Reverse engineering gene networks with artificial neural networks

Abstract

Temporal gene expression data is of particular interest to researchers as it can be used to create regulatory gene networks. Such gene networks represent the regulatory relationships between genes over time and provide insight into how genes up- and down-regulate each other from one time-point to the next (the Biological Motherboard). Reverse engineering gene networks from temporal gene expression data is considered an important step in the study of complex biological systems. This paper introduces sensitivity analysis of trained perceptions to reverse engineer the gene networks from temporal gene expression data. It is shown that a trained neural network, with pruning (gene silencing), can also be described as a gene network with minimal re-interpretation, where the sensitivity between nodes reflects the probability of one gene affecting another gene in time. The methodology is known as the Neural Network System Biology Approach with Gene Silencing Simulations (NNSBAGSS). The methodology was applied to artificial temporal data and rat CNS development time-course data.