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.