Pattern discovery in multilayer networks

Traditional methods model biological systems using single and binary network. However, this model is inadequate as different sets of interactions can simultaneously take place for different interaction constraints. Existing methods for motif counting on single network topologies are inadequate to capture patterns of molecular interactions that have significant changes in biological expression when identified across different organisms that are similar, or even time-varying networks within the same organism. In this paper, we consider the problem of counting the number of instances of a user supplied motif topology in a given multilayer network. We apply our model and algorithm to study frequent patterns in cellular networks that are common in varying cellular states under different stress conditions, where the cellular network topology under each stress condition describes a unique network layer. Results on synthetic datasets demonstrate that our algorithm finds motif embeddings with very high accuracy and it is several orders of magnitude faster than existing state-of-the-art methods. Our results on Escherichia coli (E. coli) transcription regulatory network under different experimental conditions demonstrate that our method is able to select genes that conserve functional characteristics under various stress conditions with very low FDR values.