External control of a genetic toggle switch via Reinforcement Learning

We use a learning-based strategy to stabilize a synthetic toggle switch via an external control approach, adopting a sim-to-real paradigm to overcome the data efficiency problem that would render the algorithm infeasible for practical use in synthetic biology. Here, the policy is learnt via training on a simple model capturing the main dynamical features of a toggle switch and is then validated in-silico using stochastic agent-based simulations. Our in-silico experiments confirm the viability of the approach suggesting its potential use for in-vivo control implementation in microfluidics.