Systems Biomedicine
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Systems Biomedicine
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Author : Edison T. Liu
language : en
Publisher: Academic Press
Release Date : 2009-09-17
Systems Biomedicine written by Edison T. Liu and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-09-17 with Science categories.
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
Comprehensive Systems Biomedicine
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Author : Pietro Lio
language : en
Publisher: Frontiers E-books
Release Date : 2014-12-03
Comprehensive Systems Biomedicine written by Pietro Lio and has been published by Frontiers E-books this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-03 with Genetics categories.
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
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Author : Thomas Deisboeck
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-06-13
Complex Systems Science In Biomedicine written by Thomas Deisboeck and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-06-13 with Medical categories.
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.
New Challenges For Cancer Systems Biomedicine
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Author : Alberto D'Onofrio
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-01-25
New Challenges For Cancer Systems Biomedicine written by Alberto D'Onofrio and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-01-25 with Mathematics categories.
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.
Systems Biomedicine Approaches In Cancer Research
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Author : Shailza Singh
language : en
Publisher: Springer Nature
Release Date : 2022-08-10
Systems Biomedicine Approaches In Cancer Research written by Shailza Singh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-10 with Science categories.
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.
Metadata Driven Software Systems In Biomedicine
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Author : Prakash M. Nadkarni
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-05-27
Metadata Driven Software Systems In Biomedicine written by Prakash M. Nadkarni and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-05-27 with Medical categories.
While the use of database technology is ubiquitous throughout IT (and health IT in particular), it is not generally appreciated that, as a database increases in scope, certain designs are far superior to others. In biomedical domains, new knowledge is being generated continually, and the databases that must support areas such as clinical care and research must also be able to evolve while requiring minimal or no logical / physical redesign. Appropriately designed metadata, and software designed to utilize it effectively, can provide significant insulation against change. Many of the larger EMR or clinical research database vendors have realized this, but their designs are proprietary and not described in the literature. Consequently, numerous misconceptions abound among individuals who have not had to work with large-scale biomedical systems, and graduates of a health or bioinformatics program may find that they need to unlearn what they were taught in database and software design classes in order to work productively with such systems. A working knowledge of such systems is also important for individuals who are not primarily software developers, such as health informaticians, medical information officers and data analysts. This book is, in a sense, intended to prepare all of the above individuals for the real world.
Complex Systems Science In Biomedicine
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Author : Thomas Deisboeck
language : en
Publisher: Springer
Release Date : 2010-11-16
Complex Systems Science In Biomedicine written by Thomas Deisboeck and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-11-16 with Medical categories.
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.
A Systems Approach To Biomedicine
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Author : William B. Blesser
language : en
Publisher:
Release Date : 1969
A Systems Approach To Biomedicine written by William B. Blesser and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1969 with Technology & Engineering categories.
Biomedicine And Alternative Healing Systems In America
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Author : Hans A. Baer
language : en
Publisher: Univ of Wisconsin Press
Release Date : 2001
Biomedicine And Alternative Healing Systems In America written by Hans A. Baer and has been published by Univ of Wisconsin Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Alternative medicine categories.
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
Model Based Hypothesis Testing In Biomedicine
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Author : Rikard Johansson
language : en
Publisher: Linköping University Electronic Press
Release Date : 2017-10-03
Model Based Hypothesis Testing In Biomedicine written by Rikard Johansson and has been published by Linköping University Electronic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-03 with categories.
The utilization of mathematical tools within biology and medicine has traditionally been less widespread compared to other hard sciences, such as physics and chemistry. However, an increased need for tools such as data processing, bioinformatics, statistics, and mathematical modeling, have emerged due to advancements during the last decades. These advancements are partly due to the development of high-throughput experimental procedures and techniques, which produce ever increasing amounts of data. For all aspects of biology and medicine, these data reveal a high level of inter-connectivity between components, which operate on many levels of control, and with multiple feedbacks both between and within each level of control. However, the availability of these large-scale data is not synonymous to a detailed mechanistic understanding of the underlying system. Rather, a mechanistic understanding is gained first when we construct a hypothesis, and test its predictions experimentally. Identifying interesting predictions that are quantitative in nature, generally requires mathematical modeling. This, in turn, requires that the studied system can be formulated into a mathematical model, such as a series of ordinary differential equations, where different hypotheses can be expressed as precise mathematical expressions that influence the output of the model. Within specific sub-domains of biology, the utilization of mathematical models have had a long tradition, such as the modeling done on electrophysiology by Hodgkin and Huxley in the 1950s. However, it is only in recent years, with the arrival of the field known as systems biology that mathematical modeling has become more commonplace. The somewhat slow adaptation of mathematical modeling in biology is partly due to historical differences in training and terminology, as well as in a lack of awareness of showcases illustrating how modeling can make a difference, or even be required, for a correct analysis of the experimental data. In this work, I provide such showcases by demonstrating the universality and applicability of mathematical modeling and hypothesis testing in three disparate biological systems. In Paper II, we demonstrate how mathematical modeling is necessary for the correct interpretation and analysis of dominant negative inhibition data in insulin signaling in primary human adipocytes. In Paper III, we use modeling to determine transport rates across the nuclear membrane in yeast cells, and we show how this technique is superior to traditional curve-fitting methods. We also demonstrate the issue of population heterogeneity and the need to account for individual differences between cells and the population at large. In Paper IV, we use mathematical modeling to reject three hypotheses concerning the phenomenon of facilitation in pyramidal nerve cells in rats and mice. We also show how one surviving hypothesis can explain all data and adequately describe independent validation data. Finally, in Paper I, we develop a method for model selection and discrimination using parametric bootstrapping and the combination of several different empirical distributions of traditional statistical tests. We show how the empirical log-likelihood ratio test is the best combination of two tests and how this can be used, not only for model selection, but also for model discrimination. In conclusion, mathematical modeling is a valuable tool for analyzing data and testing biological hypotheses, regardless of the underlying biological system. Further development of modeling methods and applications are therefore important since these will in all likelihood play a crucial role in all future aspects of biology and medicine, especially in dealing with the burden of increasing amounts of data that is made available with new experimental techniques. Användandet av matematiska verktyg har inom biologi och medicin traditionellt sett varit mindre utbredd jämfört med andra ämnen inom naturvetenskapen, såsom fysik och kemi. Ett ökat behov av verktyg som databehandling, bioinformatik, statistik och matematisk modellering har trätt fram tack vare framsteg under de senaste decennierna. Dessa framsteg är delvis ett resultat av utvecklingen av storskaliga datainsamlingstekniker. Inom alla områden av biologi och medicin så har dessa data avslöjat en hög nivå av interkonnektivitet mellan komponenter, verksamma på många kontrollnivåer och med flera återkopplingar både mellan och inom varje nivå av kontroll. Tillgång till storskaliga data är emellertid inte synonymt med en detaljerad mekanistisk förståelse för det underliggande systemet. Snarare uppnås en mekanisk förståelse först när vi bygger en hypotes vars prediktioner vi kan testa experimentellt. Att identifiera intressanta prediktioner som är av kvantitativ natur, kräver generellt sett matematisk modellering. Detta kräver i sin tur att det studerade systemet kan formuleras till en matematisk modell, såsom en serie ordinära differentialekvationer, där olika hypoteser kan uttryckas som precisa matematiska uttryck som påverkar modellens output. Inom vissa delområden av biologin har utnyttjandet av matematiska modeller haft en lång tradition, såsom den modellering gjord inom elektrofysiologi av Hodgkin och Huxley på 1950?talet. Det är emellertid just på senare år, med ankomsten av fältet systembiologi, som matematisk modellering har blivit ett vanligt inslag. Den något långsamma adapteringen av matematisk modellering inom biologi är bl.a. grundad i historiska skillnader i träning och terminologi, samt brist på medvetenhet om exempel som illustrerar hur modellering kan göra skillnad och faktiskt ofta är ett krav för en korrekt analys av experimentella data. I detta arbete tillhandahåller jag sådana exempel och demonstrerar den matematiska modelleringens och hypotestestningens allmängiltighet och tillämpbarhet i tre olika biologiska system. I Arbete II visar vi hur matematisk modellering är nödvändig för en korrekt tolkning och analys av dominant-negativ-inhiberingsdata vid insulinsignalering i primära humana adipocyter. I Arbete III använder vi modellering för att bestämma transporthastigheter över cellkärnmembranet i jästceller, och vi visar hur denna teknik är överlägsen traditionella kurvpassningsmetoder. Vi demonstrerar också frågan om populationsheterogenitet och behovet av att ta hänsyn till individuella skillnader mellan celler och befolkningen som helhet. I Arbete IV använder vi matematisk modellering för att förkasta tre hypoteser om hur fenomenet facilitering uppstår i pyramidala nervceller hos råttor och möss. Vi visar också hur en överlevande hypotes kan beskriva all data, inklusive oberoende valideringsdata. Slutligen utvecklar vi i Arbete I en metod för modellselektion och modelldiskriminering med hjälp av parametrisk ”bootstrapping” samt kombinationen av olika empiriska fördelningar av traditionella statistiska tester. Vi visar hur det empiriska ”log-likelihood-ratio-testet” är den bästa kombinationen av två tester och hur testet är applicerbart, inte bara för modellselektion, utan också för modelldiskriminering. Sammanfattningsvis är matematisk modellering ett värdefullt verktyg för att analysera data och testa biologiska hypoteser, oavsett underliggande biologiskt system. Vidare utveckling av modelleringsmetoder och tillämpningar är därför viktigt eftersom dessa sannolikt kommer att spela en avgörande roll i framtiden för biologi och medicin, särskilt när det gäller att hantera belastningen från ökande datamängder som blir tillgänglig med nya experimentella tekniker.