Overview

This year, we developed a rapid and novel detection method called CHELO. To achieve this, we created hardware for quick detection and designed an NLP-based approach to identify and construct CHELO. While the predicted peptides did not perform as well as the full proteins, the method remains valuable for proteins lacking antibodies. We aim to present these tools—hardware, model, and model-based software—to future iGEM teams to support their research and development.

Hardware

In our project, we designed hardware that is user-friendly, easy to fabricate, cost-effective, and efficient in reducing detection time. After thorough testing, the device has proven effective in detecting electrochemical features in biological samples. Looking ahead, this method has the potential to be widely used in villages for rapid analysis of biological features.

Model

For model development, we harnessed artificial intelligence (AI), recognizing its global significance. We designed a custom AI to process large volumes of language data and scientific papers, enabling us to build on existing knowledge and bring novel ideas to life.

Model based software

Due to the complexity and time required to create a custom AI, we also developed software to share our model. Through this software, researchers can easily identify potential underlying protein markers. Additionally, with the function to list binding proteins, the software can utilize CHELO to detect and identify other disease markers.

Summary

CHELO is not only a valuable tool for researchers in identifying target markers for detection but also an innovative approach to integrating engineering and biology for next-generation diagnostics. It serves as a strong example for future researchers in synthetic biology. For upcoming iGEMers, the detection method and model construction come with straightforward and clear protocols to follow.