Computer Science Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2756
Browse
Search Results
Item ESTIMATION AND ANALYSIS OF CELL-SPECIFIC DNA METHYLATION FROM BISULFITE-SEQUENCING DATA(2018) Dorri, Faezeh; Bravo Corrada, Hector; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)DNA methylation is the best understood heritable gene regulatory mechanism that does not involve direct modification of DNA sequence itself. Cells with different methylation profiles (over temporal or micro-environmental dimensions) may exhibit different phenotypic properties. In cancer, heterogeneity across cells in the tumor microenvironment presents significant challenges to treatment. In particular, epigenetic heterogeneity is discernible among tumor cells, and it is believed to impact the growth properties and treatment resistance of tumors. Existing computational methods used to study the epigenetic composition of cell populations are based on the analysis of DNA methylation modifications at multiple consecutive genomic loci spanned by single DNA sequencing reads. These approaches have yielded great insight into how cell populations differ epigenetically across different tissues. However, they only provide a general summary of the epigenetic composition of these cell populations without providing cell-specific methylation patterns over longer genomic spans to perform a comprehensive analysis of the epigenetic heterogeneity of cell populations. In this dissertation, we address this challenge by proposing two computational methods called methylFlow and MCFDiff. In methylFlow, we propose a novel method based on network flow algorithms to reconstruct cell-specific methylation profiles using reads obtained from sequencing bisulfite-converted DNA.We reveal the methylation profile of underlying clones in a heterogeneous cell population including the methylation patterns and their corresponding abundance within the population. In MCFDiff, we propose a statistical model that leverages the identified cell-specific methylation profiles (from methylFlow) to determine regions of differential methylation composition (RDMCs) between multiple phenotypic groups, in particular, between tumor and paired normal tissue. In MCFDiff, we can systematically exclude the tumor tissue impurities and increase the accuracy in detecting the regions with differential methylation composition in normal and tumor samples. Profiling the changes between normal and tumor samples according to the reconstructed methylation profile of underling clone in different samples leads us to the discovery of de novo epigenetic markers and a better understanding about the effect of epigenetic heterogeneity in cancer dynamics from the initiation, progression to metastasis, and relapse.Item Modeling the Transfer of Drug Resistance in Solid Tumors(2018) Becker, Matthew Harrington; Levy, Doron; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)ABC efflux transporters are a key factor leading to multidrug resistance in cancer. Overexpression of these transporters significantly decreases the efficacy of anti-cancer drugs. Along with selection and induction, drug resistance may be trans- ferred between cells, which is the focus of this dissertaion. Specifically, we consider the intercellular transfer of P-glycoprotein (P-gp), a well-known ABC transporter that was shown to confer resistance to many common chemotherapeutic drugs. In a recent paper, Dura ́n et al. studied the dynamics of mixed cultures of resistant and sensitive NCI-H460 (human non-small cell lung cancer) cell lines [1]. As expected, the experimental data showed a gradual increase in the percentage of resistance cells and a decrease in the percentage of sensitive cells. The experimental work was accompanied with a mathematical model that assumed P-gp transfer from resistant cells to sensitive cells, rendering them temporarily resistant. The mathematical model provided a reasonable fit to the experimental data. In this dissertation we develop three new mathematical model for the transfer of drug resistance between cancer cells. Our first model is based on incorporating a resistance phenotype into a model of cancer growth [2]. The resulting model for P-gp transfer, written as a system of integro-differential equations, follows the dynamics of proliferating, quiescent, and apoptotic cells, with a varying resistance phenotype. We show that this model provides a good match to the dynamics of the experimental data of [1]. The mathematical model further suggests that resistant cancer cells have a slower division rate than the sensitive cells. Our second model is a reaction-diffusion model with sensitive, resistant, and temporarily resistant cancer cells occupying a 2-dimensional space. We use this model as another extension of [1]. We show that this model, with competition and diffusion in space, provides an even better fit to the experimental data [1]. We incorporate a cytotoxic drug and study the effects of varying treatment protocols on the size and makeup of the tumor. We show that constant infusion leads to a small but highly resistant tumor, while small doses do not do enough to control the overall growth of the tumor. Our final model extends [3], an integro-differential equation with resistance modeled as a continuous variable and a Boltzmann type integral describing the transfer of P-gp expression. We again extend the model into a 2-dimensional spatial domain and incorporate competition inhibited growth. The resulting model, written as a single partial differential equation, shows that over time the resistance transfer leads to a uniform distribution of resistance levels, which is consisten with the results of [3]. We include a cytotoxic agent and determine that, as with our second model, it alone cannot successfully eradicate the tumor. We briefly present a second extension wherein we include two distinct transfer rules. We show that there is no qualitative difference between the single transfer rule and the two-transfer rule model.