Finding Missing Interactions in Gene Regulatory Networks Using Boolean Models

Abstract

Gene regulatory networks (GRNs) play a fundamental role in development and cellular behavior. However, due to a lack of experimental information, there are missing interactions in the GRNs inferred from published data. It is not a trivial task to predict the position and nature of such interactions. We propose a set of procedures for detecting and predicting missing interactions in Boolean networks that are biologically meaningful and maintain previous experimental information. We tested the utility of our procedures using the GRN of the Arabidopsis thaliana root stem-cell niche (RSCN). With our approach we were able to identify some missing interactions necessary to recover the reported gene stable state configurations experimentally uncovered for the different cell types within the RSCN.