# External control of a genetic toggle switch via Reinforcement Learning

arXiv: https://arxiv.org/abs/2204.04972 · HTML: https://mlsanity.com/paper/2204.04972/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 11 Apr 2022.

## Builds on (most cited first, 5 of 5)

- [Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task](https://mlsanity.com/paper/1707.02267/) (2017)
- [On Periodic Reference Tracking Using Batch-Mode Reinforcement Learning with Application to Gene Regulatory Network Control](https://mlsanity.com/paper/1303.2987/) (2013)
- [Toggling a Genetic Switch Using Reinforcement Learning](https://mlsanity.com/paper/1303.3183/) (2013)
- [Analysis and control of genetic toggle switches subject to periodic multi-input stimulation](https://mlsanity.com/paper/1806.02171/) (2018)
- [Ratiometric control for differentiation of cell populations endowed with synthetic toggle switches](https://mlsanity.com/paper/1903.09414/) (2019)

## Abstract (arXiv)

We investigate the problem of using a learning-based strategy to stabilize a synthetic toggle switch via an external control approach. To overcome the data efficiency problem that would render the algorithm unfeasible for practical use in synthetic biology, we adopt a sim-to-real paradigm where the policy is learnt via training on a simplified model of the toggle switch and it is then subsequently exploited to control a more realistic model of the switch parameterized from in-vivo experiments. Our in-silico experiments confirm the viability of the approach suggesting its potential use for in-vivo control implementations.