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Participants will be assigned a REU mentor based on their application materials. In each mentor laboratory, students will pursue an individual, authentic research question, which will foster independence and ownership in their project. Students will individually contribute to the inquiry process, including developing the research question, designing and conducting experiments, data analysis and presentation. Students will also participate in discussions of literature, experimental design and data analysis with faculty.

Current Mentors and Research Projects

Structural Biochemistry and Enzymology; Chris Berndsen, Associate Professor of Chemistry & Biochemistry: Dr. Berndsen’s lab focuses on the structure and mechanism of ubiquitin/ubiquitin-like proteins utilizing enzymes and plant beta-amylases. REU students will purify proteins and assay activity followed by collecting X-ray data on their selected protein in a variety of conditions. Where structural information is limited, students will use bioinformatic approaches to identify similar sequences/structures, to predict structure, and then study the dynamics. Students will combine their experimental results with their computational studies to connect changes in structure to mechanism and function. Most data sets are analyzed using R programming and Python is used to script the setup and execution of simulations and modeling.

Computational Neurobiology; Corey Cleland, Associate Professor of Biology: Dr. Cleland’s lab explores sensorimotor integration in invertebrate and vertebrate model systems using high-speed video, computer simulations, and computational analysis in Matlab. Students will design individualized, independent projects involving predator and/or prey behavior in jumping spiders, one of the laboratory’s principal model systems. Students will incorporate high-speed video (big data), off-line video tracking (image processing) and computational approaches (analytics) in their research.

Molecular Analysis of Nociception; Susan Halsell, Associate Professor of Biology: In Dr. Halsell’s lab students will utilize ImageJ analysis of behavioral changes in genetically modified Drosophila larvae to study protective behavioral responses to noxious stimuli. Students will process videos for each mutant type (image processing), compiling them to create a single data set (big data). Data analytics will determine the significance of behavioral changes. Students will use qPCR to analyze changes in RNA expression patterns (bioinformatics). 

Molecular Mechanisms of Transcriptional Repression; Casonya Johnson, Professor and Head of Biology: Dr. Johnson’s lab studies the mechanisms by which the Hairy/Enhancer of Split (HES) family proteins repress transcription using C. elegans as the experimental model.  Students will use ChIP and real-time PCR tools to examine the effects of HLH-25 binding on H3K4 methylation at three HES target genes and use programming in R and Python to predict the H3K4 methylation status at other HES protein targets.

Quantitative Cell-Substrate Mechanobiology; Kristopher Kubow, Assistant Professor of Biology: Students in Dr. Kubow’s lab will design independent projects investigating different aspects of the interplay between migration and extracellular matrix (ECM) synthesis. Students will conduct experiments using simple cell-substrate model systems already established in the laboratory, high-content light microscopy with digital imaging (big data), quantitative image processing (image analysis), and computational analysis (R programming).

Cluster Analysis of Acoustic Data; Dana Moseley, Assistant Professor of Biology: Dr. Moseley focuses on how sensory ecology integrates acoustic analyses of big data with field ecology across an urban gradient focusing on avian communities. Students will conduct field work to quantify noise and bio-acoustic signals in the sound environment and perform cluster analysis (data analytics) to detect species through machine learning. Individual interests of students will shape their specific questions addressing topics from anthropogenic noise to the acoustic ecological community.

Integrative and Quantitative Chemical Ecology; M. Rockwell Parker, Assistant Professor of Biology:  Students in Dr. Parker’s lab will analyze the role of chemical signals used by reptiles in sexual communication. The role of these signals in behavior is assessed using video image analysis and multivariate statistics programmed in R. Students will be offered a choice between designing a data-based project using either a large, multi-year data set (big data) on garter snake pheromone evolution or bench-based empirical studies extracting, analyzing, and designing tests for the pheromones of Burmese pythons. Students will receive hands-on experience with animal behavior analysis with the potential to design artificial intelligence or machine learning approaches for extracting data from videos.

Visual Neuroscience; Marquis Walker, Assistant Professor of Biology: Dr. Walker’s research focuses on identifying novel mechanisms that underlie photoreceptor cell death and retinal degeneration in the eye. By studying this critical cellular mechanism we hope to identify potential therapeutic targets that help to restore photoreceptor function in retinal degenerative disorders. Students will design and test loss of visual function experiments in transgenic retinal degenerative mouse models by recording electroretinograms measuring changes in protein expression (big data), and visualizing protein localization (image analysis), in the retina.

Functional Morphology of Primate Locomotion; Roshna Wunderlich, Professor of Biology:  Dr. Wunderlich uses inertial sensor technology to quantify primate locomotion and energy expenditure at different stages of the life cycle and under different environmental conditions. Students will work on individualized projects in which they will develop independent questions within the larger research project, observe and collect data on lemur muscle physiology and/or the locomotor dynamics of lemurs obtained previously at the Duke Lemur Center, and interpret locomotor biomechanics and energetics using modern computational approaches. Students will work with large datasets of video and inertial sensor data (big data) and will have the opportunity to develop machine learning algorithms (data analytics) to identify behaviors in lemur species. 

Marine Microbial Ecology; Louie Wurch, Assistant Professor of Biology: Dr. Wurch studies the interactions among marine microorganisms, focusing on phytoplankton (microalgae). Using a combination of classic microbiology techniques, tools from molecular biology, and “omics” (bioinformatics), his lab seeks to understand the ecological niche of harmful algal species. Students will design and execute independent projects involving an aspect of harmful algal bloom research. Students will be involved in every aspect of the project, from initial design through presentation and publication of the data. Students will incorporate genomics and/or transcriptomics in their experimental design, which are powerful tools for understanding phytoplankton physiology and generate extremely large datasets (big data). Students will learn computational skills to handle, analyze, and visualize these datasets.

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