Reinforcement learning and genetic regulatory network reconstruction

Abstract

Many different models of genetic regulatory networks (GRN) exist, but most of them are focused on off-line processing, so that important features of real networks, like adaptive and non-stationary characterare missed. Interdisciplinary insight into the area of self-organization within the living organisms has caused some interesting new thoughts, and the suggested model is among them. Based on reinforcement learning of the Boolean network with random initial structure, the model is searching for a specialized network, that agrees with experimentally obtained data from the real GRN. With some experiments of real biological networks we investigate its behaviour. © 2013 Springer-Verlag Berlin Heidelberg.