Networks - Gene Disruption Networks - Graphs

Graphs are a standard method to represent data in computer science. Graphs consist of nodes and edges (see Fig. 1). Nodes are representing entities like e.g. genes and are often graphically represented by circles. Edges are representing relationships between these entities and are graphically represented as lines or arcs connecting the nodes. The relationship can be directed (arcs), e.g. A activates B, or symmetric, e.g. the sequence of A is similar to the sequence of B.

Graphs have been used to represent all kinds of networks. Recent biological examples are protein-protein interaction networks, where nodes represent proteins and edges represent the physical interaction between proteins ( Maslov & Sneppen Science 2002; Schwikowsky et al. Nat. Biotech. 2000 ). A path is a ordered list of edges (or arcs) which connect two nodes. The path length in this context is the number of edges(arcs) you have to "walk along" to get from one node to the other. If there is an edge between two nodes, i.e. the path length between the two nodes is 1, we will call this a direct connection. If the path length between two nodes is larger than 1 we will call it a indirect connection. A component (or subnet) of an undirected graph is a subgraph, where all nodes are connected by paths; in a directed graph we can use the same definition if we ignore the directionality of the arcs. The diameter of a graph is the average length of the shortest paths between any two nodes in the graph. The degree of a node is the number of adjacent edges. In a directed graph it is useful to make a distinction between indegree, number of arcs pointing to a given node and the outdegree, the number of arcs pointing from a given node to other nodes. Nodes with very high degrees are sometimes called hubs.

The representation in the form of graphs allows the use of powerful algorithms to examine large data sets efficiently, e.g. to find the shortest path between two nodes.

... more information on graph theory are here.
or in Schlitt T and Brazma A: Learning about gene regulatory networks from gene deletion experiments. Comp Funct Genom 2002(3):499-503.