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Computational Biomedicine

Edited by Peter Coveney, Vanessa Díaz-Zuccarini, Peter Hunter, and Marco Viceconti

June 2014

ISBN: 9780199658183

296 pages
Paperback
265x195mm

In Stock

Price: £52.99

Computational Biomedicine unifies the different strands of a broad-ranging subject to demonstrate the power of a tool that has the potential to revolutionise our understanding of the human body, and the therapeutic strategies available to maintain and protect it.

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Description

Computational Biomedicine unifies the different strands of a broad-ranging subject to demonstrate the power of a tool that has the potential to revolutionise our understanding of the human body, and the therapeutic strategies available to maintain and protect it.

  • The first text to make this burgeoning area of multidisciplinary research accessible to the student reader
  • Brings together the fields of mathematics, physics, computer science, biology, engineering and medicine to provide a single, coherent view of the subject
  • Carefully edited by leaders in the field to present latest knowledge in a way that those new to the field can readily understand
  • Emphasis throughout on the power of computational biomedicine as a tool demonstrates how theory can translate into clinical benefit
  • An Online Resource Centre provides additional teaching and learning resources for both academic and students

About the Author(s)

Edited by Peter Coveney, Centre for Computational Science, University College London, Vanessa Díaz-Zuccarini, Department of Mechanical Engineering, University College London, Peter Hunter, Auckland Bioengineering Institute, and Marco Viceconti, Department of Mechanical Engineering , The University of Sheffield

Professor Peter Coveney, Centre for Computational Science, University College London

Dr Vanessa Díaz-Zuccarini, Department of Mechanical Engineering, University College London

Professor Peter Hunter, Auckland Bioengineering Institute, New Zealand

Professor Marco Viceconti, Department of Mechanical Engineering , The University of Sheffield

Table of Contents

    1:Introduction
    2:Molecular Foundations of Computational Bioscience
    2.1 Introduction
    2.2 Types of Omics Data
    2.3 Databases and Data Sources
    2.4 Management of Omics Data Types
    2.5 Software Systems and Interoperability
    2.6 Clinical Phenotypes, Security and Data Sharing
    2.7 Conclusions
    3: Understanding the Genotype-Phenotype Relationship
    3.1 Introduction
    3.2 Quantitative Genetics Theory
    3.3 Systems Genetics
    3.4 Implementing CGP Models
    3.5 CGP Applications
    3.6 Linking CGP Models to Data
    3.7 Conclusions
    4:Image Based Modelling
    4.1 Introduction
    4.2 Biomedical Imaging Techniques
    4.3 Image Based Modelling
    4.4 Medical Image Simulation
    4.5 Statistical Atlases, Populational Imaging and Modelling
    4.6 Open Source Image Modelling Tools
    4.7 Conclusions
    5:Modelling Cell Function
    5.1 Introduction
    5.2 General Cell Functions
    5.3 Cell Fundamentals
    5.4 Levels of Abstraction
    5.5 Cell Simulation
    5.6 Approaches to Modelling and Simulation
    5.7 Simulation Tools
    5.8 Example: An Agent Model in Skeletal Mechanobiology
    5.9 Reproducible Modelling: Ordinary Differential Equations
    5.10 Conclusions
    6:Modelling Tissues and Organs
    6.1 Introduction
    6.2 Modelling Epithelia
    6.3 Cardiac Modelling
    6.4 Modelling the Gastro-Intestinal Tract
    6.5 Modelling Kidney Function and Homeostasis
    6.6 General Homeostasis and Blood Pressure Regulation
    6.7 Conclusions
    7:Multi-Scale Modelling
    7.1 Introduction
    7.2 Why Multi-Scale Modelling?
    7.3 A Framework for Multi-Scale Modelling and Computing
    7.4 Scale Bridging
    7.5 Multi-Scale Computing
    7.6 Example of a Multiscale Model: In-Stent Restenosis in Coronary Arteries
    7.7 Conclusions
    8:Workflows: Principles, Tools and Clinical Applications
    8.1 Introduction: What is a Workflow?
    8.2 Computational Workflows
    8.3 Workflow Implementations
    8.4 Provenance
    8.5 Examples of Scientific Workflows
    8.6 Key Considerations
    8.7 Conclusions
    9:Distributed Biomedical Computing
    9.1 Introduction
    9.2 Parallel Applications
    9.3 The Computational Ecosystem
    9.4 Computing Beyond the Desktop
    9.5 Simulations in a High Performance Computing Environment
    9.6 Case Study 1: Calculating Drug Binding Affinities
    9.7 Computational Infrastructures
    9.8 Distributed Applications
    9.9 Orchestrated Workflows from Distributed Applications
    9.10 Case Study 2: Computational Investigations of Cranial Haemodynamics
    9.11 Conclusions
    10:Managing Security and Privacy of Patient Data Sharing Platforms
    10.1 Introduction
    10.2 Legal Background
    10.3 Brief Overview of Information Security Concepts
    10.4 Common Data Sharing Requirements
    10.5 The Data Sharing Lifecycle
    10.6 Data Warehousing Architecture
    10.7 Conclusions
    11:Toward Clinical Deployment: Verification and Validation of Models
    11.1 Introduction: Technology Assessment versus Health Assessment
    11.2 Code and Model Verification
    11.3 Sensitivity Analysis
    11.4 Model Validation
    11.5 Validation of Integrative Models
    11.6 Clinical Accuracy
    11.7 Efficacy, Risk and Cost-Benefit
    11.8 Impact
    11.9 Sustainability
    11.10 Conclusions
    Appendix: Modelling Standards and Model Repositories
    A.1 Introduction
    A.2 Infrastructure for Computational Biomedicine
    A.3 Syntax, Semantics and Annotation of Models
    A.4 Markup Languages
    A.5 Model Repositories
    A.6 Conclusions

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