El. knyga: Network Medicine: Complex Systems in Human Disease and Therapeutics

"Network medicine, a new field which developed from the application of systems biology approaches to human disease, embraces the complexity of multifactorial influences on disease, which can be driven by non-linear effects and molecular and statistical interactions.The development of comprehensive and affordable Omics platforms provides the data types for network medicine, and graph theory and statistical physics provide the theoretical framework to analyze networks. While network medicine provides a fundamentally different approach to understanding disease etiology, it will also lead to key differences in how diseases are treated--with multiple molecular targets that may require manipulation in a coordinated, dynamic fashion. Much remains to be learned regarding the optimal approaches to integrate different Omics data types and to perform network analyses; this book provides an overview of the progress that has been made and the challenges that remain."--

Big data, genomics, and quantitative approaches to network-based analysis are combining to advance the frontiers of medicine as never before. With contributions from leading experts, Network Medicine introduces this rapidly evolving field of research, which promises to revolutionize the diagnosis and treatment of human diseases.

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.