Computational Biomedicine

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

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