Modeling of Gene Regulatory Networks

Abstract

Many crucial molecular processes and cellular pathways are based on the interactions among genes. The genes in living cells regulate each other to control the production of gene products. Gene regulatory networks provide information on the control at gene expression level and can be inferred from a number of data-sets expressed in different ways. There are two types of gene expression data used for gene regulatory network construction: time series and perturbation experiments. Time series expression data enables biologists to investigate the temporal pattern in biological networks. Perturbed expression data provides the information on interactions directions. In the past, gene regulatory networks were constructed by using the clustering approach. However, this approach failed to identify significant transcriptional network interactions. Hence, many computational approaches have been developed for constructing gene regulatory networks more effectively. Reverse engineering from given data-sets can prove to computationally challenging, so the approach taken aims to construct stable and scalable gene regulatory networks from given steady state data.