Computational Methods for Biological Networks

Dear Colleagues,

Biological networks are used to define relationships (edges) between biological entities (nodes). They are used to represent a huge variety of biological systems from the microscopic (biomolecular, genetic, and regulatory interactions) to the macroscopic (taxonomic trees, evolutionary relationships, predator-prey relationships). They can be used to define dynamic relationships with time-varying networks representing molecular interactions, regulation, evolutionary changes, or even species migration, or space-varying networks such as tissue-specific relationships, contacts or near-contacts in folded structures, or predator–prey relationships in some particular region. Dynamic or space-varying networks are often simplified to be static and represent all possible relationships. Biological networks can have any number of nodes from a handful (e.g., functional pathways) to tens of thousands (gene/protein networks) or more; they can be directed or undirected; and nodes may be sparsely connected with binary edges or densely connected with every pair, having a real-valued weight on the edge between them.

The huge diversity of biological networks gives rise to an equally huge diversity in the types of computational analyses that can be performed: one may model the dynamics of regulation in a handful of nodes in a small network using differential equations to look for steady states or cyclic behavior; one may wish to compare networks of the same type across species, across tissues, or across time; or one may wish to transfer information between networks of similar but not identical types (e.g., genetic vs. protein interactions). In this issue, we welcome novel computational advances in any area of biological network analysis.

Dr. Wayne B Hayes
Guest Editor

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• biological networks
• biomolecular interactions
• network comparison
• network alignment
• evolutionary dynamics