2013/03/12 by Aivar Sootla, Sootla, Aivar, Natalja Strelkowa +7
Biochemistry, Genetics and Molecular Biology · #Computational Engineering #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Finance #Gene Regulatory Network Analysis #Machine Learning (cs.LG) #Microbial Metabolic Engineering and Bioproduction #Molecular Networks (q-bio.MN) #Systems and Control (eess.SY) #Viral Infectious Diseases and Gene Expression in Insects #and Science (cs.CE) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1303.3183
openalex publication_date 2013/03/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.