Toggling a Genetic Switch Using Reinforcement Learning
This work addresses the challenge of controlling complex biological systems like gene regulatory networks for researchers in synthetic biology, though it is incremental as it applies an existing RL method to a new domain.
The paper tackled the problem of optimal exogenous control of gene regulatory networks by adapting the fitted Q iteration reinforcement learning algorithm to infer control laws from system response measurements, without requiring a mathematical model, and demonstrated its application by successfully driving protein concentrations in a toggle switch system to a target region.
In this paper, we consider the problem of optimal exogenous control of gene regulatory networks. Our approach consists in adapting an established reinforcement learning algorithm called the fitted Q iteration. This algorithm infers the control law directly from the measurements of the system's response to external control inputs without the use of a mathematical model of the system. The measurement data set can either be collected from wet-lab experiments or artificially created by computer simulations of dynamical models of the system. The algorithm is applicable to a wide range of biological systems due to its ability to deal with nonlinear and stochastic system dynamics. To illustrate the application of the algorithm to a gene regulatory network, the regulation of the toggle switch system is considered. The control objective of this problem is to drive the concentrations of two specific proteins to a target region in the state space.