Human diseases and social networks would seem to have little in common. However, at the crux of these two lies a network, communities within the network, and farther even, substructures of the communities. In a recent paper in Physical Review E 77:016104 (2008), Weixiong Zhang, Ph.D., Washington University associate professor of computer science and engineering and of genetics, and his Ph.D. student, Jianhua Ruan, published an algorithm, a recipe of computer instructions, to automatically discover communities and their subtle structures in various networks.
Many complex systems can be represented as networks, Zhang said, including the genetic networks he studies, social networks and the Internet. The community structure of networks features a natural division of the network where the vertices in each subnetwork are highly involved with each other, though connected less strongly with the rest of the network. Communities are relatively independent of one another structurally, but it is thought that each community may correspond to a fundamental functional unit. A community in a genetic network usually contains genes with similar functions, just as a community on the World Wide Web often corresponds to web pages on similar topics.
All Zhang and Ruan need are data. Their algorithm is more scalable than existing algorithms and can detect communities at a finer scale and with a higher accuracy than similar algorithms. The impact of having such a computational biology tool is in genomics, where researchers may be better able to identify and understand communities of genes and their networks as well as how they cooperate in causing diseases, such as sepsis, virus infections, cancer and Alzheimers disease.
Versatile math tool
The algorithm is so versatile that it has been applied to identify the
community structure of a network of co-expressed genes involved in
bacterial sepsis. This is a tool
|Contact: Wexiong Zhang|
Washington University in St. Louis