The Role of Model Integration in Complex Systems Modelling: An Example from Cancer Biology

by Manish Patel, Sylvia Nagl

Overview

Model integration – the process by which different modelling efforts can be brought together to simulate the target system – is a core technology in the field of Systems Biology. In the work presented here model integration was addressed directly taking cancer systems as an example. An in-depth literature review was carried out to survey the model forms and types currently being utilised. This was used to formalise the main challenges that model integration poses, namely that of paradigm (the formalism on which a model is based), focus (the real-world system the model represents) and scale.

A two-tier model integration strategy, including a knowledge-driven approach to address model semantics, was developed to tackle these challenges. In the first step a novel description of models at the level of behaviour, rather than the precise mathematical or computational basis of the model, is developed by distilling a set of abstract classes and properties. These can accurately describe model behaviour and hence describe focus in a way that can be integrated with behavioural descriptions of other models. In the second step this behaviour is decomposed into an agent-based system by translating the models into local interaction rules.

The book provides a detailed and highly integrated presentation of the method, encompassing both its novel theoretical and practical aspects, which will enable the reader to practically apply it to their model integration needs in academic research and professional settings. The text is self-supporting. It also includes an in-depth current bibliography to relevant research papers and literature. The review of the current state of the art in tumour modelling provides added value.

Product Details

ISBN-13:	9783642156021
Publisher:	Springer Berlin Heidelberg
Publication date:	09/08/2010
Series:	Understanding Complex Systems
Edition description:	2010
Pages:	168
Product dimensions:	6.30(w) x 9.30(h) x 0.70(d)

Table of Contents

1 Introduction 1

1.1 Complex Biological Systems, Modelling and Model Integration 1

2 Nature to Numbers: Complex Systems Modelling of Cancer 5

2.1 Mathematical Modelling in Tumour Systems Biology 5

2.1.1 Growth Models 7

2.1.2 Angiogenesis Models 16

2.1.3 Treatment Response Models 21

2.1.4 Modelling Methodologies 23

2.1.5 Dynamic Pathway Models 27

2.1.6 Other Models 31

2.2 Summary 32

2.3 Conclusion 32

3 Coping with Complexity: Modelling of Complex Systems 33

3.1 Preamble 33

3.2 Dynamic Complex Systems 35

3.2.1 Properties of Complex Systems 37

3.3 A Systems-Theoretic Approach in Biology System Biology 43

3.4 Coping with Complexity: Modelling of Complex Systems 46

3.4.1 Individual-Based Modelling Methods 48

3.4.2 Simulation Software 51

3.5 Summary 54

3.6 Conclusion 55

4 Complexity and Model Integration: Formalisations 57

4.1 Preamble 57

4.2 The Nature of Models 58

4.2.1 Abstract Views of Models 59

4.3 Model Integration: Previous Work 71

4.3.1 Model Integration in Management and Environmental Sciences 71

4.3.2 Model Integration in Systems Biology 74

4.4 Summary 76

4.5 Conclusion 76

5 Novel Strategies for Integrating Models into Systems-Level Simulations 77

5.1 Motivation 77

5.2 Novel Model Integration Formalisations 79

5.2.1 Linear Integration Strategy 79

5.2.2 Agent-Based Integration 81

5.2.3 A Knowledge-Driven Approach (KDA) for Addressing Model Focus and Scope 86

5.2.4 Knowledge-Driven ABI: A Novel Formal Protocol for Model Integration 90

5.2.5 Expected Results and Validation 94

5.3 Summary 94

5.4 Conclusion 95

6 Experiments in Model Integration 97

6.1 Model Integration Experiments 97

6.2 Knowledge Driven Approach: BIT-Building 98

6.2.1 Chen (2004) 99

6.2.2 De Pillis (2005) 99

6.2.3 Mallet (2006) 100

6.2.4 Markus (1999) 101

6.2.5 Zhang (2007) 102

6.3 ABI: Model Decompositions 104

6.3.1 Decomposition of the Diffusion Model 104

6.3.2 Decomposition of the Gompertz Model 107

6.4 KDA+ABT: Formal Model Integrations 111

6.4.1 Gompertz Model Integration with ODEs and Discrete Models 112

6.5 Summary 125

6.6 Conclusion 125

7 Discussion 127

7.1 KDA/ABI Performance 127

7.1.1 The Knowledge-Driven Approach 127

7.1.2 Agent-Based Integration 135

7.2 Conclusion 152

References 153

Index 165