Network Medicine: From Conceptual Frameworks to Applications and Future Trends

Abstract

The intricate nature of biological processes is orchestrated by molecular interactions. The complexity of these interactions stems from the sheer number of components involved and their relationships. To overcome this complexity, network medicine adopts a holistic, integrative approach at multiple levels. The human interactome involves over 100,000 molecules, including proteins, RNAs, and metabolites, all interconnected by a network of connections. One challenge in understanding the human interactome is associating specific parts of this network with biological phenomena such as diseases, drug resistance, and other abnormalities. Although molecular measurements can quantitatively identify many altered molecules, making sense of these molecular changes within the broader network context is a formidable task. Notably, alterations in the human interactome often occur in closely connected regions of the network. By using prior biological knowledge and applying the context-specific molecular interplays, specific sub-networks can be extracted. These network modules can provide valuable insights into complex biological questions. Furthermore, a range of learning and graph-based methodologies are employed to deduce meaningful clinical outcomes in these modules. In this context, we present a comprehensive overview of the standard workflows utilized in network medicine, along with a discussion of its applications and future directions.