Validation and discovery from computational biology models

Simulation software is often a fundamental component in systems biology projects and provides a key aspect of the integration of experimental and analytical techniques in the search for greater understanding and prediction of biology at the systems level. It is important that the modelling and analy...

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Bibliographic Details
Published in:BioSystems 2008-07, Vol.93 (1), p.141-150
Main Authors: Kiran, Mariam, Coakley, Simon, Walkinshaw, Neil, McMinn, Phil, Holcombe, Mike
Format: Article
Language:English
Subjects:
Agent-based modelling
Cell Cycle
Cell Differentiation
Cells, Cultured
Computational Biology - methods
Humans
Keratinocytes - cytology
Models, Biological
Parallel computation
Reproducibility of Results
Simulation
Validation and testing
X-machines
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Summary:Simulation software is often a fundamental component in systems biology projects and provides a key aspect of the integration of experimental and analytical techniques in the search for greater understanding and prediction of biology at the systems level. It is important that the modelling and analysis software is reliable and that techniques exist for automating the analysis of the vast amounts of data which such simulation environments generate. A rigorous approach to the development of complex modelling software is needed. Such a framework is presented here together with techniques for the automated analysis of such models and a process for the automatic discovery of biological phenomena from large simulation data sets. Illustrations are taken from a major systems biology research project involving the in vitro investigation, modelling and simulation of epithelial tissue.
ISSN:0303-2647
1872-8324
DOI:10.1016/j.biosystems.2008.03.010