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