An Optimal Boolean Approach for Computational Modeling of Gene Regulatory Networks from Temporal Gene Expression Profile

Deciphering the crucial interactions among genes is one of the key issues in understanding the fundamental molecular and intracellular mechanisms of cell. Computational modeling of gene regulatory networks can be used as a powerful tool in various fields of molecular biomedicine such as identification of metabolic, regulator, and signal transduction pathways, analysis of complex genetic diseases, and drug discovery. In this paper, an optimal Boolean approach is proposed for computational modeling of gene regulatory networks from temporal gene expression profile. In this method, the optimal values of the Boolean thresholds of gene expression signals and the parameters of the interaction patterns between target and regulator genes are all designed as a mixed-integer nonlinear programming solved by Genetic Algorithm. To evaluate the performance of the proposed scheme, it has been applied to a well-known time course microarray data and gene regulatory network of Saccharomyces Cerevisiae from the literature. The reference network has 11 genes, 9 targets, and 61 regulatory interactions, and the original transcriptional dataset includes 18 timepoints for each gene expression signal. In this case study, the proposed computational model contains 142 unknown parameters that are optimally determined through optimization. The results demonstrate the efficiency of the proposed approach.