LAUSR

Reductionist approaches have served as the cornerstone for traditional mechanistic endeavors in biomedical research. However, for pulmonary hypertension (PH), a relatively rare but deadly vascular disease of the lungs, the use of traditional reductionist approaches has failed to define the complexities of pathogenesis. With the development of new -omics platforms (i.e., genomics, transcriptomics, proteomics, and metabolomics, among others), network biology approaches have offered new pipelines for discovery of human disease pathogenesis. Human disease processes are driven by multiple genes that are dysregulated which are affected by regulatory networks. Network theory allows for the identification of such gene clusters which are dysregulated in various disease states. This framework may in part explain why current therapeutics that seek to target a single part of a dysregulated cluster may fail to provide clinically significant improvements. Correspondingly, network biology could further the development of novel therapeutics which target clusters of "disease genes" so that a disease phenotype can be more robustly addressed. In this chapter, we seek to explain the theory behind network biology approaches to identify drivers of disease as well as how network biology approaches have been used in the field of PH. Furthermore, we discuss an example of in silico methodology using network pharmacology in conjunction with gene networks tools to identify drugs and drug targets. We discuss similarities between the pathogenesis of PH and other disease states, specifically cancer, and how tools developed for cancer may be repurposed to fill the gaps in research in PH. Finally, we discuss new approaches which seek to integrate clinical health record data into networks so that correlations between disease genes and clinical parameters can be explored in the context of this disease.