Toggling a Genetic Switch Using Reinforcement Learning
Abstract
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.
Cite
@article{arxiv.1303.3183,
title = {Toggling a Genetic Switch Using Reinforcement Learning},
author = {Aivar Sootla and Natalja Strelkowa and Damien Ernst and Mauricio Barahona and Guy-Bart Stan},
journal= {arXiv preprint arXiv:1303.3183},
year = {2015}
}
Comments
12 pages, presented at the 9th French Meeting on Planning, Decision Making and Learning, Li\`ege (Belgium), May 12-13, 2014