Inferring a system of differential equations for a gene regulatory network by using genetic programming

Describes an evolutionary method for identifying a gene regulatory network from the observed time series data of the gene's expression. We use a system of ordinary differential equations as a model of the network and infer their right-hand sides by using genetic programming (GP). To explore the search space more effectively in the course of evolution, the least mean squares (LMS) method is used along with ordinary GP. We apply our method to three target networks and empirically show how successfully GP infers the systems of differential equations.