Big data, genomics, and quantitative approaches to network-based analysis are combining to advance the frontiers of medicine as never before. Network Medicine introduces this rapidly evolving field of medical research, which promises to revolutionize the diagnosis and treatment of human diseases. With contributions from leading experts that highlight the necessity of a team-based approach in network medicine, this definitive volume provides readers with a state-of-the-art synthesis of the progress being made and the challenges that remain.

Medical researchers have long sought to identify single molecular defects that cause diseases, with the goal of developing silver-bullet therapies to treat them. But this paradigm overlooks the inherent complexity of human diseases and has often led to treatments that are inadequate or fraught with adverse side effects. Rather than trying to force disease pathogenesis into a reductionist model, network medicine embraces the complexity of multiple influences on disease and relies on many different types of networks: from the cellular-molecular level of protein-protein interactions to correlational studies of gene expression in biological samples. The authors offer a systematic approach to understanding complex diseases while explaining network medicine’s unique features, including the application of modern genomics technologies, biostatistics and bioinformatics, and dynamic systems analysis of complex molecular networks in an integrative context.

By developing techniques and technologies that comprehensively assess genetic variation, cellular metabolism, and protein function, network medicine is opening up new vistas for uncovering causes and identifying cures of disease.

In this modern era of mathematical modeling, applications have become increasingly complicated. As the complexity grows, it becomes more and more difficult to draw meaningful conclusions about the behavior of theoretical models and their relations to reality. Alongside methods that emphasize quantitative properties and the testing of scientific details, there is a need for approaches that are more qualitative. These techniques attempt to cover whole families of models in one bold stroke, in a manner that allows robust conclusions to be drawn about them.

Loop analysis and time averaging provide a means of interpreting the properties of systems from the network of interactions within the system. The authors' methodology concentrates on graphical representation to guide experimental design, to identify sources of external variability from the statistical pattern of variables, and to make management decisions.

Although most of the examples are drawn from ecology, the methods are relevant to all of the pure and applied sciences. This relevance is enhanced by case studies from such diverse areas as physiology, resource management, the behavioral sciences, and social epidemiology. The book will be useful to a broad readership from the biological and social sciences as well as the physical sciences and technology. It will interest undergraduate and graduate students along with researchers active in these disciplines. Here the reader will find a strong rationale for maintaining a holistic approach, revealing what insights and advantages are retained by the broader perspective and, more explicitly, by the synergistic effects that cannot be discerned by reducing systems to their smallest parts.