Genetic Regulatory Systems Modeled by Recurrent Neural Network

Abstract

In this paper, we focus on modeling and exploring the genetic regulatory systems (GRS) with an artificial recurrent neural network (ARNN). Based on the approximation capability of the ARNN, the proposed model can be used to express and analyze the genetic regulatory systems of genetic components, even the large-scale genetic systems. Unlike the conventional ARNN, the ARNN used in the paper is the echo state network (ESN), in which the connection weights of internal neurons are fixed and only the output weights are adjustable. Thus, there are no cyclic dependencies between the trained readout connections and, training the genetic regulatory system becomes a simple linear regression task. The experiment studies shows the new genetic regulatory system modeled by ESN and trained from the fluorescent density of reporter protein has a satisfactory performance in modeling the synthetic oscillatory network of transcriptional regulatory of Escherichia coli cells.