Systems Biomedicine

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

The future of oncology seems to lie in Molecular Medicine (MM). MM is a new science based on three pillars. Two of them are evident in its very name and are well known: medical science and molecular biology. However, there is a general unawareness that MM is firmly based on a third, and equally important, pillar: Systems Biomedicine. Currently, this term denotes multilevel, hierarchical models integrating key factors at the molecular, cellular, tissue, through phenotype levels, analyzed to reveal the global behavior of the biological process under consideration. It becomes increasingly evident that the tools to construct such complex models include, not only bioinformatics and modern applied statistics, as is unanimously agreed, but also other interdisciplinary fields of science, notably, Mathematical Oncology, Systems Biology and Theoretical Biophysics.

This book presents the applications of systems biology and synthetic biology in cancer medicine. It highlights the use of computational and mathematical models to decipher the complexity of cancer heterogeneity. The book emphasizes the modeling approaches for predicting behavior of cancer cells, tissues in context of drug response, and angiogenesis. It introduces cell-based therapies for the treatment of various cancers and reviews the role of neural networks for drug response prediction. Further, it examines the system biology approaches for the identification of medicinal plants in cancer drug discovery. It explores the opportunities for metabolic engineering in the realm of cancer research towards development of new cancer therapies based on metabolically derived targets. Lastly, it discusses the applications of data mining techniques in cancer research. This book is an excellent guide for oncologists and researchers who are involved in the latest cancer research.

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.

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.

Examining medical pluralism in the United States from the Revolutionary War period through the end of the twentieth century, Hans Baer brings together in one convenient reference a vast array of information on healing systems as diverse as Christian Science, osteopathy, acupuncture, Santeria, southern Appalachian herbalism, evangelical faith healing, and Navajo healing. In a country where the dominant paradigm of biomedicine (medical schools, research hospitals, clinics staffed by M.D.s and R.N.s) has been long established and supported by laws and regulations, the continuing appeal of other medical systems and subsystems bears careful consideration. Distinctions of class, Baer emphasizes, as well as differences in race, ethnicity, and gender, are fundamental to the diversity of beliefs, techniques, and social organizations represented in the phenomenon of medical pluralism. Baer traces the simultaneous emergence in the nineteenth century of formalized biomedicine and of homeopathy, botanic medicine, hydropathy, Christian Science, osteopathy, and chiropractic. He examines present-day osteopathic medicine as a system parallel to biomedicine with an emphasis on primary care; chiropractic, naturopathy, and acupuncture as professionalized heterodox medical systems; homeopathy, herbalism, bodywork, and lay midwifery in the context of the holistic health movement; Anglo-American religious healing; and folk medical systems, particularly among racial and ethnic minorities. In closing he focuses on the persistence of folk medical systems among working-class Americans and considers the growing interest of biomedical physicians, pharmaceutical and healthcare corporations, and government in the holistic health movement

Mathematicalmodelingofhumanphysiopathologyisatremendouslyambitioustask. It encompasses the modeling of most diverse compartments such as the cardiovas- lar,respiratory,skeletalandnervoussystems,aswellasthemechanicalandbioch- ical interaction between blood ?ow and arterial walls, and electrocardiac processes and electric conduction in biological tissues. Mathematical models can be set up to simulate both vasculogenesis (the aggregation and organization of endothelial cells dispersed in a given environment) and angiogenesis (the formation of new vessels sprouting from an existing vessel) that are relevant to the formation of vascular networks, and in particular to the description of tumor growth. The integration of models aimed at simulating the cooperation and interrelation of different systems is an even more dif?cult task. It calls for the setting up of, for instance, interaction models for the integrated cardio-vascular system and the interplay between the central circulation and peripheral compartments, models for the mid-to-long range cardiovascular adjustments to pathological conditions (e.g., to account for surgical interventions, congenital malformations, or tumor growth), models for integration among circulation, tissue perfusion, biochemical and thermal regulation, models for parameter identi?cation and sensitivity analysis to parameter changes or data uncertainty – and many others.

The book focuses on various detection targets applied in single cell studies, including tumor tissue cells, circulating tumor cells (CTCs), disseminated tumor cells (DTCs), circulating tumor DNA (ctDNA), cell-free DNA (cfDNA) and cancer stem cells (CSCs). It also discusses and compares detection methods using these detection targets in different fields to reveal single cell biomedical functions. The volume focuses not only on the methods already been established and validated, and also the methods newly developed. The book also highlights the importance and potential of single cell biomedicine in the development and validation of precision medicine strategies. It is useful for researchers and students in the field of cell biology, molecular medicine and precision medicine etc.