Although this treatment failed to affect Igf1 mRNA levels at this skeletal site in wild type littermates. Our data in Igf1-null mice also indicate that PTHrP was effective in promoting ERK1/2 and p38a phosphorylation, suggesting that these pathways could be effectively modulated through IGF-II/IGF1R. On the contrary, the decreased p-AKT levels in these mice were not normalized after treatment with either PTHrP peptide. Therefore, PI3K/AKT pathway activation by PTHrP in bone seems to be IGF-I-dependent. These aggregated findings confirm and extend the reported skeletal defects of Igf1-null mice using other mouse strains. In addition, our data in mice on a hybrid MF1/129/Sv PCI-32765genetic background support the notion that PTHrP and osteostatin can exert osteogenic actions even in the absence of IGF-I. With the increasing availability of high-throughput, genomewide assay data and high-performance computational resources, network biology, which addresses the intrinsic structure and organisation of networks of pairwise biological interactions, has rapidly evolved as a promising research area. Viewing the functional machinery of the cell as a complex network of physical and logical interactions rather than a simple assembly of individual functional components has contributed unprecedented insight into the cell’s wiring scheme. The implications of methodology in network biology have been taken a step further by network medicine which focuses on the application to the understanding of complex disease pathophysiology. The fundamental hypothesis is that the impact of genetic and environmental disturbance upon disease phenotype is likely to be asserted through coordinated activity of a group of genes and their products which interact intensively, termed as disease modules. It has been argued that there is a significant overlap among the topological module, the functional module, and the disease module consisting of disease-associated genes. A primary objective in network medicine, therefore, PD 0332991 is to integrate the topological modules of biological networks and functional annotation to identify disease modules that contain both known and unknown disease genes and potential therapeutic targets. To identify disease modules with high confidence, the first and most important step is the identification of significant and robust topological modules in a network constructed from patient data. Several module identification algorithms was previously applied. One of the most popular algorithms is community detection algorithm that maximises a modularity measure brought forth by Newman. Though it is capable of yielding biological insight in several case studies, a major drawback of the community detection algorithm is the resolution limit problem which results in huge modules with large numbers of genes. Such problem is serious in disease module identification since it will inevitably introduce a lot of false disease genes and consequently adds difficulties to validation and interpretation. Another popular algorithm is Molecular Complex Detection, which only identifies the nodes that actually belong to a module.