Network Medicine: Complex Systems in Human Disease and Therapeutics

J. Loscalzo, A.-L. Barabási, E. K. Silverman (eds.)
Harvard University Press
2017
February 15, 2017

Abstract

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.

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