Research Projects
Automation of Established Computational Approaches Examining Alterations in Cell Physiology
Mentor: Dr. Laura Harris
Gene expression profiling is a powerful technique for examining cellular activity across a genome from almost any cell culture and tissue sample. Dr. Harris has developed computational meta-analysis approaches to examine differential gene expression and pathway activity changes associated with altered states of cellular physiology (e.g., disease). In this project, the selected student will automate Dr. Harris’s computational approaches using established SARS, cancer, and/or microgravity data.
By the end of this project, the student will learn how to conduct an advanced bioinformatics analysis on RNA-seq data to identify, verify, and compare gene expression and pathway activity changes applicable to a wide variety of scientific questions. The student will be able to freely share his/her/their resulting code via repositories like GitHub and referenced more widely as used in upcoming biological research articles, both internal and external to Dr. Harris’s research group.
Students applying to this project should have familiarity with a programming language, preferably NumPy, SciPy, and Pandas in Python, and statistics such as z-score, T-score, and ANOVA.
Keywords: Bioinformatics, Computational Genomics, Software Development, Biomedical Data Science, Modelling
Computational modeling tools for predicting cardiovascular/heart diseases and treatments
Mentors: Dr. Lik Chuan Lee and Dr. Lei Fan
The Computational Biomechanics Lab has developed powerful computational tools for understanding cardiovascular/heart diseases and treatments in patients. These tools can overcome some of the limitations associated with experimental or clinical studies. In this project, the students will apply computational algorithms (implemented in high-performance computing environment) that are based on finite element modeling and optimization to investigate some cardiovascular/heart diseases such as coronary artery diseases and left branch bundle block. The students will also have a chance to work on developing graphic-user interface (GUI) that can help monitor and predict hemodynamics and heart function under different physiological (e.g., exercise) and pathological (e.g., ischemia) conditions in humans. These tools/models will be calibrated based on subject-specific data from animal experiments and the clinic (e.g., magnetic resonance images) with the intention for them to be translated into the clinic.
By the end of this project, students will be able to gain some knowledge in computational modeling of the heart, clinical/experimental data analysis and experience in developing powerful visualizations and user-friendly GUI. The students will take part in and contribute to active collaborative research projects, potentially leading to a publication.
Students applying to this project should be able to program using Python and/or MATLAB. Additionally, some knowledge in finite element method would be helpful (but not necessary).
Keywords: Computational Heart Modeling, Cardiovascular Diseases, Finite Element Method, Graphic-User Interface
Intelligent Social Network Interventions to Augment Human Cognition for Bolstered Inter-disciplinary Interactions in Project Teams
Mentors: Dr. Hanzhe Zhang
Can machine-learning interventions enhance team performance? We will analyze students and authentic project team email, meeting, interview, survey, and experiment data to find out.
By the end of this project, students will be able to clean and analyze data in Stata, R, Mathematica, Python, or z-tree. Using Stata or R, students will be able to analyze surveys and other tabulated data (like emails or transcripts) to produce descriptive statistics and measures of team productivity. Using Mathematica or Python, students will be able to produce social network diagrams and manipulate videos. Using z-tree or Python, students will be able to design and implement lab experiments to test whether the machine-learning interventions improve team performance (on real human participants). Given the multidisciplinary nature of the project, students have flexibility in the program they choose to produce the desired results.
Students applying to this project should know basic Excel, and are preferred to have program experience in Stata, R, Mathematica, Python, or z-tree (but not necessary).
Keywords: machine-learning, team interventions, social network analysis, experiment
3D Modeling of Mitochondrial Ultrastructure
Mentor: Dr. Jason Bazi
The project will involve the optimization of feature tracing and identification in cryo-electron tomograms of isolated mitochondria using IMOD, an open-source, cross-platform suite of modeling, display, and image processing programs used for 3D reconstruction and modeling of microscopy images with a special emphasis on electron microscopy data. During the project, the student will test out various filtering and averaging algorithms to improve image contrast and clarity to facilitate automated segmentation and feature identification.
By the end of this project, students will be able to construct detailed 3D tomograms of intact mitochondria from various conditions and states. The student will also learn a great deal about mitochondrial physiology and aid a current research program geared towards resolving the relationship between structure and function using biophysical-based computer simulations.
At the end of the summer, the student will produce a poster reporting their results and present their findings to the lab. Students applying to this project should be aware of signal and digital image processing techniques in MATLAB and/or Fiji.
Keywords: Mitochondria, cryo-EM, image processing
Modeling Brain Network Connectivity in Alzheimer’s Disease
Mentor: Dr. Andrew Bender
The project will focus on computational and statistical modeling of structural brain connectivity in aging and Alzheimer’s disease. Students will work with Dr. Bender and his students to apply graph theory analysis and machine learning, using methods in R and MATLAB, to model structural brain networks from magnetic resonance imaging (MRI) data.
By the end of this project, students will be able to implement different methods for brain network modeling and analyze network measures in prediction and classification of Alzheimer’s disease.
Students applying to this project should have some prior coding experience in R and/or MATLAB. Students from a wide range of academic backgrounds are encouraged to apply.
Keywords: Neuroscience, Alzheimer’s disease, graph network
Studying giant impacts during terrestrial planet formation
Mentor: Dr. Seth Jacobson
During terrestrial planet formation, the inner part of our solar system transformed from a disk of gas and dust to the 4 terrestrial planets, their moons, and the leftover asteroids that we observe today. In this project, the student will simulate the growth of planetesimals and planetary embryos in this disk considering different proposed planet formation scenarios. In particular, the student will be exploring the role of giant impacts and whether these putative violent collisions produced features observable in the main asteroid belt today.
By the end of this project, students will be able to work with state-of-the-art N-body accretion models to test hypotheses regarding the latest terrestrial planet formation scenarios. Students will develop skills in a GPU-accelerated C program using the NVIDIA CUDA toolkit as well as Python (or an equivalent language of their choice) for data analysis.
Students applying to this project should have basic programming skills and be familiar with the command line. They should also possess an interest in astronomy and planetary sciences.
Crop Field Phenomics and Genomics
Mentor: Dr. Addie Thompson
Physical characteristics of plants, or phenotypes, are influenced by both genetics and the environment. We are using drone and rover-based imagery and sensor data alongside genotypic sequencing and expression data to describe and model plant growth and development over time in order to figure out what genes are influencing the traits and predict how different varieties will grow in new environments. The Thompson Lab works primarily with field-grown maize and sorghum.
By the end of this project, students will be able to manipulate imagery and sensor datasets and extract features and use these features to build genotype-to-phenotype predictive models.
Students applying to this project should feel comfortable using R and/or Python, be willing to learn some geospatial analysis, understand the basic concepts of genetics and inheritance, have taken some course(s) in statistics and linear algebra, and have an interest in working with plants. There will be opportunities to work outside in the field to collect data if it is of interest, but this is not required.
Keywords: maize, phenotyping, quantitative genetics, machine learning, image analysis
Genome-centric bioinformatics analyses to understand microbiome functions
Mentor: Dr. Ashley Shade
Our overarching objective is to manage the plant microbiome to support crop resilience to global climate stress. In this bioinformatics project, the student(s) will analyze and integrate multiple types of high-throughput sequencing data to understand plant-microbiome functions and activities. Student(s) will discover functional genes and biosynthetic gene clusters expected to support a plant-associated microbial lifestyle.
By the end of this project, students will be able to: 1) implement different bioinformatic tools for genome and transcriptome analysis; 2) create a version-controlled computational workflow for integrative analysis and visualization of microbiome data; 3) prepare publication-ready bacterial genome announcement(s) to communicate novel findings.
No additional skills are needed; students applying to this project will be using various bioinformatic tools and the R environment for statistical computing. Prior experience in using any bioinformatic tool or in python programming will be viewed favorably.
Keywords: microbiome, genomics, bioenergy, plants, bioinformatics
Computing, visualizing, and analyzing deformation of the Earth caused by the global redistribution of water
Mentor: Dr. Jeffrey Freymueller
The changing weight of water causes the solid Earth to deform and change shape, by amounts that are small but actually measurable! Physical models allow us to compute how much the Earth deforms, making the Earth a kind of "measuring scale" to show how water moves about.
The student will work on developing and improving the computational workflow for using global or regional water mass distribution models that result from ongoing measurements, or from projections of climate change.
By the end of the project, the student will be able to manage data sets, run computational programs, and extract the results for plotting and comparison to data. Students applying to this project should be ready to learn how to execute code and write scripts in a command-line environment and/or Jupyter notebook (some things will be easier on the command line using unix tools). Familiarity with python would be useful.
Keywords: Global Change, deformation, GPS, data visualization, data
Earth’s Groundwater Microbiome and Environmental Change
Mentor: Dr. Matthew Schrenk
Distinctive microbial communities (or microbiomes) are present in natural and built environments throughout the Earth and are critical to their function and health. These microbial populations reflect the environmental conditions they experience and may retain these signals in their genetic composition. This project will evaluate the microbial diversity of groundwater through DNA sequence analyses in the context of associated chemical and physical data. These associations will allow us to observe and trace the impacts of contaminants and climate change at the surface as reflected in the composition of microbiomes as water moves underground.
Analyze DNA sequence data to characterize microbial diversity and to conduct statistical analyses to study relationships between groundwater characteristics and its associated microbiome.
Have a basic knowledge of biology and have experience working with R. Bioinformatics experience is useful but not required.
Keywords: Microbiome, environmental health, groundwater, contaminants
Membrane permeation of plant products with molecular simulation
Mentors: Dr. Josh Vermaas
Calculate the permeability for specific plant products across plant membranes, in conjunction with experimental researchers.
By the end of the project, the student will learn the basics of permeation theory, how to build and visualize molecular systems, run molecular dynamics simulations on high performance computing resources, and how to analyze the resulting trajectories to calculate permeability.
There are no pre-requisites for this opportunity beyond typical first-year science courses that introduce biological membranes.
Physical-informed modeling of complex multiscale dynamic systems from data
Mentor: Dr. Haun Lei
Despite the establishment of modern physics and its far-reaching impact on our understanding of complex physical systems, there are many systems still facing fundamental challenges, e.g., prediction of the nanoscale heat transfer relevant to the CPU design, the dynamics of protein folding, even a droplet of hand soap; conventional physical models show limitations for such systems. This summer project is designed to introduce some recent development of machine-learning based methods to directly learn the governing equations from data. One essential problem is how to ensure that such data-driven-based models strictly preserve all the physical symmetries and constraints.
By the end of this project, students will be able to get familiar with modeling systems with ordinary differential equations, data-driven reduced modeling, and symmetry-preserving neural network construction and training.
Students applying to this project should be able to program in python or Matlab.