Computational Biomedicine

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