The iCompBio REU in 2024 tentatively is from May 21 to July 30, 2024.
10 weeks in-person REU
(remote participation and flexible schedules can be accommodated)
Summer 2024 REU application is now open
The about 10-week iCompBio REU focuses on interdisciplinary computational biology training to undergraduates in science, technology, engineering, math, and other related fields. Please look through the potential research projects and then choose your preferences of the potential research topics.
- $6,250 stipend + housing and meal plan
- Travel allowance up to $500
- Inter-disciplinary training in computer science and biology
- Coding boot camp.
- Diverse range of research topics.
- Chattanooga is a great outdoor city
A one-week computing bootcamp will be provided to teach students essential data science using R, and advanced data processing and deep learning methods using Python. Each student will be jointly mentored by a computer science mentor and a biology mentor. The computing training includes coding, modeling, simulation, deep learning neural networks, computer vision, parallel computing, statistics, data visualization, and mobile App development.
Student application includes an application form, transcripts, a personal statement, a resume, preferences of research topics, and at least 2 letters of recommendations. All application materials will be upload through an online-application form. Student applications will be reviewed by a team of faculty research mentors. (Please note that the application form is a Google-form whichl requires a Gmail account to complete).
Summer 2024 REU application is now open
Apply for the Summer 2024 ICompBio REU
Review of applications will start on March 1, 2024.
Please be aware the fund of the 2024 REU program is still pending, and we will update status of the 2024 iCompBio program as best as we can.
For more information about the program, please email: [email protected]
PI, Dr. Hong Qin
Co-PI Dr. Yingfeng Wang
High-lights of Past REU Participants
- Ledesma, Dakila, et al 2020, Application in Plant Sciences
- Baldwin, Q. and Panagiotou E., 2021, The local topological free energy of proteins (submitted) [link to preprint]
- Baldwin, Q., Sumpter, B. G. and Panagiotou E., 2021, The local topological free energy of the SARS-CoV-2 spike protein (submitted) [link to preprint]
- Zimmerman, Dell, Goldwater Scholarship, 2020
- Falvey, Cleo, NSF BIOREU blog, 2020
- Isimeme and de Blonk, NSF BIOREU blog, 2020
- Zimmerman, Dell, NSF BIOREU blog, 2020
Potential iCompBio REU Research Projects.
Systems biology of aging and deep learning of biological big data (H. Qin)
Why individuals with similar genetic makeup live to different lifespans? How can we extend health lifespan? Dr. Qin’s lab studies these questions through mathematical formulation and computational analysis of genomics data to generate predictive models based on gene/protein networks. Dr. Qin’s group also develop various machine learning tools to extrapolate cellular lifespans from time lapsed microscopic images. Students in this projects will learn advanced network modeling and analysis tools, deep learning methods, and computer vision.
Computational models for transport of nanofertilizers in soil (S. Palchoudhury)
Engineered magnetic nanoparticles like iron oxide have huge potential as agricultural fertilizers. Dr. Palchoudhury’s team has developed an experimental protocol based on multi-method material characterization in house at UTC to simultaneously assess the ability of iron oxide NPs to enhance seedling growth and its associated environmental risk.
Students will develop a suitable computational model for assessing the transport of iron oxide nono particles in soil through three specific deliverable objectives: 1) Experimentally characterize the transport of iron oxide NPs using a packed bed column of Ottawa quartz. 2) Run different particle transport computational models relevant to our NP size and concentration. 3) Statistically compare the experimental and computational results to predict a best suitable model.
Molecular dynamics studies on bacterial cell membranes and associated proteins. (B. Harris)
The membranes surrounding a bacterial cell are responsible for mediating processes such as cellular recognition, signal transduction, and the transportation of ions and molecules across the membrane. Further research into these processes is essential in order to advance our understanding of how pathogenic bacteria sense and adapt to their environment. Students working in Dr. Harris’s lab will conduct molecular dynamics studies of bacterial cell membranes and analyze how membrane phospholipid remodeling contributes to survival and persistence. This research is essential in order to advance strategies for the prevention and treatment of diseases caused by bacterial pathogens.”
Use remote sensing and GIS to study the impact of urbanization on surface water quality (A. Hossain)
The aim of this research is to use remote sensing and GIS to determine whether increased urban development in Hamilton County has had a negative effect on surrounding water quality and if this interaction is a concern for urban sustainability. Students will learn to perform various machine learning methods to analyze geo-location, environment al and weather related data.
Automatic assessment of soil-borne fungal growth rates using time-lapse images ( D. Beasley)
A critical goal of modern ecology is to determine how rapid climate change will influence ecological interactions among species. Of these, fungal interactions are of particular importance as major drivers of population dynamics, community structure, and biological diversity. Here, we propose to work with undergraduate students to explore the effects of temperature on soil-borne fungi by quantifying growth rates using high resolution time-lapse images. In addition to understanding implications for insect-fungal interactions under changing ecological conditions, the project will provide students with computational research training.
Mathematical and computational modeling for infectious diseases (Jin Wang)
Infectious diseases remain a serious public health burden throughout the world, leading to high morbidity and mortality every year. Mathematical modeling provides a theoretical tool to investigate the mechanisms of infectious diseases and offers useful guidelines for the design of control strategies. In this project, REU students will learn various techniques in constructing mathematical models, analyzing disease dynamics, and numerically simulating disease transmission and spread. In addition, the developed mathematical and computational epidemic models will be possibly applied to realistic case studies.
A topological model for protein folding ( E Panagiotou)
Some proteins attain particular 3-dimensional conformations which are essential for their function. In this project, we will use tools from Mathematical Topology and Geometry to analyze the conformations of proteins and establish connections between their structure and function. To do this, we will study aspects of geometry and topology relevant to proteins and the implementation of these tools in python to analyze specific proteins from the Protein Data Bank.
Apply graph algorithms to ecological big data to identify key factors that influence mating and parental dynamics (H. Klug)
Students will use network control theory to develop novel computational tools that allow us to effectively utilize large datasets to answer complex, broad, and fundamental questions in ecology and evolution. Students will apply these tools to ask a fundamental question in evolutionary ecology: How do life-history traits, ecological conditions, and sociality interact to influence mating and parental dynamics?
Rare versus common plant species (J. Boyd)
The question of why some species are rare while others are common is enduring and has important implications for ecological theory, rare species conservation, and overall biodiversity. As part of a broad research agenda to help address this question, I am using network analysis – a computational tool that uses mathematical graph theory to link different concepts as a visual map – to characterize the body of research comparing rare and common plant species, an area that has not been comprehensively reviewed in nearly two decades. One of the aims of this work is to refine techniques to facilitate the use of network analysis to characterize broader areas of scientific research. REU students will learn and apply network analysis to explore an area of scientific research of their interest.
Addressing sampling biases in SARS-COV-2 genomic data (Ziwei Ma)
The standard genome-wide association study (GWAS) is developed for the random sampling procedure. For SARS-CoV-2 genomics data, the random sampling assumption is not well satisfied. We will explore several statistical and computational methods to measure and analyze the biases and then explore the association between genetic variation and weather conditions.
Mobile app and deep learning for plant species digitization (J. Shaw)
Large digital biology data sets are important for increasingly trans-disciplinary studies Dr. Shaw’s group led a recent large digitization effort of herbarium collections, aiming to digitize 886,373 vascular plant specimens housed in 12 separate herbaria in Tennessee. Students will participate in developing a field data collection program, designed by digitization staff to enforce input normalization. Students will learn to apply recurrent neural networks using the Python Keras library to detect sample classification anomalies using the large number of plan digital images.
Evolutionary plasticity of fatty acid pathways in microbial genomes (D. Giles)
The Giles laboratory has observed the effects of fatty acids in several bacteria of medical importance, including Vibriospecies, Pseudomonas aeruginosa, Acinetobacter baumannii, and Klebsiella pneumoniae . Our central hypothesis is that the conservation of fatty acid handling machinery stems from an aquatic origin. This project will address this hypothesis bioinformatically by phylogenetically mapping Gammaproteobacteria based on possession of genes encoding fatty acid transporters (FadL), fatty acid CoA synthetases (FadD), and acyltransferases (PlsB/C/X/Y). Characterizing the extent to which groups of this class of ecologically and medically important bacteria can import, activate, and assimilate exogenous fatty acids may reveal evolutionary insights.
Modeling, Monitoring, and Mapping of Urban Infrastructure (D Wu, Y Liang, and L Yang)
Real-time and automated modeling, monitoring and mapping of urban infrastructure, such as, roads, bridges, and subsurface utilities, are essential for maintainability and serviceability of these important assets. Dr. Wu’s team is interested in creating a digital twin of urban infrastructure by combining geophysical instruments, such as, ground penetrating radar (GPR), light detection and ranging (LiDAR), and hyperspectral imaging sensors, with computing technology, such as, artificial intelligence, augmented reality, and cognitive decision-making. The digital twin, considered as a digital replica of physical infrastructure, will be used to simulate, monitor, and optimize the operation and maintenance of those physical infrastructure assets. Students will learn the knowledge, skills, and tools related with cyber-enabled high-bandwidth sensing and information processing.
Biodiversity of freshwater fishes (F. Aldo)
Dr. Aldo wants to understand how and why species originate and diverge from one another, and focus on the diversity of freshwater fishes. His lab uses molecular (genetic and genomic) and computational tools, together with data collected in the field and natural history collections, to reconstruct the evolutionary history of species and test hypotheses about their evolution. His lab is also interested in methodological questions such as what are the best methods and data types to obtain accurate species trees.
Molecular dynamic studies on lysozyme modifications (J. Kim)
Dr. Kim’s laboratory is interested in benzoquinones (BQs) and their biological roles because BQs are available ubiquitously in the environment as free quinones, protein cofactors, or as metabolites of polycyclic aromatic hydrocarbons. BQs exhibit toxicity by reacting with cellular proteins and nucleic acids mainly through redox cycling. Recently, our lab discovered BQs can induce protein aggregation which is known to associate with neurodegenerative diseases. We will investigate how BQs interact with a model protein, using a molecular dynamics approach.
Cancer genomics (L. Gao)
Cancers are distributed unevenly across the body, but the importance of cell intrinsic factors such as stem cell function in determining organ cancer risk is unknown. Prom1+ Cell (a commonly used cancer stem cell marker) generative capacity is a major determinant of organ cancer risk. To understand what cell properties might dictate cancer risk, we use statistical modeling to explore how Prom1+ cell population size, proliferative capacity, and generative capacity in each organ related to susceptibility to tumorigenesis.
This figure shows that generalized linear mixed models with a logit link (GLMM_LL) of tumor probability versus Prom1 cell generative capacity, prom1 cell population size and prom cell proliferative capacity (A-C). Numbers at top of each graph indicate the regression coefficient and p value of each as well as the AIC score (D and E) GLMM-LL iterative multivariable modeling of tumor probability versus Prom1 cell generative and proliferative capacities in adult and neonatal tissues.
Biodiversity estimates of microscopic organisms in the Tennessee waters (F. Leasi)
This research aims to investigate biodiversity shifting of microscopic organisms (invertebrates, bacteria, fungi, protists, etc.) in changing environments, with a focus on freshwater. Freshwater ecosystems are under intense pressure from overuse, pollution, and habitat degradation. Climate change is predicted to add additional threats and interact in complex ways with other stressor types, such as eutrophication. Consequentially, the distribution of sensitive freshwater species is expected to change. This is especially true for microscopic organisms, which are abundant and ubiquitous in aquatic ecosystems, performing key functions such as nutrient cycling and sediment stability. Yet, their unexplored diversity and response to disturbances represent one of the major challenges in biology and currently limits our capacity to understand, mitigate, and remediate the consequences of pollution and environmental change. In this project, students will integrate field investigation, genetics, genomics, and computational approaches to test the following hypotheses: 1- biodiversity of microscopic organisms is correlated to environmental conditions; 2- biodiversity shifting can be predicted in changing environments; and 3- the interaction invertebrates and microbial communities significantly affect biodiversity shifting.