text: The study of network motifs was pioneered by Holland and Leinhardt who introduced the concept of a triad census of networks. They introduced methods to enumerate various types of subgraph configurations, and test whether the subgraph counts are statistically different from those expected in random networks.
  This idea was further generalized in 2002 by Uri Alon and his group  when network motifs were discovered in the gene regulation (transcription) network of the bacteria E. coli and then in a large set of natural networks. Since then, a considerable number of studies have been conducted on the subject. Some of these studies focus on the biological applications, while others focus on the computational theory of network motifs.
  The biological studies endeavor to interpret the motifs detected for biological networks. For example, in work following, the network motifs found in E. coli were discovered in the transcription networks of other bacteria as well as yeast and higher organisms. A distinct set of network motifs were identified in other types of biological networks such as neuronal networks and protein interaction networks.
  The computational research has focused on improving existing motif detection tools to assist the biological investigations and allow larger networks to be analyzed. Several different algorithms have been provided so far, which are elaborated in the next section in chronological order.
  Most recently, the acc-MOTIF tool to detect network motifs was released.