Evolution and Networks: an Analytic Path to Personalized Medicine

Progress in medicine and biology has been so steep that data interpretation has now become an even greater bottleneck than data generation.
These data are either structured or unstructured, i.e. found either in databases or in texts; and they tell about biological interactions within species or about evolutionary relationships among them.
Our hypothesis is that it is possible to integrate all of these heterogeneous and massive data types coherently via a single network, and that in turn, the topological properties of this networks predict novel relationships and mechanisms that either explain the behavior of an organism as well as novel targets for drugs that modify it.
Support for this hypothesis comes from multiple predictions that are experimentally validated, including:
a) the identification of protein function and substrates with and without structures; and
b) the elucidation of new post-translational modifier-protein target and Protein-Protein  Interaction pairs.
Together with other evolutionary tools to identify disease-causing mutations and genes, this integrative network approach opens many possibilities, including the design of personalized therapy for precision medicine