This book presents methods and tools of network biology and bioinformatics for understanding the disease dynamics and identification of drug targets. The initial section of chapters introduce the theoretical aspects followed by the different applications for construction and analysis of biological networks, methods for identifying crucial nodes in networks, and network dynamics. The book covers the latest advances in the network medicine, exploring the different types of biological networks, and their applications. It further reviews the role of R language in the network-based approaches that help in understanding biological systems and identifying biological functions. Towards the end, the book explores the recent developments and applications in machine learning and its potential for advancing network biology. Finally, the book elucidates a comprehensive yet a representative description of challenges associated with the understanding of disease dynamics using network biology. Given its scope, the book is intended for researchers and advanced postgraduate students of bioinformatics, computational biology, and medical sciences. ​

RNA-based Regulation in Human Health and Disease offers an in-depth exploration of RNA mediated genome regulation at different hierarchies. Beginning with multitude of canonical and non-canonical RNA populations, especially noncoding RNA in human physiology and evolution, further sections examine the various classes of RNAs (from small to large noncoding and extracellular RNAs), functional categories of RNA regulation (RNA-binding proteins, alternative splicing, RNA editing, antisense transcripts and RNA G-quadruplexes), dynamic aspects of RNA regulation modulating physiological homeostasis (aging), role of RNA beyond humans, tools and technologies for RNA research (wet lab and computational) and future prospects for RNA-based diagnostics and therapeutics. One of the core strengths of the book includes spectrum of disease-specific chapters from experts in the field highlighting RNA-based regulation in metabolic & neurodegenerative disorders, cancer, inflammatory disease, viral and bacterial infections. We hope the book helps researchers, students and clinicians appreciate the role of RNA-based regulation in genome regulation, aiding the development of useful biomarkers for prognosis, diagnosis, and novel RNA-based therapeutics. - Comprehensive information of non-canonical RNA-based genome regulation modulating human health and disease - Defines RNA classes with special emphasis on unexplored world of noncoding RNA at different hierarchies - Disease specific role of RNA - causal, prognostic, diagnostic and therapeutic - Features contributions from leading experts in the field

This book presents methods and tools of network biology and bioinformatics for understanding the disease dynamics and identification of drug targets. The initial section of chapters introduce the theoretical aspects followed by the different applications for construction and analysis of biological networks, methods for identifying crucial nodes in networks, and network dynamics. The book covers the latest advances in the network medicine, exploring the different types of biological networks, and their applications. It further reviews the role of R language in the network-based approaches that help in understanding biological systems and identifying biological functions. Towards the end, the book explores the recent developments and applications in machine learning and its potential for advancing network biology. Finally, the book elucidates a comprehensive yet a representative description of challenges associated with the understanding of disease dynamics using network biology. Given its scope, the book is intended for researchers and advanced postgraduate students of bioinformatics, computational biology, and medical sciences. ​

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.

This book presents a range of current research topics in biological network modeling, as well as its application in studies on human hosts, pathogens, and diseases. Systems biology is a rapidly expanding field that involves the study of biological systems through the mathematical modeling and analysis of large volumes of biological data. Gathering contributions from renowned experts in the field, some of the topics discussed in depth here include networks in systems biology, the computational modeling of multidrug-resistant bacteria, and systems biology of cancer. Given its scope, the book is intended for researchers, advanced students, and practitioners of systems biology. The chapters are research-oriented, and present some of the latest findings on their respective topics.

Introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, using real-world biological and medical examples.

Motivated by the explosion of molecular data on humans-particularly data associated with individual patients-and the sense that there are large, as-yet-untapped opportunities to use this data to improve health outcomes, Toward Precision Medicine explores the feasibility and need for "a new taxonomy of human disease based on molecular biology" and develops a potential framework for creating one. The book says that a new data network that integrates emerging research on the molecular makeup of diseases with clinical data on individual patients could drive the development of a more accurate classification of diseases and ultimately enhance diagnosis and treatment. The "new taxonomy" that emerges would define diseases by their underlying molecular causes and other factors in addition to their traditional physical signs and symptoms. The book adds that the new data network could also improve biomedical research by enabling scientists to access patients' information during treatment while still protecting their rights. This would allow the marriage of molecular research and clinical data at the point of care, as opposed to research information continuing to reside primarily in academia. Toward Precision Medicine notes that moving toward individualized medicine requires that researchers and health care providers have access to very large sets of health- and disease-related data linked to individual patients. These data are also critical for developing the information commons, the knowledge network of disease, and ultimately the new taxonomy.

In recent years, powered by evolving technologies and experimental design, studies have better illuminated the regulating role of proteolytic enzymes across human development and pathologies. Proteolytic Signaling in Health and Disease provides an in-depth discussion of fundamental physiological and developmental processes regulated by proteases, from protein turnover and autophagy to antigen processing and presentation and major histocompatibility complex (MHC) molecules. Moving on from basic biology, international chapter authors examine a range of pathological conditions associated with proteolysis, including inflammation, wound healing, and cancer. Later chapters discuss the newly discovered network of connected events among proteases (and their inhibitors), the so-called 'protease web', and how best to study it. This book also empowers new research with up-to-date analytical methods and step-by-step protocols for studying proteolytic signaling events. - Examines biological events triggered by proteolytic enzyme activity across human development and pathologies - Discusses the role of proteolytic signaling in inflammation, wound healing, and cancer, among other disease types - Features methods and protocols supporting further study of proteolytic signaling events - Includes chapter contributions from international leaders in the field

Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials.

A fresh examination of the past successes of natural products as medicines and their new future from both conventional and new technologies. High-performance liquid chromatography profiling, combinatorial synthesis, genomics, proteomics, DNA shuffling, bioinformatics, and genetic manipulation all now make it possible to rapidly evaluate the activities of extracts as well as purified components derived from microbes, plants, and marine organisms. The authors apply these methods to new natural product drug discoveries, to microbial diversity, to specific groups of products (Chinese herbal drugs, antitumor drugs from microbes and plants, terpenoids, and arsenic compounds), and to specific sources (the sea, rainforest, and endophytes). These new opportunities show how research and development trends in the pharmaceutical industry can advance to include both synthetic compounds and natural products, and how this paradigm shift can be more productive and efficacious.