Uncovering fundamental features of biological networks

Abstract

Many biological networks are truly complex systems, displaying highly irregular yet significantly non-random structure. Extracting the statistical regularities of a biological network is a challenging inverse problem since one often has access to only a single experimental realization. In this case the analysis necessarily relies on a statistical model, typically a random graph null model against which statistical significance is defined. Results of the analysis may crucially depend on the choice of null model, leading to possible uncontrolled biases when this choice is ambiguous.