Detecting Crosstalk Modules of Combined Networks: the Case for the NF-κB and p53

It has become increasingly clear that signalling pathways are extensively interconnected and are embedded in networks with common protein components. These components do not exist in isolation but may gather together to form crosstalk modules. Constructing these crosstalk modules has emerged as a good method to understand the mechanisms underlying the propagation of transduction signals in cell. In this paper, we have presented an advancement of the method, which is chiefly used to integrate multiple topological and functional data to detect crosstalk modules between NF- B and p53 signalling systems. Applying the Expectation Maximization (EM) clustering algorithm, we obtained the competitive results compared to the k-means algorithm. The EM algorithm as a soft clustering method is able to distinguish overlapping parts among clusters, and here we that it is potentially more sensible than the k-means algorithm in detecting the cross talking modules involved in the network interactions between the two systems NF- B and p53. In addition, the biological analyses support our findings, and propose testable hypotheses to which the functional networks are involved in along with their associated human diseases.