Similarly, the CSD concept can be modified and extended to create a new theoretical framework that will enable the study of the functional fusion of network topology and node activities and can significantly widen and deepen our understanding of complex network systems.
We believe that this will provide a better understanding of complex network related to the multi-functions of TS protein as an RNA binding protein or as a potential oncogene.
Other priorities include understanding phenomena of complex networks such as malware propagation and new models of cybersecurity based on biological concepts such as biodiversity and immunity.
Understanding the structure of complex networks is a continuing challenge, which calls for novel approaches and models to capture their structure and reveal the mechanisms that shape the networks.
Improved techniques for integration of data arising from different sources, as well as for visualization, will be crucial for understanding the functionality of complex networks.
Understanding the global organization of complex networks will provide valuable and perhaps unique topological information, and may also lead to a better understanding of the dynamical and evolutionary processes of the networks, as demonstrated in several other biochemical systems, such as metabolic networks and protein-protein interaction networks [18] [22].
The visualization of the network map lead to an intuitive understanding of topological architecture of complex networks.
We conclude by discussing the importance of these principles for both, understanding the natural evolution of complex networks at the molecular level and for creating artificial biological systems with specific features.
Recent advances in understanding the generic properties of complex networks, including various biological, technological, and social networks [15], [16], allow for a quantitative examination of the organization of N-linked glycosylation pathways.
