MIT EECS Undergraduate Research and Innovation Scholar
Improving Tools for Synthetic Biology Through Machine Learning
Synthetic biologists are developing new capabilities for sensing, computation, and actuation within living biological systems. My project seeks to improve upon the TASBE (Tool-chain to Accelerate Synthetic Biology Engineering) workflow. TASBE uses a chain of tools to realize desired biological functions with DNA. Specifically, I will extend MatchMaker, a software component of TASBE that takes abstract gene regulation networks as input and selects actual genes and proteins to implement them. I plan to apply machine learning techniques such as classification and clustering to allow for validation and enhancement of the software. This will lead to an improved version of MatchMaker that can create larger, more complex biological systems.
I have worked to develop an adaptive disaster evacuation simulation as a research assistant at the University of Notre Dame. I have also participated in a research project at the Broad Institute seeking to develop innovative techniques for understanding the behavior of protein molecules.