Proteomic Data Analysis Optimization Using a Parallel MPI C Approach

Proteins and the networks they determine, called interactome networks, have received attention at an important degree during the last years, because they have been discovered to have an influence on some complex biological phenomena, such as problematic disorders like cancer. This paper presents a new parallel computation technique that allows for an accurate and fast analysis of the human interactome network to be conducted. It constitutes, essentially, a proteomic data analysis process that takes into consideration the sparse nature of interactome networks. Thus, the first stage of the analysis involves the parallel computation of each proteins betweenness centrality measure through a parallel sparse networks-dedicated approach. Then, the second phase detects the functionally-related communities of proteins. In order to accomplish this purpose, we make use of a community detection algorithm that is based on the edge betweenness calculation and that has been already described in one of the authors previous papers. The new protein data analysis technique was carefully tested on real biological data and the results acknowledge the existence of some important properties of those proteins that participate in the carcinogenesis process. Apart from being particularly useful for research purposes, the novel technique also speeds up the proteomic databases analysis process, as compared to our own sequential approach. The results of the comprehensive battery of tests that were applied prove some unique topological features of cancer mutated proteins, and a possible optimization solution for cancer drugs design is suggested.