Type-2 diabetes mellitus (T2DM) is a complex disease with multiple causes covering several functional entities of the metabolism. Environmental factors contribute to the pathogenesis of the disease – most notably nutrition and weight of the organism. The identification of disease genes is the driving power of many research projects. In a previous paper (Rasche et al. 2008) we presented a method that integrates results from different T2DM related studies and identifies candidate genes with high disease relevance. This chapter is designated to elaborate on our work from a network based perspective. Network biology is a promising field that can shed light on interrelations between disease genes and from disease genes to their functional neighborhood. We use network-based tools to advance from a single-gene analysis towards a subnet, a functional module, of disease genes. Proteins are gene products that are associated with particular molecular functions. Molecular functions are interpreted as activities that can be performed by individual proteins following the definitions introduced by the Gene Ontology Consortium (Ashburner et al. 2000). Examples of molecular functions are catalytic activity, transporter activity or binding. Additionally, a biological process is accomplished by one or more ordered assemblies of molecular functions (Ashburner et al. 2000). Proteins physically interact with each other in order to carry out a biological function. A biological function is related to the term biological process. A signal transduction cascade whose biological function is to transmit information from a receptor to a transcription factor is a succession of protein-protein interactions (PPIs). Both the molecular function of a protein and the biological function in which it is involved are best deduced by studying the environment where it operates in. To this end, scientists pursue the ambitious goal of assembling all PPIs in an organism – the interactome – to elucidate how proteins work together and promote individual biological processes and eventually the complete cellular machinery. Today, mainly two methods are used to detect PPIs: Yeast two-hybrid screens (Fields & Sternglanz 1994) and affinity purification (Pandey & Mann 2000). These large-scale technologies provide vast numbers of interactions but have high false positive rates. Additionally, such experiments only reflect one environmental condition and not the dynamics of interactions between different phyiological states leading to high false negative rates. Regarding the current size of the human interactome, we have only a draft of the complete set of interactions. However, looking at the course of construction (fig. 1) so far and bearing in mind new quality standards we are continuously moving towards the completion of a