Topological Network Analysis

  • Johannes F. Knabe
Part of the Studies in Computational Intelligence book series (SCI, volume 428)


Understanding GRN dynamics is a hard task and so methods for breaking down their complexity in more easily analysable parts have been proposed. Very influential has been the so-called “motif analysis”, a general analysis method for all kinds of networks. This might be due to its simplicity, as it only searches for patterns (subgraphs) in the static, time-independent, connectivity structure of networks. Network motifs “are those patterns for which the probability P of appearing in a randomised network an equal or greater number of times than in the real network is lower than a cutoff value” [Milo et al(2002)Milo, Shen-Orr, Itzkovitz, Kashtan, Chklovskii, and Alon]. [Alon(2006)] and co-workers have developed the approach and used it to analyse the patterns networks are made up of, from the gene regulation network of E. coli to the world wide web. They have also shown what functions some overrepresented motifs might serve by analysing their range of dynamics exhibited in isolation. [Conant and Wagner(2003)] have suggested that network motifs were independently selected for particular functionality in a converging manner.


Function Network Gene Node Network Motif Evolve Function Motif Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Science and Technology Research InstituteUniversity of HertfordshireHatfieldUnited Kingdom

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