Electronic Theses and Dissertations
Permanent URI for this collectionhttps://hdl.handle.net/2346/521
About: Electronic theses and dissertations (ETDs) are the graduate research outputs of Texas Tech University. They represent years of work from our Master's and Doctoral graduates. If you find the ThinkTech digital repository useful, please tell us! Share how open access to scholarship benefits you. Your story matters to us.
To listen to recitals, Madrigal Dinners, and other performance recordings related to the TTU School of Music's graduates, login and stream through our Stream Audio and Video Experience (SAVE)
Browse
Browsing Electronic Theses and Dissertations by Author "Aavani, Pooya"
Now showing 1 - 3 of 3
- Results Per Page
- Sort Options
Item A new theoretical treatment of pathogen and host evolution(2019-12) Aavani, Pooya; Rice, Sean; Allen, Linda J. S.; Salazar-Bravo, Jorge; Olson, Matt; Schwilk, Dylan; Schmidt, KennethEvolutionary processes are inherently stochastic, since we can never know with certainty exactly how many descendants an individual will leave, or what the phenotypes of those descendants will be. Despite this, models of pathogen evolution have nearly all been deterministic, treating values such as transmission and virulence as parameters that can be known ahead of time. I present a broadly applicable analytic approach for modeling pathogen evolution in which vital parameters such as transmission and virulence are treated as random variables, rather than as fixed values. Starting from a general stochastic model of evolution, I derive specific equations for the evolution of transmission and virulence. I show that adding stochasticity introduces new directional components to pathogen evolution. In particular, two kinds of covariation between traits emerge as important: covariance across the population (what is usually measured), and covariance between random variables within an individual. I show that these different kinds of trait covariation can be of opposite sign and contribute to evolution in very different ways. I then apply these to a particular special case; the SIR model of pathogen dynamics. In host-parasite coevolution, the parasite is selected to increase its infectivity while host is selected to resists the parasite infection. It is widely held that parasite-mediated sexual selection can further amplify the selective pressure on the host to overcome parasite infection. I focus on certain types of parasites, those that can impair the activity of the host immune function and I show that the effect of sexual selection can actually reduce the selective pressure on the host immune response to adapt to the parasite infection. I design a simple mathematical model for a population of sexually reproducing organism in which individuals are choosy, preferring traits that are correlated with immune system activity. I introduce to this population a parasite that can suppress activation of the host's immune response. The derived results show that even though the host immune system is likely to ultimately evolve and adapt to the parasite infection, when sexual selection is part of this process, it can slow down this evolution on the host and give the parasite more time to get established.Item Detecting imprinting and maternal effects using Monte Carlo expectation maximization algorithm(2019-12) Aavani, Pooya; Zhang, Fangyuan; Trindade, Alex; Rice, SeanNumerous statistical methods have been developed to explore genomic imprinting and maternal effects, which are causes of parent-of-origin patterns in complex human diseases. However, most of them either only model one of these two confounded epigenetic effects, or make strong yet unrealistic assumptions about the population to avoid over- parameterization. A recent partial likelihood method (LIME) can identify both epigenetic effects based on case-control family data without those assumptions. Theoretical and empirical studies have shown its validity and robustness. However, because LIME obtains parameter estimation by maximizing partial likelihood, it is interesting to compare its efficiency with full likelihood maximizer. To overcome the difficulty in over-parameterization when using full likelihood, in this study we propose a Monte Carlo Expectation Maximization (MCEM) method to detect imprinting and maternal effects jointly. Those unknown mating type probabilities, the nuisance parameters, can be considered as latent variables in EM algorithm. Monte Carlo samples are used to numerically approximate the expectation function that cannot be solved algebraically. Our simulation results show that this MCEM algorithm takes longer computational time, and can give higher bias in some simulations compared to LIME. However, it can generally detect both epigenetic effects with higher power and smaller standard error which demonstrates that it can be a good complement of LIME method.Item Ordinary and delay differential equation models of viral infection with application to HIV and Hepatitis C virus(2012-08) Aavani, Pooya; Allen, Linda J. S.; Allen, Edward J.; Hoang, Luan T.Human adaptive immune response consists of three major types of cells, namely, CD4 T cells, CTL (Cytotoxic T Lymphocytes), and antibodies. CTL attack and kill cells that are infected by viruses. Antibodies are capable of identifying and neutralizing viruses. In the presence of virus infection, CD4 T Cells stimulate the proliferation of CTL. Also the proliferation of antibodies becomes stimulated by viruses. These ideas are used to introduce a new ordinary differential equation model for exploring the dynamics of infection. Production of viruses by infectious CD4 T cells are not instantaneous and they require time to occur. Thus, explaining the dynamics of infections more accurately in the model, it is important to consider a time gap, which is known as delay. The new delay differential equation model, which considers a delay in the production of viruses, is also analyzed in this thesis. Both models are useful to be applied for HIV and hepatitis C infections, because in these models target cells are CD4 T cells, infectious agents are viruses, and the biological implications of the mathematical results are similar to the stages of the infections.