Inference of gene regulatory networks with variable time delay from time-series microarray data

Regulatory interactions among genes and gene products are dynamic processes and hence modeling these processes is of great interest. Since genes work in a cascade of networks, reconstruction of gene regulatory network (GRN) is a crucial process for a thorough understanding of the underlying biological interactions. We present here an approach based on pairwise correlations and lasso to infer the GRN, taking into account the variable time delays between various genes. The proposed method is applied to both synthetic and real data sets, and the results on synthetic data show that the proposed approach outperforms the current methods. Further, the results using real data are more consistent with the existing knowledge concerning the possible gene interactions.