<p><span>Model integration β the process by which different modelling efforts can be brought together to simulate the target system β 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-d
The Role of Model Integration in Complex Systems Modelling: An Example from Cancer Biology
β Scribed by Manish Patel, Sylvia Nagl (auth.)
- Publisher
- Springer-Verlag Berlin Heidelberg
- Year
- 2010
- Tongue
- English
- Leaves
- 173
- Series
- Understanding Complex Systems
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
Model integration β the process by which different modelling efforts can be brought together to simulate the target system β 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.
β¦ Table of Contents
Front Matter....Pages -
Introduction....Pages 1-3
Nature to Numbers: Complex Systems Modelling of Cancer....Pages 5-32
Coping with Complexity: Modelling of Complex Systems....Pages 33-55
Complexity and Model Integration: Formalisations....Pages 57-76
Novel Strategies for Integrating Models into Systems-Level Simulations....Pages 77-95
Experiments in Model Integration....Pages 97-125
Discussion....Pages 127-152
Back Matter....Pages -
β¦ Subjects
Complexity;Statistical Physics, Dynamical Systems and Complexity;Systems Biology;Cancer Research
π SIMILAR VOLUMES
Data-driven dynamical systems is a burgeoning fieldβit connects how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical systems theory. This is a critically important new direction because the governing equations of many problems
Mathematical modeling, analysis and simulation are set to play crucial roles in explaining tumor behavior, and the uncontrolled growth of cancer cells over multiple time and spatial scales. This book, the first to integrate state-of-the-art numerical techniques with experimental data, provides an in
The correct functioning of the mammalian brain depends on the integrated activity of myriad neuronal and non-neuronal cells. Discrete areas serve discrete functions, and dispersed or distributed communities of cells serve others. Throughout, these networks of activity are under the control of neurom
<P>Systems techniques are integral to current research in molecular cell biology, and system-level investigations are often accompanied by mathematical models. These models serve as working hypotheses: they help us to understand and predict the behavio