iGEM Project 2018 - Optimal Dynamical Control of Bacterial Co-Cultures using Optogenetics
General, iGEM ·The iGEM team is a student-lead research and design team, focusing on engineering applications of synthetic biology. In 2018, we developed a novel optimal control algorithm for stabilizing mixed-strain bacterial cultures using an optogenetic actuator. A detailed description of the project can be found at this link. The project involved more than 30 team members, working on both the laboratory-based aspects of the project as well as the computational and software aspects. My role in this project was to research and prototype the optimal control algorithm, and to attempt to incorporate the algorithm into a lab-based setting.
Two optimal control methods were used - PID control and Model Predictive Control. Literature review showed that Model Predictive Control is more robust to oscillations. These algorithms were developed using GEKKO, a package for python used for optimal control.