Proteins carry out their functions by interacting with other proteins and small molecules, forming a complex interaction network. In this review, we briefly introduce classical graph theory based protein-protein interaction networks. We also describe the commonly used experimental methods to construct these networks, and the insights that can be gained from these networks. We then discuss the recent transition from graph theory based networks to structure based protein-protein interaction networks and the advantages of the latter over the former, using two networks as examples. We further discuss the usefulness of structure based protein-protein interaction networks for drug discovery, with a special emphasis on drug repositioning.

The KEGG pathway maps are widely used as a reference data set for inferring high-level functions of the organism or the ecosystem from its genome or metagenome sequence data. The KEGG modules, which are tighter functional units often corresponding to subpathways in the KEGG pathway maps, are designed for better automation of genome interpretation. Each KEGG module is represented by a simple Boolean expression of KEGG Orthology (KO) identifiers (K numbers), enabling automatic evaluation of the completeness of genes in the genome. Here we focus on metabolic functions and introduce reaction modules for improving annotation and signature modules for inferring metabolic capacity. We also describe how genome annotation is performed in KEGG using the manually created KO database and the computationally generated SSDB database. The resulting KEGG GENES database with KO (K number) annotation is a reference sequence database to be compared for automated annotation and interpretation of newly determined genomes.

A central goal of synthetic biology is to apply successful principles that have been developed in electronic and chemical engineering to construct basic biological functional modules, and through rational design, to build synthetic biological systems with predetermined functions. Here, we apply the reverse engineering design principle of biological networks to synthesize a gene circuit that executes semi-log dose-response, a logarithmically linear sensing function, in Escherichia coli cells. We first mathematically define the object function semi-log dose-response, and then search for tri-node network topologies that can most robustly execute the object function. The simplest topology, transcriptional coherent feed-forward loop (TCFL), among the searching results is mathematically analyzed; we find that, in TCFL topology, the semi-log dose-response function arises from the additive effect of logarithmical linearity intervals of Hill functions. TCFL is then genetically implemented in E. coli as a logarithmically linear sensing biosensor for heavy metal ions [mercury (II)]. Functional characterization shows that this rationally designed biosensor circuit works as expected. Through this study we demonstrated the potential application of biological network reverse engineering to broaden the computational power of synthetic biology.