Rosana Molina

Rosana Molina

Ph.D. candidate in neuroscience, Department of Microbiology & Immunology

Rosana Molina studies fluorescent proteins—tiny biological lights that serve as markers to see otherwise invisible things under a microscope. Fluorescent proteins can help to illuminate brain cell activity in small model animals. Neuroscientists are then able to see this activity deep inside the brain with a special type of microscope called a two-photon microscope. The goal of Molina’s research is to make fluorescent proteins brighter for this specific type of microscopy. With brighter "lights," it is possible to make measurements of many more brain cells. This extra information leads to a better understanding of how the brain works and better ways to treat and prevent disease.

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Angela Patterson

Angela Patterson

Ph.D. candidate in biochemistry, Department of Chemistry & Biochemistry 

Angela Patterson is using biophysical techniques to add to the understanding of how bacterial cells fight off viral infections in an adaptive manner and how viruses respond to these adaptive immune systems. CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) systems have gained a lot of attention lately for their ability to be used as gene editing tools. An exciting recent development in the field was the discovery of virus encoded anti-CRISPR systems. The interplay between CRISPR and anti-CRISPR machinery highlights the battle between cells and viruses for control over infection and replication. Understanding the interplay between CRISPR systems and anti-CRISPR proteins can help make gene editing using CRISPR systems and phage therapy more effective.

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Allison Theobold

Allison Theobold

Ph.D. candidate in statistics, Department of Mathematical Sciences

Allison Theobold studies the computing skills necessary for graduate students in the biological sciences to implement statistics for research in their fields. The volume and variety of data collected by researchers in the biological sciences for statistical analysis continues to increase at a rapid pace, but the computational preparation of graduate students lags behind. Only 56 percent of students claim a basic skill level in statistical computing applications, due to the lack of computational preparation in their curriculum. Theobold's research identifies the key skills these graduate students are using in their research to implement statistics and uses this knowledge to develop a suite of statistical computing workshops. These workshops aid in alleviating the computational burden these students face and provide campus-wide resources for incorporating data science into the classroom. 

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