Cancer is a leading cause of death worldwide with around one in every six caused by cancer, but many cancers can be cured if treated properly. Mathematically programmed cancer cell models can be used by researchers to study the use of oncolytic viruses to treat tumors. With these models, we are able to help predict the viral characteristics needed in order for a virus to effectively kill a tumor. Our approach uses both cancerous and non cancerous cells in relationship to the tumor to determine the speed at which the cells replicate, however there are several models used to describe cancer growth, including the Exponential, Mendelsohn, Logistic, Linear, Surface, Gompertz, and Bertalanffy. We study how the choice of a particular model affects the predicted outcome of treatment.

The first galaxies formed 12.5 billion years ago during the first billion years after the Big Bang. However, these first, faint, galaxies remain too faint for direct detection, even by our most powerful telescopes. Therefore we study them using their fossils relics, ultra-faint dwarf galaxies orbiting the Milky Way. In this work, we look at the histories of star formation in simulated analogs to the ultra-faint dwarfs. These star formation histories will allow us to study the details of how and when star formation occurred during the first billion years of cosmic time. We are particularly interested in how massive the first galaxies were when they formed the majority of their stars.

Coinfection affects up to 60% of patients hospitalized influenza-like illnesses, however, the role of the innate immune response in coinfections is not understood. Interferons, part of the innate immune response, are a type of chemical released by infected cells that can help establish an antiviral state in cells by increasing resistance to infection and reducing production of viruses. Although the increased resistance to infection can help suppress both viruses, the reduction in the production of one virus may aid in increasing the growth of another virus during coinfection due to less competition. We will use a mathematical model to examine the interaction via interferons between respiratory syncytial virus (RSV) and influenza A virus (IAV) during coinfections. This model will measure viral titer, duration of the viral infection, and interferon production allowing us to understand how interferon production of one virus helps or hinders the secondary virus.

Due to the enormity of different forms of cancer and the increase in cancer rates globally, it is essential to continually develop more advanced methods of early and localized detection of cancer cells, as well as methods of targeted drug delivery. As a result, a vast amount of research has gone into the use of nano-materials such as graphene quantum dots (GQDs) as the basis for a wide variety of biomedical sensing and treatment applications. While many diagnostic biomarkers have been detected using modified GQDs, one biomarker that has not yet been successfully detected or targeted using GQDs is Transgelin-2. Transgelin-2 is a unique actin-binding protein that has been projected to be a useful biomarker and target of treatment for many different forms of cancer, as well as asthma and immune diseases such as lupus. Herein I review the structure of the Transgelin-2 protein, novel methods of GQD modification to sense cell membrane surface proteins, and ultimately determine the viability of GQDs as a method for detecting and targeting Transgelin-2. Furthermore, I develop a possible methodology by which these biophysical applications may be tested.

Antimicrobial action of micro- and nanoscale ZnO particles has been documented, but the fundamental physical mechanisms driving these actions are still not identified. We hypothesize that one of the key mechanisms behind the antibacterial action of ZnO is rooted in interactions between ZnO surfaces and extracellular material. An investigation was done of the biological components of that interaction using diffusion theory and more specifically Brownian motion computational models to look at the interaction of Zn+2 and O-2 ions with staphylococcus aureus bacteria. The analysis allowed us to find a correlation between the thickness of the staphylococcus aureus bacteria and the amount of the zinc and oxygen ions present in the solution.