Most of the popular approaches towards gene regulatory networks inference e.g., Dynamic Bayesian Networks, Probabilistic Boolean Networks etc. are computationally complex and can only be used to infer small networks. While high-throughput experimental methods to monitor gene expression provide data for thousands of genes, these methods cannot fully utilize the entire spectrum of generated data. With the advent of information theoretic approaches in the last decade, the inference of larger regulatory networks from high throughput microarray data has become possible. Not all information theoretic approaches are scalable though; only methods that infer networks considering pair-wise interactions between genes such as, relevance networks, ARACNE and CLR to name a few, can be scaled upto genome-level inference. ARACNE and CLR attempt to improve the inference accuracy by pruning false edges, and do not bring in newer true edges. REVEAL is another information theoretic approach, which considers mutual information between multiple genes. As it goes beyond pair wise interactions, this approach was not scalable and could only infer small networks. In this paper, we propose two algorithms to improve the scalability of REVEAL by utilizing a transcription factor list (that can be predicted from the gene sequences) as prior knowledge and implementing time lags to further reduce the potential transcription factors that may regulate a gene. Our proposed S-REVEAL algorithms can infer larger networks with higher accuracy than the popular CLR algorithm.