In bioinformatics vast amount of data are being generated from various types of biological networks: protein complex networks, gene regulatory networks, protein-protein interaction networks and many others. Such problems as network comparison, identification of similar sub-networks are of high interest in Computational Systems Biology. Due to large amount of noise and incompleteness of information, more reliable and robust approaches are required to effectively process real biological data. The Graph Edit Distance (GED) – the minimal amount of necessary distortions for transforming one graph into another, is both error-tolerant and flexible, which is very promising in this context. However, GED problem is of high computational complexity being both NP-hard and non-fixed parameter tractable, so that in practice it is impossible to find exact solution. Nevertheless, we are sure that deep understanding of related biological data and development of GED-based techniques applying to biological network analysis problems will produce valuable results and provide more insight into the global view of biological processes, behavior and evolution.