Using Reinforcement Learning to Rationally Reprogram Multicellular Decision Making

FALL 2019 RESEARCH INCUBATION AWARDEES

PI: Allyson Sgro, Assistant Professor of Biomedical Engineering, ENG
Co-PI: Pankaj Mehta, Associate Professor of Physics, CAS
Track: HIC & Artificial Intelligence Research

While standard modeling techniques can describe and predict biological behaviors, they fall short of offering strategies for manipulation and reprogramming. The team hypothesizes that single cells have algorithms for interpreting their environment and that they can reprogram these algorithms with Reinforcement Learning (RL) techniques. It would be an experimentally-informed hybrid-automata-continuum model of multicellular behavior. The eventual goal is to make an open-loop controller directly capable of learning to control cellular behaviors in real-time during experiments.