Our Vision
Our team chose this project to address a major unmet challenge in neurodegenerative disease research: the lack of molecular tools that can prevent or reverse early-stage protein aggregation. ALS served as a model system because of its clear molecular hallmark, TDP-43 aggregation, and the opportunity to combine transcript-level and protein-level interventions. We wanted to build a project that bridged computational modeling and synthetic biology and created an adaptable framework that other teams could use for similar diseases.
By designing and testing antisense oligonucleotides (ASOs) and RNA aptamers in conjunction, we developed a two-tiered approach that targets both the production and pathological behavior of TDP-43. Our computational pipelines and analytical tools are intentionally accessible and reproducible, so that future teams can replicate or expand our work to explore other RNA-binding proteins or aggregation-driven disorders.
Reproducible RNA Design Models
We introduce our team's computational RNA therapeutic design toolkit to help iGEM teams design and evaluate antisense oligonucleotides (ASOs) and RNA aptamers that target disease-related proteins such as TDP-43.
ASO Development Pipeline
Our team modified a computational ASO design pipeline that can be easily adapted by other iGEM teams and researchers to target different disease-related genes. This model combined scoring, filtering, and optimization methods to identify the most effective ASO candidates before experimental testing. The model represents the preliminary step to make the ASO design process faster, cheaper, and more reliable, especially for early-phase research.
By refining and documenting each step, we created a reproducible framework that supports both experimental validation and future improvements. Therefore, our ASO design model provides other iGEM teams with a ready-to-use foundation for designing effective ASOs in their own projects across synthetic biology research.
Key Innovation: Our ASO design pipeline reduces time and cost by identifying optimal candidates computationally before wet-lab validation.
Machine Learning Model for Predictive Analysis of ASOs
We developed a machine learning model that predicts the efficacy of an ASO in knocking down a target protein. Our machine learning-based contribution includes:
- Standardized Dataset: Standardizes heterogeneous %KD labels and encodes sequence/chemistry features given the ASO
- Robust Model: A small-data-robust, gradient-boosting model plus simple one-shot family calibrations that correct bias while preserving sequence rankings
- Reproducible Evaluation: Pre-registered splits and out-of-distribution (OOD) family tests
Our machine learning model is therefore a transferable tool for future iGEM teams and the scientific community that connects biophysics to predicted ASO efficacy, thereby reducing screening burden and improving in silico testing of candidates before wet-lab experimentation (saving time and resources).
Aptamer Model
Our workflow combines sequence modeling, molecular docking, and a novel Aptamer Priority Index (API) which evaluates multiple biological features into a single ranking system: binding strength, stability, and interaction surface. These pipelines make candidate screening more straightforward and reduce the need for early wet-lab testing, making therapeutic design more accessible to student researchers. We hope to support future iGEM teams in developing their own RNA-based tools for addressing other complex diseases.
ASOmatic: Conceptual Hardware Framework
The ASOmatic, our hardware design, was created as a conceptual framework to inspire future iGEM teams and researchers to automate and accelerate antisense oligonucleotide (ASO) development. We designed it to address one of the biggest setbacks in RNA therapeutic research: the time-consuming and error-prone process of ASO design and validation.
By outlining a system that combines protein sequencing, computational ASO prediction, and efficacy scoring into one streamlined platform, the ASOmatic demonstrates how automation can make ASO development more accessible and reproducible. Although it remains a prototype, the model serves as a blueprint for future teams to expand on or physically build into a working device.
Impact: The ASOmatic contributes a replicable vision for how synthetic biology can accelerate therapeutic discovery for future iGEM teams.
Educational Resources for Beginner Teams
We strive to provide beginner iGEM teams with simple tips and information on how to successfully execute their projects in wet lab and preliminary research. Our one-pagers include topics such as lab procedures, laboratory equipment, and safety rules, which are all important features that contribute to conducting research. We hope that our experiences in wet labs can provide insight in the research aspect of projects and pave a path for future iGEM teams.
Educational One-Pagers
Browse our comprehensive guides for beginner iGEM teams
Lab Safety Rules
Working in labs requires taking precautions that protect yourself, the experiment, and the environment outside the lab. These safety rules are important for understanding the basic precautions that prevent accidents and ensure everyone's safety.
Email Template
Understanding how to craft an email that aligns with your goal is important when discussing with potential stakeholders, labs, and your community to explicitly get your point across. In the provided email outline, we communicated with a lab that we wanted to work with; however, it can be modified. We hope that our template will guide beginner iGEM teams in their outreach.
Common Laboratory Equipment
Various lab equipment serves specific purposes in producing your results. These descriptions will help you gain a better understanding of each equipment that is commonly used.
Micropipetting
Micropipettes are commonly used laboratory tools that efficiently and precisely measure small amounts of liquids that are transferred. Pipettes differ in measuring volumes, with most between 1 and 1000µl, which is extremely helpful in yielding accurate results when dispensing liquids. Additionally, micropipettes may appear simple, but they are fragile tools that can easily break, which may affect the accuracy of measurement. Because micropipettes are commonly used, it is important to use them properly.
Serial Dilution
Serial dilution is a process in which a given substance concentration is repeatedly reduced until it meets a usable concentration. In our experiment, using this skill was necessary for establishing our primer efficiency curves. Due to its wide variety of applicability, new iGEM teams can easily use this skill in their experiments.
Hazard Symbols and Waste Disposal
Being aware of the different hazard symbols is a practical and necessary skill for safe lab work. They can prevent accidents, injuries, or long-term health effects. Proper waste disposal prevents dangerous cross-contamination in the lab and reduces the risk of sharp injuries. For new iGEM teams, these practices build a habit of responsibility and safety which allows teams to focus on their research with confidence.
Interpreting Results: Nanodrop
Machines in the lab can be difficult to operate and interpret for beginners, so this infographic is a quick guide to interpreting Nanodrop results for DNA and RNA. It explains how to read the absorbance spectrum, check purity ratios, and recognize characteristic peaks. By comparing these values to expected ranges, iGEM teams can judge sample quality and decide if further purification is needed.