Science Research Initiative

COVID-19 Research STREAM

Frederick R. Adler, Professor of Mathematics and Biological Sciences
Lindsay Keegan, Assistant Professor of Epidemiology

In addition to disrupting about every aspect of normal life, the COVID-19 epidemic has brought unprecedented attention to the importance of mathematical modeling and data analysis. The tools needed to understand and predict this epidemic run the gamut from differential equations and large simulations, with methods coming from statistics and applied mathematics. Data are noisy and complicated, and raise many questions about the challenges of counting cases, tracking their sources, understanding viral spread, and quantifying stresses on the health care system and the economy.

We will access the vast quantity of available data, and use them to study the spread and genetics of this virus. Recent studies have shown that the spike protein, that lives on the outside of the virus and is critical for it to enter cells, has mutated in ways that might affect its ability to infect people.

Our SRI team will take an interdisciplinary approach to this aspect of the pandemic. Students will learn the skills needed to download and visualize genetic data using R and python, link these data with fundamental mathematical models of epidemiology, evolution, and the physics of viral entry. Working in teams, we'll investigate hypotheses about the causes consequences of viral evolution, and learn to effectively communicate and display these results to audiences ranging from scientists and decision-makers to the general public.



>> Apply Now <<


Biological Invaders

Science Research Initiative

Fox Squirrel Biology Research STREAM

Denise Dearing, PhD, Distinguished Professor, School of Biological Sciences
Tess Stapleton, PhD Candidate, School of Biological Sciences

Biological invaders are one of the key drivers of ecosystem change. Invasion can result in loss of native species, reduction of ecosystem diversity, and even loss of ecosystem services such as soil stabilization, water filtration, and natural pest control. These disturbances can cause long-term disruptions and even extinction of native species. Therefore, it’s imperative to understand the effect of invaders if we wish to preserve local ecosystems.

For the last hundred years, the fox squirrel (Sciurus niger) has used human urbanization to spread out of its native eastern range and to invade the western United States. In 2011, these invasive squirrels were first spotted in Utah along the Jordan River Parkway and have since spread into the Salt Lake Valley.

This area is home to two native Utah species, the rock squirrel (Otospermophilus variegatus) and the red squirrel (Tamiasciurus hudsonicus). How far these fox squirrels have spread throughout the valley, whether they are moving into the mountains, and how they affect these native species remains unclear.

The goal of this project is to determine the current range of this invasive squirrel, including how much they overlap with native species and how far east and upslope they extend. Working in collaboration with the natural history museum we will document sightings, collect voucher specimens, and prepare study skins of fox squirrels. This project will greatly contribute to ongoing work on the spread of these invasive animals and these specimens may be used for decades to come.



>> Apply Now <<


Neural Networks

Science research initiative

Neural Networks Research Stream

Braxton Osting, Associate Professor of Mathematics

The abundance of data created in science, engineering, business, and everyday human activity is simply staggering. This data is often complex and high-dimensional, taking the form of video or time-dependent sensor data. Machine learning methods allow us to understand such data, automatically identifying patterns and making important data-driven decisions without human intervention. Machine learning methods have found a wide variety of applications, including providing new scientific insights and the development of self-driving cars.

One machine learning method in particular, neural networks, has emerged as the preeminent tool for the supervised learning tasks of regression and classification. Loosely modeled after the human brain and the basis for deep learning, Neural Networks use composition to develop complex representations of data. In recent years, researchers using Neural Networks have made tremendous breakthroughs in topics as varied as image processing, natural language processing, and playing board games such as Go.

Undergraduate students participating in this SRI stream will be introduced to neural networks and learn how they're used. Students will learn about the mathematics that forms the basis for neural networks and the optimization methods used to train them. They'll learn how to program in python and use machine learning packages such as scikit-learn and TensorFlow to analyze data. Working in teams, students will use Neural networks to solve real-world classification problems like object recognition in images, detecting falsified financial transactions, and controlling for manufacturing defects. They'll also learn to effectively communicate and visualize results.



>> Apply Now <<


Cellular Biology

Science Research Initiative

Cellular Biology Research STREAM

Markus Babst, PhD, Associate Professor, School of Biological Sciences

The basic driving forces of evolution, mutation and selection, are often difficult to observe and study in the laboratory because it requires large numbers of individuals and long time periods in order to observe the evolutionary changes.

During our research we found that the yeast knockout collection (~4200 strains each containing a deletion of a non-essential gene) is a treasure trove for evolutionary and cell biological studies. Genome sequencing of several of these mutant strains in my lab identified additional mutations in each mutant that help the strain cope with the initial gene deletion.

These suppressor mutations most likely arose during the copying of the original knockout collection, during which strains with suppressor mutations had the opportunity to outcompete the original mutant strain. Knowledge of the suppressor mutations can give insights not only into evolutionary mechanisms but also into the interactions between different cell biological pathways (systems biology).

The goal of the project is to systematically analyze the genomes of yeast deletion strains propose a mechanism of phenotypic suppression and test the model. Furthermore, we will perform evolutionary experiments in the lab to identify the factors that are responsible for the rapid genetic adaptation of yeast.



>> Apply Now <<


Electrosynthetic Chemistry

Science Research Initiative

Electrosynthetic Chemistry Research STREAM

Shelley Minteer, PhD, Associate Chair of Chemistry
Henry S. White, PhD, Distinguished Professor of Chemistry

Chemists and engineers strive to develop safe, efficient, and environmentally sustainable chemical synthesis for the production of high-value molecules, such as those used in medical applications. Advances by electrochemists have demonstrated remarkable new means for improving product selectivity under mild reaction conditions. Unexplored realms of chemical synthesis are now attainable using electrons at the primary reactant.

Supported by the National Science Foundation Center for Chemical Innovation, chemists at the University of Utah and across the country are embarking on a collaborative project to employ the extensive knowledge electrochemists, materials scientists, and physical chemists in using electrons to make new molecules.  The overarching goal is to deploy this exciting new knowledge to advance chemical synthesis.

Undergraduates participating in this SRI project will demonstrate how using electrons as reactants can make pharmaceutical synthesis greener, safer, and environmentally friendly. Students will work towards learning advanced electrochemical methods for carrying out chemical transformations.  Working as a team, they will participate in designing a research plan for developing a general electrochemical route for introducing chemical functionality into molecules, and then demonstrate the general application of their method in the chemical syntheses of a series of molecules.

The project will provide students with a working knowledge of many aspects of organic preparatory chemistry, the physical chemistry of electron-transfer reactions, catalysis, materials chemistry, and quantitative analytical measurements, providing a foundation for future advance research in all areas of chemistry.  Biweekly meetings of the entire team with the project leaders (Profs. Minteer and White) will focus on discussion of individual student results and the overall progress of the team.

Communication skills and scientific writing will be emphasized as part of the project.  Undergraduates from all areas of science and engineering are encouraged to apply.


>> Apply Now <<