Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.biosystems.2008.03.010 | Biosystems |
Keywords | Field | DocType |
Agent-based modelling,Simulation,X-machines,Validation and testing,Parallel computation | Data set,Simulation software,Computer science,Software,Computational science,Artificial intelligence,Verification and validation of computer simulation models,Software engineering,Analysis software,Systems biology,Epithelial tissue,Modelling biological systems,Machine learning | Journal |
Volume | Issue | ISSN |
93 | 1 | 0303-2647 |
Citations | PageRank | References |
7 | 1.20 | 2 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mariam Kiran | 1 | 121 | 17.83 |
| Simon Coakley | 2 | 120 | 8.87 |
| Neil Walkinshaw | 3 | 345 | 27.27 |
| phil mcminn | 4 | 2384 | 97.58 |
| Mike Holcombe | 5 | 510 | 54.25 |