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- Member of: Theses and Dissertations
Description
In complex consumer-resource type systems, where diverse individuals are interconnected and interdependent, one can often anticipate what has become known as the tragedy of the commons, i.e., a situation, when overly efficient consumers exhaust the common resource, causing collapse of the entire population. In this dissertation I use mathematical modeling to explore different variations on the consumer-resource type systems, identifying some possible transitional regimes that can precede the tragedy of the commons. I then reformulate it as a game of a multi-player prisoner's dilemma and study two possible approaches for preventing it, namely direct modification of players' payoffs through punishment/reward and modification of the environment in which the interactions occur. I also investigate the questions of whether the strategy of resource allocation for reproduction or competition would yield higher fitness in an evolving consumer-resource type system and demonstrate that the direction in which the system will evolve will depend not only on the state of the environment but largely on the initial composition of the population. I then apply the developed framework to modeling cancer as an evolving ecological system and draw conclusions about some alternative approaches to cancer treatment.
ContributorsKareva, Irina (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Collins, James (Committee member) / Nagy, John (Committee member) / Smith, Hal (Committee member) / Arizona State University (Publisher)
Created2012
Description
A functioning food web is the basis of a functioning community and ecosystem. Thus, it is important to understand the dynamics that control species behaviors and interactions. Alterations to the fundamental dynamics can prove detrimental to the future success of our environment. Research and analysis focus on the global dynamics involved in intraguild predation (IGP), a three species subsystem involving both competition and predation. A mathematical model is derived using differential equations based on pre-existing models to accurately predict species behavior. Analyses provide sufficient conditions for species persistence and extinction that can be used to explain global dynamics. Dynamics are compared for two separate models, one involving a specialist predator and the second involving a generalist predator, where systems involving a specialist predator are prone to unstable dynamics. Analyses have implications in biological conservation tactics including various methods of prevention and preservation. Simulations are used to compare dynamics between models involving continuous time and those involving discrete time. Furthermore, we derive a semi-discrete model that utilizes both continuous and discrete time series dynamics. Simulations imply that Holling's Type III functional response controls the potential for three species persistence. Complicated dynamics govern the IGP subsystem involving the white-footed mouse, gypsy moth, and oak, and they ultimately cause the synchronized defoliation of forests across the Northeastern United States. Acorn mast seasons occur every 4-5 years, and they occur simultaneously across a vast geographic region due to universal cues. Research confirms that synchronization can be transferred across trophic levels to explain how this IGP system ultimately leads to gypsy moth outbreaks. Geographically referenced data is used to track and slow the spread of gypsy moths further into the United States. Geographic Information Systems (GIS) are used to create visual, readily accessible, displays of trap records, defoliation frequency, and susceptible forest stands. Mathematical models can be used to explain both changes in population densities and geographic movement. Analyses utilizing GIS softwares offer a different, but promising, way of approaching the vast topic of conservation biology. Simulations and maps are produced that can predict the effects of conservation efforts.
ContributorsWedekin, Lauren (Author) / Kang, Yun (Thesis advisor) / Green, Douglas (Committee member) / Miller, William (Committee member) / Arizona State University (Publisher)
Created2012
Description
Predicting resistant prostate cancer is critical for lowering medical costs and improving the quality of life of advanced prostate cancer patients. I formulate, compare, and analyze two mathematical models that aim to forecast future levels of prostate-specific antigen (PSA). I accomplish these tasks by employing clinical data of locally advanced prostate cancer patients undergoing androgen deprivation therapy (ADT). I demonstrate that the inverse problem of parameter estimation might be too complicated and simply relying on data fitting can give incorrect conclusions, since there is a large error in parameter values estimated and parameters might be unidentifiable. I provide confidence intervals to give estimate forecasts using data assimilation via an ensemble Kalman Filter. Using the ensemble Kalman Filter, I perform dual estimation of parameters and state variables to test the prediction accuracy of the models. Finally, I present a novel model with time delay and a delay-dependent parameter. I provide a geometric stability result to study the behavior of this model and show that the inclusion of time delay may improve the accuracy of predictions. Also, I demonstrate with clinical data that the inclusion of the delay-dependent parameter facilitates the identification and estimation of parameters.
ContributorsBaez, Javier (Author) / Kuang, Yang (Thesis advisor) / Kostelich, Eric (Committee member) / Crook, Sharon (Committee member) / Gardner, Carl (Committee member) / Nagy, John (Committee member) / Arizona State University (Publisher)
Created2017
Description
Cellular and molecular biologists often perform cellular assays to obtain a better understanding of how cells work. However, in order to obtain a measurable response by the end of an experiment, the cells must reach an ideal cell confluency. Prior to conducting the cellular assays, range-finding experiments need to be conducted to determine an initial plating density that will result in this ideal confluency, which can be costly. To help alleviate this common issue, a mathematical model was developed that describes the dynamics of the cell population used in these experiments. To develop the model, images of cells from different three-day experiments were analyzed in Photoshop®, giving a measure of cell count and confluency (the percentage of surface area covered by cells). The cell count data were then fitted into an exponential growth model and were correlated to the cell confluency to obtain a relationship between the two. The resulting mathematical model was then evaluated with data from an independent experiment. Overall, the exponential growth model provided a reasonable and robust prediction of the cell confluency, though improvements to the model can be made with a larger dataset. The approach used to develop this model can be adapted to generate similar models of different cell-lines, which will reduce the number of preliminary range-finding experiments. Reducing the number of these preliminary experiments can save valuable time and experimental resources needed to conduct studies using cellular assays.
ContributorsGuerrero, Victor Dominick (Co-author) / Guerrero, Victor (Co-author) / Watanabe, Karen (Thesis director) / Jurutka, Peter (Committee member) / School of Mathematical and Natural Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
The Hippo signaling pathway is responsible for regulating organ size through cell proliferation, stemness, and apoptosis. Through targeting proteins Yes-associated kinase 1(YAP) and transcriptional co-activator with a PDZ-binding domain(TAZ), YAP/TAZ are unable to enter the nucleus and bind with coactivators to express target genes. To understand YAP/TAZ dynamics and its role in tumorigenesis, tissue regeneration, and tissue degeneration, a regulatory network was modeled by ordinary differential equations. Using MATLAB, the deterministic behavior of the network was observed to determine YAP/TAZ activity in different states. Performing the bifurcation analysis of the system through Oscill8, three states were identified: tumorigenic/regenerative, degenerative, and homeostatic states. Further analysis through parameter modification allowed a better understanding of which proteins can be targeted for cancer and degenerative disease.
ContributorsBarra Avila, Diego Rodrigo (Author) / Tian, Xiaojun (Thesis director) / Wang, Xiao (Committee member) / Harrington Bioengineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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
Description
The intervertebral disc goes through degenerative changes with age, which leads to disc thinning, bulging, or herniation. Spinal fusion treatments are ineffective as they cause quicker degeneration of adjacent discs and fail in nearly 20% of cases, so researchers have turned to tissue-engineering biocompatible intervertebral discs for transplantation. However novel and effective as this may seem, these transplanted discs still show evidence of degeneration after just 5 years. I hypothesize that these discs are degenerating due to a blockage of the cartilaginous endplates post-transplantation that is hindering nutrient transport through the intervertebral disc. In order to test this hypothesis, I developed a mathematical model of nutrient transport through the intervertebral disc in one diurnal daily loading cycle. This model was used to simulate open endplates and blocked endplates and then compare differences in nutrient concentration and nutrient transport to the center of the disc. Results from the math model simulations were then compared to in vitro experimental data collected in lab to verify the findings on a physiological level. Results showed significant differences, both in vitro and in the model, between nutrient transport in open endplates vs blocked endplates, lending support to the original hypothesis. This study only presents preliminary results, but could hold the key to preventing future disc degeneration post-transplantation.
ContributorsMunter, Bryce Taylor (Author) / Santello, Marco (Thesis director) / Caplan, Michael (Committee member) / Giers, Morgan (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2015-05
Description
Pharmacokinetics describes the movement and processing of a drug in a body, while Pharmacodynamics describes the drug's effect on a given subject. Pharmacokinetic/Pharmacodynamic(Pk/Pd) models have become a fundamental tool when predicting bacterial behavior and drug development. In November of 2009, Katsube et al. published their paper detailing their Pk/Pd model for the drug Doripenem and the bacteria P. aeruginosa. In their paper, they determined that there is a dependent relationship between the drug's effectiveness and the dosing strategy of the drug. Therefore, this thesis has applied optimal control in order to optimize the drug's effectiveness, while not burdening the subject with the side effects of the drug. Optimal Control is a mathematical tool used to balance two competing factors. As a result, it has become a useful tool used to make decisions involving complex behavior. By using Optimal Control, the model will maximize the drug's effect on the bacterial population of P. aeruginosa, while minimizing the drug concentration of Doripenem. In doing so, our research will enable doctors and clinicians to maximize a drug's effectiveness on the body, while minimizing side effects.
ContributorsSawkins, Bryan Thomas (Author) / Camacho, Erika (Thesis director) / Wirkus, Stephen (Committee member) / School of Mathematical and Natural Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The evolution of blindness in cave animals has been heavily studied; however, little research has been done on the interaction of migration and drift on the development of blindness in these populations. In this study, a model is used to compare the effect that genetic drift has on the fixation of a blindness allele for varying amounts of migration and selection. For populations where the initial frequency is quite low, genetic drift plays a much larger role in the fixation of blindness than populations where the initial frequency is high. In populations where the initial frequency is high, genetic drift plays almost no role in fixation. Our results suggest that migration plays a greater role in the fate of the blindness allele than selection.
ContributorsMerry, Alexandra Leigh (Author) / Cartwright, Reed (Thesis director) / Rosenberg, Michael (Committee member) / Schwartz, Rachel (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2014-05
Description
Glioblastoma Multiforme (GBM) is an aggressive and deadly form of brain cancer with a median survival time of about a year with treatment. Due to the aggressive nature of these tumors and the tendency of gliomas to follow white matter tracks in the brain, each tumor mass has a unique growth pattern. Consequently it is difficult for neurosurgeons to anticipate where the tumor will spread in the brain, making treatment planning difficult. Archival patient data including MRI scans depicting the progress of tumors have been helpful in developing a model to predict Glioblastoma proliferation, but limited scans per patient make the tumor growth rate difficult to determine. Furthermore, patient treatment between scan points can significantly compound the challenge of accurately predicting the tumor growth. A partnership with Barrow Neurological Institute has allowed murine studies to be conducted in order to closely observe tumor growth and potentially improve the current model to more closely resemble intermittent stages of GBM growth without treatment effects.
ContributorsSnyder, Lena Haley (Author) / Kostelich, Eric (Thesis director) / Frakes, David (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05