Analysis of Gene Interactions Using Restricted Boolean Networks and Time-Series Data

A popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and time-series data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. We applied the proposed algorithm in a case study of the budding yeast cell cycle network using an artificial dataset. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered. We have shown that this analysis can be used as the first step for gene relationships detection with a high flexibility to include biological knowledge. What we envisage with our method is a model that points out which connections should be checked in the wet lab and consequently facilitate some biological experiments.