.
I designed, built, and tested a panel of synthetic pioneer factors (SPiFs) to open condensed, repressive chromatin with the aims of 1) activating repressed transgenes in mammalian cells and 2) reversing the inhibitory effects of closed chromatin on Cas9-endonuclease activity. Pioneer factors are unique in their ability to bind DNA in closed chromatin. In order to repurpose this natural function, I designed SPiFs from a Gal4 DNA binding domain, which has inherent pioneer functionality, fused with chromatin-modifying peptides with distinct functions.
SPiFs with transcriptional activation as their primary mechanism were able to reverse this repression and induced a stably active state. My work also revealed the active site from proto-oncogene MYB as a novel transgene activator. To determine if MYB could be used generally to restore transgene expression, I fused it to a deactivated Cas9 and targeted a silenced transgene in native heterochromatin. The resulting activator was able to reverse silencing and can be chemically controlled with a small molecule drug.
Other SPiFs in my panel did not increase gene expression. However, pretreatment with several of these expression-neutral SPiFs increased Cas9-mediated editing in closed chromatin, suggesting a crucial difference between chromatin that is accessible and that which contains genes being actively transcribed. Understanding this distinction will be vital to the engineering of stable transgenic cell lines for product production and disease modeling, as well as therapeutic applications such as restoring epigenetic order to misregulated disease cells.
ContributorsBarrett, Cassandra M (Author) / Haynes, Karmella A (Thesis advisor) / Rege, Kaushal (Committee member) / Mills, Jeremy (Committee member) / Kiani, Samira (Committee member) / Arizona State University (Publisher)
Created2019
Description
Rapid development of new technology has significantly disrupted the way radiotherapy is planned and delivered. These processes involve delivering high radiation doses to the target tumor while minimizing dose to the surrounding healthy tissue. However, with rapid implementation of these new technologies, there is a need for the detection of prescribed ionizing radiation for radioprotection of the patient and quality assurance of the technique employed. Most available clinical sensors are subjected to various limitations including requirement of extensive training, loss of readout with sequential measurements, sensitivity to light and post-irradiation wait time prior to analysis. Considering these disadvantages, there is still a need for a sensor that can be fabricated with ease and still operate effectively in predicting the delivered radiation dose.
The dissertation discusses the development of a sensor that changes color upon exposure to therapeutic levels of ionizing radiation used during routine radiotherapy. The underlying principle behind the sensor is based on the formation of gold nanoparticles from its colorless precursor salt solution upon exposure to ionizing radiation. Exposure to ionizing radiation generates free radicals which reduce ionic gold to its zerovalent gold form which further nucleate and mature into nanoparticles. The generation of these nanoparticles render a change in color from colorless to a maroon/pink depending on the intensity of incident ionizing radiation. The shade and the intensity of the color developed is used to quantitatively and qualitatively predict the prescribed radiation dose.
The dissertation further describes the applicability of sensor to detect a wide range of ionizing radiation including high energy photons, protons, electrons and emissions from radioactive isotopes while remaining insensitive to non-ionizing radiation. The sensor was further augmented with a capability to differentiate regions that are irradiated and non-irradiated in two dimensions. The dissertation further describes the ability of the sensor to predict dose deposition in all three dimensions. The efficacy of the sensor to predict the prescribed dose delivered to canine patients undergoing radiotherapy was also demonstrated. All these taken together demonstrate the potential of this technology to be translatable to the clinic to ensure patient safety during routine radiotherapy.
The dissertation discusses the development of a sensor that changes color upon exposure to therapeutic levels of ionizing radiation used during routine radiotherapy. The underlying principle behind the sensor is based on the formation of gold nanoparticles from its colorless precursor salt solution upon exposure to ionizing radiation. Exposure to ionizing radiation generates free radicals which reduce ionic gold to its zerovalent gold form which further nucleate and mature into nanoparticles. The generation of these nanoparticles render a change in color from colorless to a maroon/pink depending on the intensity of incident ionizing radiation. The shade and the intensity of the color developed is used to quantitatively and qualitatively predict the prescribed radiation dose.
The dissertation further describes the applicability of sensor to detect a wide range of ionizing radiation including high energy photons, protons, electrons and emissions from radioactive isotopes while remaining insensitive to non-ionizing radiation. The sensor was further augmented with a capability to differentiate regions that are irradiated and non-irradiated in two dimensions. The dissertation further describes the ability of the sensor to predict dose deposition in all three dimensions. The efficacy of the sensor to predict the prescribed dose delivered to canine patients undergoing radiotherapy was also demonstrated. All these taken together demonstrate the potential of this technology to be translatable to the clinic to ensure patient safety during routine radiotherapy.
ContributorsSubramaniam Pushpavanam, Karthik (Author) / Rege, Kaushal (Thesis advisor) / Sapareto, Stephen (Committee member) / Nannenga, Brent (Committee member) / Green, Matthew (Committee member) / Mu, Bin (Committee member) / Arizona State University (Publisher)
Created2019
Description
Antibiotic resistant bacteria are a worldwide epidemic threatening human survival. Antimicrobial susceptibility tests (ASTs) are important for confirming susceptibility to empirical antibiotics and detecting resistance in bacterial isolates. Current ASTs are based on bacterial culturing, which take 2-14 days to complete depending on the microbial growth rate. Considering the high mortality and morbidity rates for most acute infections, such long time frames are clinically impractical and pose a huge risk to a patient's life. A faster AST will reduce morbidity and mortality rates, as well as help healthcare providers, administer narrow spectrum antibiotics at the earliest possible treatment stage.
In this dissertation, I developed a nonculture-based AST using an imaging and cell tracking technology. I track individual Escherichia coli O157:H7 (E. coli O157:H7) Uropathogenic Escherichia Coli (UPEC) cells, widely implicated in food-poisoning outbreaks and urinary tract infections respectively. Cells tethered to a surface are tracked on the nanometer scale, and phenotypic motion is correlated with bacterial metabolism. Antibiotic action significantly slows down motion of tethered bacterial cells, which is used to perform antibiotic susceptibility testing. Using this technology, the clinical minimum bactericidal concentration of an antibiotic against UPEC pathogens was calculated within 2 hours directly in urine samples as compared to 3 days using current gold standard tools.
Such technologies can make a tremendous impact to improve the efficacy and efficiency of infectious disease treatment. This has the potential to reduce the antibiotic mis-prescription steeply, which can drastically decrease the annual 2M+ hospitalizations and 23,000+ deaths caused due to antibiotic resistance bacteria along with saving billions of dollars to payers, patients, and hospitals.
In this dissertation, I developed a nonculture-based AST using an imaging and cell tracking technology. I track individual Escherichia coli O157:H7 (E. coli O157:H7) Uropathogenic Escherichia Coli (UPEC) cells, widely implicated in food-poisoning outbreaks and urinary tract infections respectively. Cells tethered to a surface are tracked on the nanometer scale, and phenotypic motion is correlated with bacterial metabolism. Antibiotic action significantly slows down motion of tethered bacterial cells, which is used to perform antibiotic susceptibility testing. Using this technology, the clinical minimum bactericidal concentration of an antibiotic against UPEC pathogens was calculated within 2 hours directly in urine samples as compared to 3 days using current gold standard tools.
Such technologies can make a tremendous impact to improve the efficacy and efficiency of infectious disease treatment. This has the potential to reduce the antibiotic mis-prescription steeply, which can drastically decrease the annual 2M+ hospitalizations and 23,000+ deaths caused due to antibiotic resistance bacteria along with saving billions of dollars to payers, patients, and hospitals.
ContributorsSyal, Karan (Author) / Tao, Nongjian (Thesis advisor) / Haydel, Shelley (Committee member) / Rege, Kaushal (Committee member) / Wang, Shaopeng (Committee member) / Haynes, Karmella (Committee member) / Arizona State University (Publisher)
Created2017