Razieh Bostani - Department of Applied Mathematics, Faculty od Mathematical Sciences, Tarbiat Modares University, Tehran, PO Box ۱۴۱۱۵-۱۳۴, Iran
Mohieddin Jafari - Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, PO Box ۱۳۱۶۴, Iran
Mehdi Mirzaie - Department of Applied Mathematics, Faculty od Mathematical Sciences, Tarbiat Modares University, Tehran, PO Box ۱۴۱۱۵-۱۳۴, Iran

Recent progress in high throuput technologies have generated big data at various levels of biological systems such as genomics, transcriptomics metabolomics and proteomics data. Some of these data can be interpreted in network content, known as biological networks. These networks originally express the pairwise interaction of cell componenets. These networks include valuable information that help us to realize cell better. The constructed networks usually are large-scale thus it is necessary to uses appropriate mathematical methods for analysis them. Some of this common analysis in biological networks are centrality analysis that can characterize the central nodes and even edges. Infact, centrality measures aim to give a numerical characterization of a node’s significance in a network. The common centralities are degree centrality, betweenness centrality and closeness centrality; howevere, these measurements have well-known limitations and does not consider functioning of a group of nodes rather than an individual nodes. The group centrality develop more sophisticated measures using cooperative game. This centrality is calculated based on Shapley value. The Shapley value is a solution concept in cooperative game theory. Recently, a centraility based on game theory have introduced and used in defeating terrorist networks. In this study, we use this concept for analyzing of biological networks and compare its result with degree centrality, betwenness centrality and closeness centrality.

Game theory; Biological Networks; Shapely Value; centrality measure