An Empirical Study of Immune System Based On Bipartite Network

TL;DR

This study models the immune system using bipartite networks, revealing power-law degree distribution, key mediators, and the system's non-social network characteristics, providing a comprehensive understanding of immune interactions.

Contribution

Introduces a bipartite graph model of the immune system based on empirical data, highlighting key mediators and network properties.

Findings

Degree distribution follows a power-law with index 1.8.

Identifies key mediators like TNF-alpha and IL-6 as crucial in immune regulation.

Shows the immune network has a negative assortativity of -0.27.

Abstract

Immune system is the most important defense system to resist human pathogens. In this paper we present an immune model with bipartite graphs theory. We collect data through COPE database and construct an immune cell- mediators network. The act degree distribution of this network is proved to be power-law, with index of 1.8. From our analysis, we found that some mediators with high degree are very important mediators in the process of regulating immune activity, such as TNF-alpha, IL-8, TNF-alpha receptors, CCL5, IL-6, IL-2 receptors, TNF-beta receptors, TNF-beta, IL-4 receptors, IL-1 beta, CD54 and so on. These mediators are important in immune system to regulate their activity. We also found that the assortative of the immune system is -0.27. It reveals that our immune system is non-social network. Finally we found similarity of the network is 0.13. Each two cells are similar to small extent. It reveals that many cells have its unique features. The results show that this model could describe the immune system comprehensive.