<p> Evolutionary forces such as recombination, demography, and selection can shape patterns of genetic diversity within populations and contribute to phenotypic variation. While theoretical models exist for each of these forces independently, mathematically modeling their joint impact on patterns of genetic diversity remains very challenging. Fortunately, it is possible to perform forward-in-time computer simulations of DNA sequences that incorporate all of these forces simultaneously. Here, I show that there are trade-offs between computational efficiency and accuracy for simulations of a widely investigated model of recurrent positive selection. I develop a theoretical model to explain this trade-off, and a simple algorithm that obtains the best possible computational performance for a given error tolerance. I then pivot to develop a framework for simulations of human DNA sequences and genetically complex phenotypes, incorporating recently inferred demographic models of human continental groups and selection on genes and non-coding elements. I use these simulations to investigate the power of rare variant association tests in the context of rampant selection and non-equilibrium demography. I show that the power of rare variant association tests is in some cases quite sensitive to underlying assumptions about the relationship between selection and effect sizes. This work highlights both the challenge and the promise of applying forward simulations in genetic studies that seek to infer the parameters of evolutionary models and detect statistical associations.</p>

<p>Structural genomics is a field of study that strives to derive and analyze the structural characteristics of proteins through means of experimentation and prediction using software and other automatic processes. Alongside implications for more effective drug design, the main motivation for structural genomics concerns the elucidation of each protein&rsquo;s function, given that the structure of a protein almost completely governs its function. Historically, the approach to derive the structure of a protein has been through exceedingly expensive, complex, and time consuming methods such as x-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. </p><p> In response to the inadequacies of these methods, three families of approaches developed in a relatively new branch of computer science known as bioinformatics. The aforementioned families include threading, homology-modeling, and the de novo approach. However, even these methods fail either due to impracticalities, the inability to produce novel folds, rampant complexity, inherent limitations, etc. In their stead, this work proposes the Fuzzy Greedy K-means Decision Forest model, which utilizes sequence motifs that transcend protein family boundaries to predict local tertiary structure, such that the method is cheap, effective, and can produce semi-novel folds due to its local (rather than global) prediction mechanism. This work further extends the FGK-DF model with a new algorithm, the Hierarchically Clustered-Hidden Markov Models (HC-HMM) method to extract protein primary sequence motifs in a more accurate and adequate manner than currently exhibited by the FGK-DF model, allowing for more accurate and powerful local tertiary structure predictions. Both algorithms are critically examined, their methodology thoroughly explained and tested against a consistent data set, the results thereof discussed at length. </p>

Thesis (MSc(R)) - University of Glasgow, 2007. / MSc(R) thesis submitted to the Faculty of Information and Mathematical Sciences, Department of Computing Science, University of Glasgow, 2007. Includes bibliographical references. Print version also available.

Thesis (Ph.D.)--University of California, San Diego, 2007. / (UMI)AAI3264605. Source: Dissertation Abstracts International, Volume: 68-04, Section: B, page: 2446. Adviser: Yoav Freund.

Thesis (Ph.D.)--University of California, San Francisco, 2007. / Source: Dissertation Abstracts International, Volume: 68-07, Section: B, page: 4209. Adviser: Patricia C. Babbitt.

Thesis (Ph.D.)--University of California, San Francisco, 2007. / Source: Dissertation Abstracts International, Volume: 68-04, Section: B, page: 2161. Adviser: Andrej Sali.

Thesis (Ph.D.)--University of California, San Francisco, 2007. / Source: Dissertation Abstracts International, Volume: 68-04, Section: B, page: 2165. Adviser: Hao Li.

Thesis (M. Phil.)--University of Hong Kong, 2009. / Includes bibliographical references (leaves 60-65). Also available in print.

Thesis (Ph.D.)--University of California, San Francisco, 2008. / Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: 4507. Adviser: Hao Li.

Thesis (Ph.D.)--University of California, San Francisco, 2008. / Source: Dissertation Abstracts International, Volume: 69-09, Section: B, page: 5135. Adviser: Bruce R. Conklin.