title: Validation and discovery from computational biology models.
abstract: 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.
authors.affiliation: University of Sheffield, Department of Computer Science, Regent Court, 211 Portobello Street, Sheffield S1 4DP, United Kingdom. m.kiran@dcs.shef.ac.uk
authors.forename: Mariam
authors.initials: M
authors.lastname: Kiran
authors.forename: Simon
authors.initials: S
authors.lastname: Coakley
authors.forename: Neil
authors.initials: N
authors.lastname: Walkinshaw
authors.forename: Phil
authors.initials: P
authors.lastname: McMinn
authors.forename: Mike
authors.initials: M
authors.lastname: Holcombe
mesh_terms: D002453:Cell Cycle; D002454:Cell Differentiation; D002478:Cells, Cultured; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D015603:Keratinocytes / Q000166:cytology; D008954:Models, Biological; D015203:Reproducibility of Results