Below are the projects for the Summer 2024 undergraduate research internships. These projects provide early research experience for undergraduates pursuing the science and engineering fields. To submit applications or questions, please contact Monica Lamm at mhlamm@iastate.edu with email subject "Summer 2024 research opportunity [Project name]." All opportunities are 8 weeks for 20 hours a week.
Project: Assessing Genome Accessibility and the Impact on Microbial Factory Performance Using CRISPR-Based Genome Editing Tool
Project scope: The long-term goal of the Shao group is to:
- Study the functional genomics of high-performing microbial species that have unique biochemical and biomedical potentials and
- Leverage these relatively simple and fast-growing testbeds to decipher critical steps in cellular commands, and eventually elucidate fundamental mechanisms in higher eukaryotes.
Shao group has established expertise in exploring the unique properties of non-model species as microbial factories to produce compounds with biopolymer, biolubricant, nutraceutical, and pharmaceutical applications. To promote the efficiency to exploit these non-model species, we gear our research towards elucidating systematic rules and developing platform technologies to create generic strain-engineering solutions.
Undergraduate research project: Genomes serve as scaffolds for transmitting information through both genetic and epigenetic means. Eukaryotes face an information packaging challenge because DNA molecules of each chromosome need to be folded within a tiny space in the nuclei. Accumulating evidence demonstrates that the spatial arrangement of the genomes in eukaryotes is far from random. We are interested in studying the influence offered by different genome contexts on heterologous pathway performance, which is directly correlated to the productivity when the host is engineered as a microbial factory to produce high-value chemicals (e.g., biofuels, biopolymer precursors and other compounds used as nutraceuticals and pharmaceuticals). The student will be exposed to frontier research topics such as high throughput DNA assembly and CRISPR-based genome editing technology, and also learn the techniques to generate recombinant DNAs, perform flow cytometry, and gene knock-in/knock-out.
Faculty mentor: Zengyi Shao
Project: AI-Guided Understanding of how Metals Interact with Proteins and Consequences in Human Diseases
Project scope: Chowdhury Lab is interested in understanding how viruses (like SARS CoV 2) thrive in the human body, how neurodegenerative diseases (like Alzheimer’s) spread, why military personnel have higher amount of lead in their blood, and how lithium therapy works in patients with bipolar disorder. By understanding how proteins interact with different metals, we leverage biology to solve larger engineering problems consequential to society spanning environment protection, magnet production, and even therapeutics. For example, we design novel proteins that selectively remove heavy metals from contaminated water, sequester rare earth lanthanide metals from electronic waste, or design antibiotics which deliver useful metals like Magnesium to the ailing patient.
Undergraduate research project: The student will work in close collaboration with PhD and postdocs in the lab and will be expected to run molecular simulations of metal protein interactions, analyze results, and participate in update writing, giving one talk in the internal group meeting and participate in manuscript writing (if applicable). Students with prior experience in python coding and biomolecular simulations are encouraged. Student willing to pick up experience in this area for the first time are also welcome – but the outcomes will depend on your current expertise.
Faculty mentor: Ratul Chowdhury
Project: Streamlined Protein Engineering Using Machine Learning and Cell-Free Expression Systems
Project Scope: Proteins are the biological machines powering life. These biological molecules carry out essential functions in your body and in nature, such as recognizing pathogens, giving your cells structural support, and producing the nutrients that your body needs to survive. Because of these properties, proteins are widely used in both industry and medicine to treat disease, develop new materials, and produce high value commodities like fragrances. To meet societal demands for new medicines and industrial products, efficient protein engineering workflows are required. Machine learning has been pitched as a way to more efficiently guide protein engineering through the unfathomably large design space (20100 for a 100 amino acid protein). However, current workflows suffer from tedious cell-based expression strategies which slow down the engineering process. The purpose of this research is to develop workflows integrating cell-free expression, a streamlined protein synthesis method, and the machine learning technique Bayesian optimization to speed up protein development cycles.
Undergraduate Research Project: It is proposed that the student accomplishes the following objectives during the 8-week program. There are two possible plans for weeks 4-8 depending on our current labs research progress by summer. The protein (enzyme more specifically) that is engineered will be finalized later, but the medically useful uricase is our fall back.
Weeks 1-3:
- Learn to apply existing lab workflows, liquid handling robots, and scripts to analyze the properties of 96+ protein designs at a time with cell-free expression. (~20 hours)
- Learn to write basic python scripts for file handling, data visualization (plotting experimental data with matplotlib/seaborn) and comparing protein sequences. (~40 hours)
Weeks 4-8 (if Bayesian optimization has not been integrated into workflows by summer):
- Develop python scripts integrating existing open-source algorithms for Bayesian optimization of proteins with enzyme screening data obtained during weeks 1-3.
- Use the Bayesian optimization algorithms (or genetic algorithms if unsuccessful) and experimental workflows learned during weeks 1-3 to carry out two rounds of Bayesian optimization experimentally. (Goal is to repeat this twice to test general workflow reproducibility)
Weeks 4-8 (if Bayesian optimization has been integrated into workflows by summer):
- Learn conceptually how different optimization algorithms work (Bayesian optimization and genetic algorithms) and how to implement these algorithms on protein screening data using scripts developed previously by the lab.
- Carry out optimization using the different algorithms experimentally (using workflows learned during weeks 1-3) to identify the best performing models.
Faculty Mentor: Nigel Reuel
Project: Microbial Production of Plastics for Additive Manufacturing
Project scope: Additive manufacturing techniques, such as 3D printing and photopolymerization, enable the production of complex components for use in transportation, manufacturing and biomedical applications. However, the materials used for additive manufacturing are often derived from petrochemicals and can have a high toxicity. As part of the Chemurgy 2.0 “Plastics for Additive Manufacturing” focal area, we aim to develop microbes that can produce (a) tri-functional fatty acids that can be polymerized and used in 3D printing; and (b) metabolites that can function as photoinitiators or synergists in photopolymerization applications.
Undergraduate research project: The undergraduate researcher will participate in the construction and characterization of microbes engineered for improved production of the target molecules.
Faculty mentor: Laura Jarboe
Project: Nanocellulose Fabrication Research Assistant
Project scope: We are working to optimize the fabrication of nanocellulose from agricultural sources. Our team is interested in reducing production costs, increasing output efficiency, and maintaining environmentally friendly production parameters. Nanocellulose is a high value product which can be created from nearly any agricultural source but is currently most often produced from lumber. The production process is similar to that for paper. We are exploring new types of feedstock, such as hemp production by the Meskwaki Tribe of Iowa. Using hemp discard would be much more sustainable, in that hemp replenishes much more rapidly than trees, grows readily in a variety of environments, and the cellulose extraction would not interfere with creation of other hemp-related products. Formation of nanocellulose is relatively simple but requires time and energy which makes production expensive. Current production costs make it far too expensive for any applications aside from military or space exploration. We seek to improve production efficiency to the point where we can use 3D printing or casting technologies to create nanocellulose materials that can replace unsustainable plastics, building materials, and catalytic agents.
Undergraduate research project: The student will focus on one of three areas depending on their particular interests and skills. In practice, students will focus on one of the areas but will be able to explore all three.
- The student will explore different cellulose extraction methods from hemp stalks and other agricultural by-products. This will involve work in chemistry/biochemistry to produce cellulose and nanocellulose materials.
- Mechanical and/or acid hydrolysis to break down microcellulose into nanocellulose fibers and crystals. The goal will be to optimize these processes to achieve nanocellulose products with desired features with minimal energy input and processing time. This project will involve using optical and electron microscopy for characterization and basic chemistry.
- This project involves fabricating nanocellulose products. This will involve freeze-drying, casting, and 3D printing nanocellulose suspensions. The goal here is to be able to minimize defects in materials production. Aside from production costs, the greatest challenge for creating products is creating uniform and homogenous solid materials.
Faculty mentor: Tim Kidd