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Complex Systems Science in Biomedicine Thomas S. Deisboeck and J. Yasha Kresh Complex Systems Science in Biomedicine covers the emerging field of systems science involving the application of physics, mathematics, engineering and computational methods and techniques to the study of biomedicine including nonlinear dynamics at the molecular, cellular, multi-cellular tissue, and organismic level. With all chapters helmed by leading scientists in the field, Complex Systems Science in Biomedicine's goal is to offer its audience a timely compendium of the ongoing research directed to the understanding of biological processes as whole systems instead of as isolated component parts. In Parts I & II, Complex Systems Science in Biomedicine provides a general systems thinking perspective and presents some of the fundamental theoretical underpinnings of this rapidly emerging field. Part III then follows with a multi-scaled approach, spanning from the molecular to macroscopic level, exemplified by studying such diverse areas as molecular networks and developmental processes, the immune and nervous systems, the heart, cancer and multi-organ failure. The volume concludes with Part IV that addresses methods and techniques driven in design and development by this new understanding of biomedical science. Key Topics Include: • Historic Perspectives of General Systems Thinking • Fundamental Methods and Techniques for Studying Complex Dynamical Systems • Applications from Molecular Networks to Disease Processes • Enabling Technologies for Exploration of Systems in the Life Sciences Complex Systems Science in Biomedicine is essential reading for experimental, theoretical, and interdisciplinary scientists working in the biomedical research field interested in a comprehensive overview of this rapidly emerging field. About the Editors: Thomas S. Deisboeck is currently Assistant Professor of Radiology at Massachusetts General Hospital and Harvard Medical School in Boston. An expert in interdisciplinary cancer modeling, Dr. Deisboeck is Director of the Complex Biosystems Modeling Laboratory which is part of the Harvard-MIT Martinos Center for Biomedical Imaging. J. Yasha Kresh is currently Professor of Cardiothoracic Surgery and Research Director, Professor of Medicine and Director of Cardiovascular Biophysics at the Drexel University College of Medicine. An expert in dynamical systems, he holds appointments in the School of Biomedical Engineering and Health Systems, Dept. of Mechanical Engineering and Molecular Pathobiology Program. Prof. Kresh is Fellow of the American College of Cardiology, American Heart Association, Biomedical Engineering Society, American Institute for Medical and Biological Engineering.
Systems biology is a critical emerging field that quantifies and annotates the complexity of biological systems in order to construct algorithmic models to predict outcomes from component input. Applications in medicine are revolutionizing our understanding of biological processes and systems. Systems Biomedicine is organized around foundations, computational modeling, network biology, and integrative biology, with the extension of examples from human biology and pharmacology, to focus on the applications of systems approaches to medical problems. An integrative approach to the underlying genomic, proteomic, and computational biology principles provides researchers with guidance in the use of qualitative systems and hypothesis generators. To reflect the highly interdisciplinary nature of the field, careful detail has been extended to ensure explanations of complex mathematical and biological principles are clear with minimum technical jargon. - Organized to reflect the important distinguishing characteristics of systems strategies in experimental biology and medicine - Provides precise and comprehensive measurement tools for constructing a model of the system and tools for defining complexity as an experimental dependent variable - Includes a thorough discussion of the applications of quantitative principles to biomedical problems
This encyclopedia provides an authoritative single source for understanding and applying the concepts of complexity theory together with the tools and measures for analyzing complex systems in all fields of science and engineering. It links fundamental concepts of mathematics and computational sciences to applications in the physical sciences, engineering, biomedicine, economics and the social sciences.
This unique title explores complex systems in clinical medicine and the subsequent implementation of that knowledge into practice. Written conversationally and as a reflection on the journey of learning about complex systems, the book explores how knowledge of these systems can be applied to four key roles in academic medicine: clinical practice, education, research, and administration. Further, this title emphasizes how gaining an understanding of complex systems can greatly help a physician deal with the many challenges found in academic medicine. Unlike other books on complexity in medicine, which tend to focus on only one aspect of the management of patients, Complex Systems in Medicine deals with the multifaceted roles of a physician. The approach in this book is uniquely qualitative rather than mathematical, and is written to make it not only of interest to physicians, trainees, and allied health providers, but also to make it more accessible to a non-medical audience. The inclusion of personal anecdotes by the author provides concrete examples of the application of knowledge of complex systems in academic medicine. A first-of-its-kind contribution to the literature, Complex Systems in Medicine: A Hedgehog’s Tale of Complexity in Clinical Practice, Research, Education, and Management is not only a novel reference for medical professionals, it is an accessible tool for the non-medical audience hoping to learn more about complex systems and their direct relevance to medicine, a field that deals with the infinite variety of humans and their ills. It illustrates the consequences of the interactive elements of patient care that make medicine both a science and an art.
The first edition of this text, based on the author's 30 years of teaching and research on neurosensory systems, helped biomedical engineering students and professionals strengthen their skills in the common network of applied mathematics that ties together the diverse disciplines that comprise this field. Updated and revised to include new materia
This volume presents a timely and comprehensive overview of biological networks at all organization levels in the spirit of the complex system approach. It discusses the transversal issues and fundamental principles as well as the overall structure, dynamics, and modeling of a wide array of biological networks at the molecular, cellular, and population levels. Anchored in both empirical data and a strong theoretical background, the book therefore lends valuable credence to the complex systems approach.
Systems Biomedicine is a field in perpetual development. By definition a translational discipline, it emphasizes the role of quantitative systems approaches in biomedicine and aims to offer solutions to many emerging problems characterized by levels and types of complexity and uncertainty unmet before. Many factors, including technological and societal ones, need to be considered. In particular, new technologies are providing researchers with the data deluge whose management and exploitation requires a reinvention of cross-disciplinary team efforts. The advent of “omics” and high-content imaging are examples of advances de facto establishing the necessity of systems approaches. Hypothesis-driven models and in silico validation tools in support to all the varieties of experimental applications call for a profound revision. The focus on phases like mining and assimilating the data has substantially increased so to allow for interpretable knowledge to be inferred. Notably, to be able to tackle the newly generated data dimensionality, heterogeneity and complexity, model-free and data-driven intensive applications are increasingly shaping the computational pipelines and architectures that quant specialists set aside of the high-throughput genomics, transcriptomics, proteomics platforms. As for the societal aspects, in many advanced societies health care needs now more than in the past to address the problem of managing ageing populations and their complex morbidity patterns. In parallel, there is a growing research interest on the impact that cross-disciplinary clinical, epidemiological and quantitative modelling studies can have in relation to outcomes potentially affecting the quality of life of many people. Complex systems, including those characterizing biomedicine, are assessed in both their functionality and stability, and also relatively to the capacity of generating information from diversity, variation, and complexity. Due to the combined interactions and effects, such systems embed prediction power available for instance in both target identification or marker discovery, or more generally for conducting inference about patients’ pathological states, i.e. normal versus disease, diagnostic or prognostic analysis, and preventive assessment (e.g., risk evaluation). The ultimate goal, personalized medicine, will be achieved based on the confluence of the system’s predictive power to patient-specific profiling.
Following the intense research activIties of the last decade, artificial neural networks have emerged as one of the most promising new technologies for improving the quality of healthcare. Many successful applications of neural networks to biomedical problems have been reported which demonstrate, convincingly, the distinct benefits of neural networks, although many ofthese have only undergone a limited clinical evaluation. Healthcare providers and developers alike have discovered that medicine and healthcare are fertile areas for neural networks: the problems here require expertise and often involve non-trivial pattern recognition tasks - there are genuine difficulties with conventional methods, and data can be plentiful. The intense research activities in medical neural networks, and allied areas of artificial intelligence, have led to a substantial body of knowledge and the introduction of some neural systems into clinical practice. An aim of this book is to provide a coherent framework for some of the most experienced users and developers of medical neural networks in the world to share their knowledge and expertise with readers.
Containing papers presented at the 2016 New Forest Conference on Complex Systems, this multi-disciplinary book presents new approaches for resolving complex issues that cannot be resolved using conventional mathematical or software models. Complex Systems occur in an infinite variety of problems encompassing fields as diverse as economics, the environment, humanities, social and political sciences, physical sciences and engineering. The papers in the book cover such topics as: Complex business processes; Supply chain complexity; Complex adaptive software; Management of complexity; Complexity in social systems; Complexity in engineering; Complex issues in biological and medical sciences; Complex energy systems Complexity and evolution.
Enormous advances in information technology have permeated essentially all facets of life in the past two decades. Formidable challenges remain in fostering tools that enhance productivity but are sensitive to work practices. Cognitive Informatics (CI) is the multidisciplinary study of cognition, information and computational sciences that investigates all facets of human computing including design and computer-mediated intelligent action, thus is strongly grounded in methods and theories from cognitive science. As an applied discipline, it has a close affiliation with human factors and human-computer interaction, and provides a framework for the analysis and modeling of complex human performance in technology-mediated settings and contributes to the design and development of better information systems. In recent years, CI has emerged as a distinct area with special relevance to biomedicine and health care. In addition, it has become a foundation for education and training of health informaticians, the Office of the National Coordinator for Health Information Technology initiating a program including CI as one of its critical elements to support health IT curriculum development. This book represents a first textbook on cognitive informatics and will focus on key examples drawn from the application of methods and theories from CI to challenges pertaining to the practice of critical-care medicine (CCM). Technology is transforming critical care workflows and re-organizing patient care management processes. CCM has proven to be a fertile test bed for theories and methods of cognitive informatics. CI, in turn, has contributed much to our understanding of the factors that result in complexity and patient errors. The topic is strongly interdisciplinary and will be important for individuals from a range of academic and professional backgrounds, including critical care specialists, psychologists, computer scientists, medical informaticians, and anthropologists.​​