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Viser: Computational Biomedicine
Computational Biomedicine
Peter Coveney, Vanessa Diaz, Peter Hunter og Marco Viceconti
(2014)
Oxford University Press, Incorporated
924,00 kr.
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Detaljer om varen
- Undefined: 432 sider
- Udgiver: Oxford University Press, Incorporated (August 2014)
- Forfattere: Peter Coveney, Vanessa Diaz, Peter Hunter og Marco Viceconti
- ISBN: 9780199658183
By the end of the twenty-first century, computer-based modelling and simulation will be integral to the way clinical decisions are taken. Drugs will be selected on the basis of an individual patient's "digital profile", while clinical treatments will be tailor-made for the individual in question. For this to come to pass, however, we must prepare for a future where we can simulate our own bodies with precision, drawing upon remarkable advances in information technology, computer, mathematical, physical and engineering science, data management, and high performance computing. Computational Biomedicine lays the foundations for a new approach to the subject - one that unifies the different strands of a broad-ranging subject, and demonstrates how computational biomedicine is a powerful tool with the potential to revolutionise our understanding of the human body, and the therapeutic strategies available to maintain and protect it. Written by a team of world-leading experts in the field, it explores the modelling of physiological systems at different scales - cells, tissues, organs - before considering the issues around biomedical computing, and data collection and analysis. It emphasises how a theoretical understanding of computational biomedicine translates into practice, with illustrative examples and case studies used throughout. Computational Biomedicine is the perfect introduction to the subject for anyone new to the field, from student to researcher.
1. Introduction2. Molecular Foundations of Computational Bioscience2.1 Introduction2.2 Types of Omics Data2.3 Databases and Data Sources2.4 Management of Omics Data Types2.5 Software Systems and Interoperability2.6 Clinical Phenotypes, Security and Data Sharing2.7 Conclusions3. Understanding the Genotype-Phenotype Relationship3.1 Introduction3.2 Quantitative Genetics Theory3.3 Systems Genetics3.4 Implementing CGP Models3.5 CGP Applications3.6 Linking CGP Models to Data3.7 Conclusions4. Image Based Modelling4.1 Introduction4.2 Biomedical Imaging Techniques4.3 Image Based Modelling4.4 Medical Image Simulation4.5 Statistical Atlases, Populational Imaging and Modelling4.6 Open Source Image Modelling Tools4.7 Conclusions5. Modelling Cell Function5.1 Introduction5.2 General Cell Functions5.3 Cell Fundamentals5.4 Levels of Abstraction5.5 Cell Simulation5.6 Approaches to Modelling and Simulation5.7 Simulation Tools5.8 Example: An Agent Model in Skeletal Mechanobiology5.9 Reproducible Modelling: Ordinary Differential Equations5.10 Conclusions6. Modelling Tissues and Organs6.1 Introduction6.2 Modelling Epithelia6.3 Cardiac Modelling6.4 Modelling the Gastro-Intestinal Tract6.5 Modelling Kidney Function and Homeostasis6.6 General Homeostasis and Blood Pressure Regulation6.7 Conclusions7. Multi-Scale Modelling7.1 Introduction7.2 Why Multi-Scale Modelling?7.3 A Framework for Multi-Scale Modelling and Computing7.4 Scale Bridging7.5 Multi-Scale Computing7.6 Example of a Multiscale Model: In-Stent Restenosis in Coronary Arteries7.7 Conclusions8. Workflows: Principles, Tools and Clinical Applications8.1 Introduction: What is a Workflow?8.2 Computational Workflows8.3 Workflow Implementations8.4 Provenance8.5 Examples of Scientific Workflows8.6 Key Considerations8.7 Conclusions9. Distributed Biomedical Computing9.1 Introduction9.2 Parallel Applications9.3 The Computational Ecosystem9.4 Computing Beyond the Desktop9.5 Simulations in a High Performance Computing Environment9.6 Case Study
1: Calculating Drug Binding Affinities9.7 Computational Infrastructures9.8 Distributed Applications9.9 Orchestrated Workflows from Distributed Applications9.10 Case Study
2: Computational Investigations of Cranial Haemodynamics9.11 Conclusions10. Managing Security and Privacy of Patient Data Sharing Platforms10.1 Introduction10.2 Legal Background10.3 Brief Overview of Information Security Concepts10.4 Common Data Sharing Requirements10.5 The Data Sharing Lifecycle10.6 Data Warehousing Architecture10.7 Conclusions11. Toward Clinical Deployment: Verification and Validation of Models11.1 Introduction: Technology Assessment versus Health Assessment11.2 Code and Model Verification11.3 Sensitivity Analysis11.4 Model Validation11.5 Validation of Integrative Models11.6 Clinical Accuracy11.7 Efficacy, Risk and Cost-Benefit11.8 Impact11.9 Sustainability11.10 ConclusionsAppendix: Modelling Standards and Model RepositoriesA.1 IntroductionA.2 Infrastructure for Computational BiomedicineA.3 Syntax, Semantics and Annotation of ModelsA.4 Markup LanguagesA.5 Model RepositoriesA.6 Conclusions
1: Calculating Drug Binding Affinities9.7 Computational Infrastructures9.8 Distributed Applications9.9 Orchestrated Workflows from Distributed Applications9.10 Case Study
2: Computational Investigations of Cranial Haemodynamics9.11 Conclusions10. Managing Security and Privacy of Patient Data Sharing Platforms10.1 Introduction10.2 Legal Background10.3 Brief Overview of Information Security Concepts10.4 Common Data Sharing Requirements10.5 The Data Sharing Lifecycle10.6 Data Warehousing Architecture10.7 Conclusions11. Toward Clinical Deployment: Verification and Validation of Models11.1 Introduction: Technology Assessment versus Health Assessment11.2 Code and Model Verification11.3 Sensitivity Analysis11.4 Model Validation11.5 Validation of Integrative Models11.6 Clinical Accuracy11.7 Efficacy, Risk and Cost-Benefit11.8 Impact11.9 Sustainability11.10 ConclusionsAppendix: Modelling Standards and Model RepositoriesA.1 IntroductionA.2 Infrastructure for Computational BiomedicineA.3 Syntax, Semantics and Annotation of ModelsA.4 Markup LanguagesA.5 Model RepositoriesA.6 Conclusions
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