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

✍ Scribed by Manish Patel, Sylvia Nagl


Publisher
Springer
Year
2010
Tongue
English
Leaves
173
Category
Library

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✦ Synopsis


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.

✦ Table of Contents


Title
Contents
List of Figures
Introduction
Complex Biological Systems, Modelling and Model Integration
Nature to Numbers: Complex Systems Modelling of Cancer
Mathematical Modelling in Tumour Systems Biology
Growth Models
Angiogenesis Models
Treatment Response Models
Modelling Methodologies
Dynamic Pathway Models
Other Models
Summary
Conclusion
Coping with Complexity: Modelling of Complex Systems
Preamble
Dynamic Complex Systems
Properties of Complex Systems
A Systems-Theoretic Approach in Biology: System Biology
Coping with Complexity: Modelling of Complex Systems
Individual-Based Modelling Methods
Simulation Software
Summary
Conclusion
Complexity and Model Integration: Formalisations
Preamble
The Nature of Models
Abstract Views of Models
Model Integration: PreviousWork
Model Integration in Management and Environmental Sciences
Model Integration in Systems Biology
Summary
Conclusion
Novel Strategies for Integrating Models into Systems-Level Simulations
Motivation
Novel Model Integration Formalisations
Linear Integration Strategy
Agent-Based Integration
A Knowledge-Driven Approach (KDA) for Addressing Model Focus and Scope
Knowledge-Driven ABI: A Novel Formal Protocol for Model Integration
Expected Results and Validation
Summary
Conclusion
Experiments in Model Integration
Model Integration Experiments
Knowledge Driven Approach: BIT-Building
Chen (2004)
De Pillis (2005)
Mallet (2006)
Markus (1999)
Zhang (2007)
ABI: Model Decompositions
Decomposition of the Diffusion Model
Decomposition of the Gompertz Model
KDA+ABI: Formal Model Integrations
Gompertz Model Integration with ODEs and Discrete Models
Summary
Conclusion
Discussion
KDA/ABI Performance
The Knowledge-Driven Approach
Agent-Based Integration
Conclusion
References
Index


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