DeepAI AI Chat
Log In Sign Up

External control of a genetic toggle switch via Reinforcement Learning

by   Sara Maria Brancato, et al.
University of Salerno
University of Naples Federico II

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.


page 1

page 2

page 3

page 4


Learning to Switch Between Machines and Humans

Reinforcement learning algorithms have been mostly developed and evaluat...

All-in-One: A DRL-based Control Switch Combining State-of-the-art Navigation Planners

Autonomous navigation of mobile robots is an essential aspect in use cas...

No Efficient Disjunction or Conjunction of Switch-Lists

It is shown that disjunction of two switch-lists can blow up the represe...

Hierarchical Reinforcement Learning as a Model of Human Task Interleaving

How do people decide how long to continue in a task, when to switch, and...

Modelling Noise-Resilient Single-Switch Scanning Systems

Single-switch scanning systems allow nonspeaking individuals with motor ...

Prize insights in probability, and one goat of a recycled error: Jason Rosenhouse's The Monty Hall Problem

The Monty Hall problem is the TV game scenario where you, the contestant...

Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions

Creating open-ended algorithms, which generate their own never-ending st...