Finding applications on Apple’s iOS device Home screen is a difficult task since applications are arranged in a disorganized grid of icons and small labels. By “jailbreaking” an iOS device, it is possible to install third party “tweaks” that modify the operating system to customize and fix annoying aspects of iOS. Current jailbreak tweaks exist that can launch applications differently than Apple’s stock Home screen, but they leave much to be desired in terms of functionality, usability, and aesthetics. HomeList is a watchOS-inspired tweak I created to add an easy to read, quick to navigate, and visually appealing list of applications integrated directly into the Home screen. Research into Apple’s private iOS frameworks was used to figure out how to perform tasks required by an app launcher as well as match iOS design aesthetics.

While techniques for reading DNA in some capacity has been possible for decades,

the ability to accurately edit genomes at scale has remained elusive. Novel techniques

have been introduced recently to aid in the writing of DNA sequences. While writing

DNA is more accessible, it still remains expensive, justifying the increased interest in

in silico predictions of cell behavior. In order to accurately predict the behavior of

cells it is necessary to extensively model the cell environment, including gene-to-gene

interactions as completely as possible.

Significant algorithmic advances have been made for identifying these interactions,

but despite these improvements current techniques fail to infer some edges, and

fail to capture some complexities in the network. Much of this limitation is due to

heavily underdetermined problems, whereby tens of thousands of variables are to be

inferred using datasets with the power to resolve only a small fraction of the variables.

Additionally, failure to correctly resolve gene isoforms using short reads contributes

significantly to noise in gene quantification measures.

This dissertation introduces novel mathematical models, machine learning techniques,

and biological techniques to solve the problems described above. Mathematical

models are proposed for simulation of gene network motifs, and raw read simulation.

Machine learning techniques are shown for DNA sequence matching, and DNA

sequence correction.

Results provide novel insights into the low level functionality of gene networks. Also

shown is the ability to use normalization techniques to aggregate data for gene network

inference leading to larger data sets while minimizing increases in inter-experimental

noise. Results also demonstrate that high error rates experienced by third generation

sequencing are significantly different than previous error profiles, and that these errors can be modeled, simulated, and rectified. Finally, techniques are provided for amending this DNA error that preserve the benefits of third generation sequencing.