Matching Items (97)
Description
In vertebrate outer retina, changes in the membrane potential of horizontal cells affect the calcium influx and glutamate release of cone photoreceptors via a negative feedback. This feedback has a number of important physiological consequences. One is called background-induced flicker enhancement (BIFE) in which the onset of dim background enhances the center flicker response of horizontal cells. The underlying mechanism for the feedback is still unclear but competing hypotheses have been proposed. One is the GABA hypothesis, which states that the feedback is mediated by gamma-aminobutyric acid (GABA), an inhibitory neurotransmitter released from horizontal cells. Another is the ephaptic hypothesis, which contends that the feedback is non-GABAergic and is achieved through the modulation of electrical potential in the intersynaptic cleft between cones and horizontal cells. In this study, a continuum spine model of the cone-horizontal cell synaptic circuitry is formulated. This model, a partial differential equation system, incorporates both the GABA and ephaptic feedback mechanisms. Simulation results, in comparison with experiments, indicate that the ephaptic mechanism is necessary in order for the model to capture the major spatial and temporal dynamics of the BIFE effect. In addition, simulations indicate that the GABA mechanism may play some minor modulation role.
ContributorsChang, Shaojie (Author) / Baer, Steven M. (Thesis advisor) / Gardner, Carl L (Thesis advisor) / Crook, Sharon M (Committee member) / Kuang, Yang (Committee member) / Ringhofer, Christian (Committee member) / Arizona State University (Publisher)
Created2012
Description
Hydrogenases catalyze the interconversion of protons, electrons, and hydrogen according to the reaction: 2H+ + 2e- <-> H2 while using only earth abundant metals, namely nickel and iron for catalysis. The enzymatic turnover of Clostridium acetobutylicum [FeFe]-hydrogenase has been investigated through the use of electrochemical and scanning probe techniques. Scanning tunneling microscopy (STM) imaging revealed sub-monolayer surface coverage. Cyclic voltammetry yielded a catalytic, cathodic hydrogen production signal similar to that observed for a platinum electrode. From the direct observation of single enzymes and the macroscopic electrochemical measurements obtained from the same electrode, the apparent turnover frequency (TOF) per single enzyme molecule as a function of potential was determined. The TOF at 0.7 V vs. Ag/AgCl for the four SAMs yielded a decay constant for electronic coupling (β) through the SAM of ~ 0.82 Å -1, in excellent agreement with published values for similar SAMs. One mechanism used by plants to protect against damage is called nonphotochemical quenching (NPQ). Triggered by low pH in the thylakoid lumen, NPQ leads to conversion of excess excitation energy in the antenna system to heat before it can initiate production of harmful chemical species by photosynthetic reaction centers. Here a synthetic hexad molecule that functionally mimics the role of the antenna in NPQ is described. When the hexad is dissolved in an organic solvent, five zinc porphyrin antenna moieties absorb light, exchange excitation energy, and ultimately decay by normal photophysical processes. However, when acid is added, a pH-sensitive dye moiety is converted to a form that rapidly quenches the first excited singlet states of all five porphyrins, converting the excitation energy to heat and rendering the porphyrins kinetically incompetent to perform useful photochemistry. Charge transport was also studied in single-molecule junctions formed with a 1,7-pyrrolidine-substituted 3,4,9,10-Perylenetetracarboxylic diimide (PTCDI) molecule. A reduction in the highest occupied (HOMO) and lowest unoccupied (LUMO) molecular orbitals energy gap due to the electronic properties of the substituents is seen when compared to an unsubstituted-PTCDI. The small HOMO-LUMO energy gap allows for switching between electron- and hole-dominated charge transport with a gate voltage, thus demonstrating a single-molecule ambipolar field effect transistor.
ContributorsMadden, Christopher (Author) / Moore, Thomas A. (Thesis advisor) / Jones, Anne (Committee member) / Tao, Nongjian (Committee member) / Arizona State University (Publisher)
Created2012
Description
CpG methylation is an essential requirement for the normal development of mammals, but aberrant changes in the methylation can lead to tumor progression and cancer. An in-depth understanding of this phenomenon can provide insights into the mechanism of gene repression. We present a study comparing methylated DNA and normal DNA wrt its persistence length and contour length. Although, previous experiments and studies show no difference between the physical properties of the two, the data collected and interpreted here gives a different picture to the methylation phenomena and its effect on gene silencing. The study was extended to the artificially reconstituted chromatin and its interactions with the methyl CpG binding proteins were also probed.
ContributorsKaur, Parminder (Author) / Lindsay, Stuart (Thesis advisor) / Ros, Robert (Committee member) / Tao, Nongjian (Committee member) / Vaiana, Sara (Committee member) / Beckenstein, Oliver (Committee member) / Arizona State University (Publisher)
Created2012
Description
In this research we consider stochastic models of Glioblastoma Multiforme brain tumors. We first look at a model by K. Swanson et al., which describes the dynamics as random diffusion plus deterministic logistic growth. We introduce a stochastic component in the logistic growth in the form of a random growth rate defined by a Poisson process. We show that this stochastic logistic growth model leads to a more accurate evaluation of the tumor growth compared its deterministic counterpart. We also discuss future plans to incorporate individual patient geometry, extend the model to three dimensions and to incorporate effects of different treatments into our model, in collaboration with a local hospital.
ContributorsManning, Michael Clare (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Gardner, Carl (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Letters and Sciences (Contributor) / School of Human Evolution and Social Change (Contributor)
Created2013-12
Description
Five immunocompetent C57BL/6-cBrd/cBrd/Cr (albino C57BL/6) mice were injected with GL261-luc2 cells, a cell line sharing characteristics of human glioblastoma multiforme (GBM). The mice were imaged using magnetic resonance (MR) at five separate time points to characterize growth and development of the tumor. After 25 days, the final tumor volumes of the mice varied from 12 mm3 to 62 mm3, even though mice were inoculated from the same tumor cell line under carefully controlled conditions. We generated hypotheses to explore large variances in final tumor size and tested them with our simple reaction-diffusion model in both a 3-dimensional (3D) finite difference method and a 2-dimensional (2D) level set method. The parameters obtained from a best-fit procedure, designed to yield simulated tumors as close as possible to the observed ones, vary by an order of magnitude between the three mice analyzed in detail. These differences may reflect morphological and biological variability in tumor growth, as well as errors in the mathematical model, perhaps from an oversimplification of the tumor dynamics or nonidentifiability of parameters. Our results generate parameters that match other experimental in vitro and in vivo measurements. Additionally, we calculate wave speed, which matches with other rat and human measurements.
ContributorsRutter, Erica (Author) / Stepien, Tracy (Author) / Anderies, Barrett (Author) / Plasencia, Jonathan (Author) / Woolf, Eric C. (Author) / Scheck, Adrienne C. (Author) / Turner, Gregory H. (Author) / Liu, Qingwei (Author) / Frakes, David (Author) / Kodibagkar, Vikram (Author) / Kuang, Yang (Author) / Preul, Mark C. (Author) / Kostelich, Eric (Author) / College of Liberal Arts and Sciences (Contributor)
Created2017-05-31
Description
Over time, tumor treatment resistance inadvertently develops when androgen de-privation therapy (ADT) is applied to metastasized prostate cancer (PCa). To combat tumor resistance, while reducing the harsh side effects of hormone therapy, the clinician may opt to cyclically alternates the patient’s treatment on and off. This method,known as intermittent ADT, is an alternative to continuous ADT that improves the patient’s quality of life while testosterone levels recover between cycles. In this paper,we explore the response of intermittent ADT to metastasized prostate cancer by employing a previously clinical data validated mathematical model to new clinical data from patients undergoing Abiraterone therapy. This cell quota model, a system of ordinary differential equations constructed using Droop’s nutrient limiting theory, assumes the tumor comprises of castration-sensitive (CS) and castration-resistant (CR)cancer sub-populations. The two sub-populations rely on varying levels of intracellular androgen for growth, death and transformation. Due to the complexity of the model,we carry out sensitivity analyses to study the effect of certain parameters on their outputs, and to increase the identifiability of each patient’s unique parameter set. The model’s forecasting results show consistent accuracy for patients with sufficient data,which means the model could give useful information in practice, especially to decide whether an additional round of treatment would be effective.
ContributorsBennett, Justin Klark (Author) / Kuang, Yang (Thesis director) / Kostelich, Eric (Committee member) / Phan, Tin (Committee member) / School of Mathematical and Statistical Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
A major challenge with tissue samples used for biopsies is the inability to monitor their molecular quality before diagnostic testing. When tissue is resected from a patient, the cells are removed from their blood supply and normal temperature-controlled environment, which causes significant biological stress. As a result, the molecular composition and integrity undergo significant change. Currently, there is no method to track the effects of these artefactual stresses on the sample tissue to determine any deviations from the actual patient physiology. Without a way to track these changes, pathologists have to blindly trust that the tissue samples they are given are of high quality and fit for molecular analysis; physicians use the analysis to make diagnoses and treatment plans based on the assumption that the samples are valid. A possible way to track the quality of the tissue is by measuring volatile organic compounds (VOCs) released from the samples. VOCs are carbon-based chemicals with high vapor pressure at room temperature. There are over 1,800 known VOCs within humans and a number of these exist in every tissue sample. They are individualized and often indicative of a person’s metabolic condition. For this reason, VOCs are often used for diagnostic purposes. Their usefulness in diagnostics, reflectiveness of a person’s metabolic state, and accessibility lends them to being beneficial for tracking degradation. We hypothesize that there is a relationship between the change in concentration of the volatile organic compounds of a sample, and the molecular quality of a sample. This relationship is what would indicate the accuracy of the tissue quality used for a biopsy in relation to the tissue within the body.
ContributorsSharma, Nandini (Co-author) / Fragoso, Claudia (Co-author) / Grenier, Tyler (Co-author) / Hanson, Abigail (Co-author) / Compton, Carolyn (Thesis director) / Tao, Nongjian (Committee member) / Moakley, George (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Magnetic resonance imaging (MRI) data of metastatic brain cancer patients at the Barrow Neurological Institute sparked interest in the radiology department due to the possibility that tumor size distributions might mimic a power law or an exponential distribution. In order to consider the question regarding the growth trends of metastatic brain tumors, this thesis analyzes the volume measurements of the tumor sizes from the BNI data and attempts to explain such size distributions through mathematical models. More specifically, a basic stochastic cellular automaton model is used and has three-dimensional results that show similar size distributions of those of the BNI data. Results of the models are investigated using the likelihood ratio test suggesting that, when the tumor volumes are measured based on assuming tumor sphericity, the tumor size distributions significantly mimic the power law over an exponential distribution.
ContributorsFreed, Rebecca (Co-author) / Snopko, Morgan (Co-author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / WPC Graduate Programs (Contributor) / School of Accountancy (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
Prostate cancer is the second most common kind of cancer in men. Fortunately, it has a 99% survival rate. To achieve such a survival rate, a variety of aggressive therapies are used to treat prostate cancers that are caught early. Androgen deprivation therapy (ADT) is a therapy that is given in cycles to patients. This study attempted to analyze what factors in a group of 79 patients caused them to stick with or discontinue the treatment. This was done using naïve Bayes classification, a machine-learning algorithm. The usage of this algorithm identified high testosterone as an indicator of a patient persevering with the treatment, but failed to produce statistically significant high rates of prediction.
ContributorsMillea, Timothy Michael (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Glioblastoma multiforme (GBM) is a malignant, aggressive and infiltrative cancer of the central nervous system with a median survival of 14.6 months with standard care. Diagnosis of GBM is made using medical imaging such as magnetic resonance imaging (MRI) or computed tomography (CT). Treatment is informed by medical images and includes chemotherapy, radiation therapy, and surgical removal if the tumor is surgically accessible. Treatment seldom results in a significant increase in longevity, partly due to the lack of precise information regarding tumor size and location. This lack of information arises from the physical limitations of MR and CT imaging coupled with the diffusive nature of glioblastoma tumors. GBM tumor cells can migrate far beyond the visible boundaries of the tumor and will result in a recurring tumor if not killed or removed. Since medical images are the only readily available information about the tumor, we aim to improve mathematical models of tumor growth to better estimate the missing information. Particularly, we investigate the effect of random variation in tumor cell behavior (anisotropy) using stochastic parameterizations of an established proliferation-diffusion model of tumor growth. To evaluate the performance of our mathematical model, we use MR images from an animal model consisting of Murine GL261 tumors implanted in immunocompetent mice, which provides consistency in tumor initiation and location, immune response, genetic variation, and treatment. Compared to non-stochastic simulations, stochastic simulations showed improved volume accuracy when proliferation variability was high, but diffusion variability was found to only marginally affect tumor volume estimates. Neither proliferation nor diffusion variability significantly affected the spatial distribution accuracy of the simulations. While certain cases of stochastic parameterizations improved volume accuracy, they failed to significantly improve simulation accuracy overall. Both the non-stochastic and stochastic simulations failed to achieve over 75% spatial distribution accuracy, suggesting that the underlying structure of the model fails to capture one or more biological processes that affect tumor growth. Two biological features that are candidates for further investigation are angiogenesis and anisotropy resulting from differences between white and gray matter. Time-dependent proliferation and diffusion terms could be introduced to model angiogenesis, and diffusion weighed imaging (DTI) could be used to differentiate between white and gray matter, which might allow for improved estimates brain anisotropy.
ContributorsAnderies, Barrett James (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Stepien, Tracy (Committee member) / Harrington Bioengineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05