2025 Schedule
What is a hackathon? and other FAQs
Hackathon team roles
Code of conduct
Background reading
In the Antibody Engineering Hackathons, participants work together in teams to develop research projects that undergraduate students can carry out in courses where they study biotechnology or related topics. Hackathon participants will learn about the uses of antibodies in diagnostics, research, and therapeutics, develop new skills in immunology-related bioinformatics programs and databases, learn about new laboratory techniques for working with antibodies, and become part of an exciting community. Faculty and students with varying levels of experience in working with antibodies are encouraged to apply. Participants will be selected based on experience and motivation to attend. Applicants from community colleges will receive the highest priority.
Please note:
- This is not a competition. We will approach science as a collaborative endeavor.
- Some projects involve coding, some do not
- This will require a full-time, 8 hrs/day, commitment
- Everyone can contribute to a project no matter what your background is like
This year, many projects will emphasize AI and biomanufacturing.
2025 Hackathon Projects:
- NISTCHO cells & Monoclonal Antibodies: Biomanufacturing with mammalian cells, (ongoing, level = medium - advanced),
Team leaders: Margaret Bryans, Montgomery County Community College, PA and Lori Kelman, Montgomery College, MD
Description:
The NISTCHO team creates projects around characterizing NISTCHO cells. NISTCHO cells are a reference material for standardizing and comparing methods for antibody biomanufacturing. These cells were developed by the National Institute for Standards and Technology (NIST). They produce an antibody to a protein from Respiratory Syncytial Virus. Last summer, the NIST-CHO hackathon team identified multiple research projects that might be used to characterize this new resource.
This summer the team will pursue ongoing questions around characterizing this cell line and the antibody it makes. The team is engaged in researching cell line stability, assays for titer determination, and developing the materials for an ELISA to quantify the antibody. The team will also investigate how AI might be used in conjunction with antibody manufacturing. Many of these projects will benefit NIST, the biotech industry, and biotech workforce education.
Protein Production in Algae: Biomanufacturing with algae, (new, level = beginner - advanced) - *wet lab option for Houston participants
Team leader: Daniel Kainer, Lone Star College, Houston, TXDescription:
Several different kinds of cells are used in biotechnology for producing proteins. Each commonly used system–insect cells, mammalian cells, E. coli, and yeast–has its own advantages and drawbacks. The outer membrane of E. coli for example, also called endotoxin, can induce shock and medical distress if therapeutic proteins are made in E. coli and endotoxin contaminates the final injectable drug. Since algae, like Spirulina, have long been consumed as health supplements, some companies have started to make antibodies in algae as a safe way of delivering antibodies to the gut.
This project will explore protein production in algae, with the goal of understanding how antibodies may be produced in algae as well. This summer, the team will continue investigating protein production in Spirulina as a milestone on the path towards antibody production.
- Antibodies & AI: Protein engineering and biomarker discovery, (level = medium - advanced, **These projects involve coding.)
Team leaders: Todd Smith, Digital World Biology, WA and Eric Olson, Borealis, WA
Description:
The use of machine learning and artificial intelligence in predicting protein structure models has exploded in the past three years. These teams will be looking at: ways to help instructors and novices understand machine learning and eventually use machine learning for predicting the best ways to modify antibodies, including the use of RFDIffusion.
Another topic that this team will address is the use of machine learning to measure gene expression and predict the activities of T cells. The team will look at gene sequences from clonally expanded (active) T cells and test whether they can be used to predict cancer prognosis.
View Antibody Engineers Git Hub and NCBI Antibody Engineers Git Hub to learn more.
- Antibodies vs Cancer: Cancer antigens and personalized medicine, (ongoing, level = medium - advanced)
Team leader: Erica Lannan, Prairie State Community College, IL
Description:
This team will investigate antibodies from the perspective of their epitopes. The team will look at CEDAR (the Cancer Epitope Database and Analysis Resource) and explore how CEDAR might be used with AI. Potential CEDAR projects are related to neoantigens, biomarkers, and personalized medicine.
Participants may use the iCn3D molecular modeling tool, the SabDab antibody database, and viral sequence database tools including nextstrain.org and influenza virus resources from NCBI to align new variants to the known structure(s) of current antibodies. Using the interactions tool within iCn3D, participants will make predictions on the strength of antibody binding to new variants through the gain or loss of various bonds.
- AI as a research assistant: Drug discovery and development, (ongoing, level = medium - advanced)
Team leader: Dylan Bulseco, Institute for Future Intelligence
Description:
This team will be combining a Large Language AI model (LLM) with the use of agents that go out and gather data from databases.
- Immunology Poker: Immunology, Game design, (new, level = beginner - advanced)
Team leader: Melanie Stegman, Maryland Institute College of Art, MD
Description:
Stegman is the creator of Immune Defense, a video game based on immunology. This team will iterate on the design of a card game that is built on the way antigens and antibodies interact. The idea is to create a deck of cards that can be used for several different games. Each card represents a type of amino acid and the game (so far) is based on T-Cell activation in the Thymus.
- Nature's BARDS AI, sequence analysis and transformation, (ongoing, level = medium - advanced)
Team leader: Thomas Onorato, LaGuardia Community College, NY
Description:
Can sound be used to identify nanobodies that bind to antigens from sea stars? This team will investigate converting DNA and protein sequences into sound/music and determine if the musical patterns can be used to characterize nanobody and antigen binding interactions.
- The A team databases, student research, antibody structure and function (ongoing, level = beginner)
Team leader: Kristi Martinez, Bonney Lake High School, WA
Description:
This team will be building a set of resources to support high school student research projects. While high school teachers and students have always shown interest in Antibody Engineers Hackathons, it's tough to know where to start and without a strong foundation, successful participation is a challenge. We will be creating and compiling background materials and procedures around using databases like SAbDab, BLAST, and iCn3D that high school students can use to design an open-ended student project that might very well be their next hackathon team.
- Other projects: Other projects may be added. Want to try something with nanobodies? Let us know! If you have a project in mind that you would like to do, please consider volunteering as a team leader. The team leaders we have, the more projects we can do.