Optimization of synthetic oscillatory biological networks through Reinforcement Learning
L. Giannantoni, A. Savino, S. Di Carlo
This study applies reinforcement learning to optimize synthetic biological networks by controlling Hopf bifurcations in ODE-based systems, showing its ability to identify bifurcation onset while facing challenges in persistent oscillations and computational speed.
bioRxiv · Nov 19, 2023Bioengineering