Structural analysis over well studied transcriptional regulatory networks indicates that these complex networks are made up of small set of reoccurring patterns called motifs. While information theoretic approaches have been immensely popular, these approaches rely on inferring the regulatory networks by aggregating pair-wise interactions. In this paper, we propose novel structure based information theoretic approaches to infer transcriptional regulatory networks from the microarray expression data. The core idea is to go beyond pair-wise interactions and consider more complex structures as found in motifs. While this increases the network inference complexity over pair-wise interaction based approaches, it achieves much higher accuracy and yet is scalable to genome-level inference. Detailed performance analyses based on benchmark precision and recall metrics on the known Escherichia coli's transcriptional regulatory network indicates that the accuracy of the proposed algorithms is consistently higher in comparison to popular algorithms such as context likelihood of relatedness (CLR), relevance networks (RN) and GEneNetwork Inference with Ensemble of trees (GENIE3). In the proposed approaches the size of structures was limited to three node cases (any node and its two parents). Analysis on a smaller network showed that the performance of the algorithm improved when more complex structures were considered for inference, although such higher level structures may be computationally challenging to infer networks at the genome scale.
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