A Boolean network inference from time-series gene expression data using a genetic algorithm

Motivation: Inferring a gene regulatory network from time-series gene expression data is a fundamental problem in systems biology, and many methods have been proposed. However, most of them were not efficient in inferring regulatory relations involved by a large number of genes because they limited the number of regulatory genes or computed an approximated reliability of multivariate relations. Therefore, an improved method is needed to efficiently search more generalized and scalable regulatory relations. Results: In this study, we propose a genetic algorithm-based Boolean network inference (GABNI) method which can search an optimal Boolean regulatory function of a large number of regulatory genes. For an efficient search, it solves the problem in two stages. GABNI first exploits an existing method, a mutual information-based Boolean network inference (MIBNI), because it can quickly find an optimal solution in a small-scale inference problem. When MIBNI fails to find an optimal solution, a genetic algorithm (GA) is applied to search an optimal set of regulatory genes in a wider solution space. In particular, we modified a typical GA framework to efficiently reduce a search space. We compared GABNI with four well-known inference methods through extensive simulations on both the artificial and the real gene expression datasets. Our results demonstrated that GABNI significantly outperformed them in both structural and dynamics accuracies. Conclusion: The proposed method is an efficient and scalable tool to infer a Boolean network from time-series gene expression data.