Discrete-time Neural Network Identification of Quorum Sensing Escherichia coli Regulators

Abstract Quorum Sensing (QS) is a complex process of cell to cell communication that allows bacteria to share information and regulate gene expression. This bacterial process is difficult to model because of their complexity, nonlinearity, and stochastic nature. While nonlinear systems can be derived using mathematical modeling, neural networks are an excellent tool to infer their dynamics. In this paper, we present a Recurrent High Order Neural Network (RHONN) to identify the communication system used by Escherichia coli (E. coli) to regulate its genetic expression. Simulation results show the applicability of the identifier.