Constructing and analyzing biological interaction networks for knowledge discovery

Many biological datasets can be effectively modeled as interaction networks where nodes represent biological entities of interest such as proteins, genes, or complexes and edges mimic associations among them. The study of these biological network structures can provide insight into many biological questions including the functional characterization of genes and gene products, the characterization of DNA-protein bindings, and the understanding of regulatory mechanisms. Therefore, the task of constructing biological interaction networks from raw data sets and exploiting information from these networks is critical, but is also fraught with challenges. First, the network structure is not always known in a priori; the structure should be inferred from raw and heterogeneous biological data sources. Second, biological networks are noisy (containing unreliable interactions) and incomplete (missing real interactions) which makes the task of extracting useful information difficult. Third, typically these networks have non-trivial topological properties (e.g., uneven degree distribution, small world) that limit the effectiveness of traditional knowledge discovery algorithms. Fourth, these networks are usually dynamic and investigation of their dynamics is essential to understand the underlying biological system. In this thesis, we address these issues by presenting a set of computational techniques that we developed to construct and analyze three specific types of biological interaction networks: protein-protein interaction networks, gene co-expression networks, and regulatory networks.