Function-preserving Filters for Sampling in Biological Networks

Assays created to study systems of disease and aging can offer a whole new set of therapeutic targets. However, with experiments of this immense volume, data becomes unmanageable for many traditional analyses. Enter the biological network, a tool for modeling relationships among high-throughput data that is quickly rising in popularity. Small networks (in the order of hundreds to few thousands of nodes) use relationships between network structure to infer biological function; this relationship has been confirmed and used in many studies to advance the study of model organisms. Networks built for assessing entire genomes, or entire protein repertoires, however, tend to be very large and complex, having tens of thousands of nodes and in some cases upwards of millions of edges. Thus, network sampling techniques take an appropriate step to reduce complexity while modeling data. Here we present a new type of network sampling applied to biological correlations network, the spanning tree, designed to identify critical hub nodes in the model. We compare this filter to others previously used to identify structures in complex networks, chordal-based filters. The results of this work highlight the applicability for multiple filters based upon the graphic structure and biological result desired.