Historically, scientific reductionism has provided the basis for our understanding of the causes of and treatments for disease. While this approach has often been successful, the great majority of diseases are complex and reflect many molecular interactions and responses defined by genomic context and environmental exposures. Given this complexity, the field of network medicine was developed, the fundamental principle of which is that a true understanding of disease definition, etiology, prognosis, and therapy requires a holistic approach to these complex molecular systems. In the current era of ‘big data’ and multi-omic data sets, we are poised to analyze these complex systems as seen through the lens of molecular interaction networks, including protein-protein interaction networks, Bayesian coexpression networks, and others. This new paradigm requires deep learning and artificial intelligence strategies, which, together, will guide us along the path to true precision medicine. Doing so requires a multidisciplinary approach involving experts in applied mathematics, biomedicine, computer science, engineering, and network science, and involves the development of novel analytical strategies for assessing the interactions among and between the elements of these multi-omic networks. This proposed symposium is designed to provide a contemporary, integrated view of this rapidly growing interdisciplinary paradigm, which offers the opportunity to identify novel personalized mechanisms of disease, unique biomarkers for disease prognosis, and novel drug targets for personalized therapies. In these ways, network medicine offers a novel path toward (re)defining and treating human disease in the modern era, and facilitates the development of precision medicine.
Joseph Loscalzo
Alberico L. Catapano