UC San Diego

Outbreaks of infectious diseases are rising around the world. In addition to outbreaks, several known infectious diseases have a significant impact on mortality in the US and around the world. In this doctoral work we use a combined experimental/computational approach to study the performance and limitations of digital High Resolution Melt (dHRM), an infectious disease screening platform that was previously established in the Fraley lab. This allows comparisonswith other microbiological technologies and helps shine some light on its potential use-cases. First, we developed a computational framework for estimating the resolving power of dHRM technology for defined sequence profiling tasks. By deriving noise models from experimentally generated dHRM datasets and applying these to in silico predicted melt curves, we enable the production of synthetic dHRM datasets that faithfully recapitulate real-world variations arising from sample and machine variables. Second, we present an advancement in universal microbial high resolution melting (HRM) analysis that is capable of accomplishing both known genotype identification and novel genotype detection. Specifically, this novel surveillance functionality is achieved through probabilistic modeling of sequence-defined HRM curves, which is uniquely enabled by the large-scale melt curve datasets generated using our high-throughput digital HRM platform. Our hope is that in the future, the dHRM platform can translate into a near-point of care, cost-effective tool for infectious disease screening.