My project using machine learning and NLP algorithms to create new composite BioBricks from user specification (combining the fields of AI + synthetic biology). It works by combining bricks from the sequenced parts database with each other based on input from the user, and uses ML/NLP to output bricks that have the highest chance of functioning together, along with satisfying the requirements from the user. This project is important because it can prove to be very beneficial in the pharmaceutical and medical fields. This is because it provides a way to create and test drugs in a much more efficient way, for example, using BioBricks to create cells that produce human compatible insulin can be achieved with this program. Using ML for this project can also be very useful for combating antibiotic resistance, in the sense that new antibiotics can be tried and tested well before they are actually needed in the field. Although most of the backend programming is completed, I am currently working on creating a clean user interface which would also provide many options for viewing new BioBricks in different ways, such as in a plasmid, vector, etc.


What inspired you (or your team)?

For this project I was inspired by the endless possibilities that stem from the field of synthetic biology + medicine. As a young girl, I was always amazed by the rapid advancements in the field of medicine and knew that I too wanted to contribute something that could help society combat disease and other health concerns. I was so lucky to have the opportunities to explore the world of synthetic biology and as soon as I saw the inefficiency with working with the database of BioBricks, I knew that I could use my AI background to help contribute to a better way to work with these complex parts.