Modelling & Abstraction
The development and use of predictive models is a common strategy in the analysis of complex systems of interacting elements. …
Article by Peter Barr
The development and use of predictive models is a common strategy in the analysis of complex systems of interacting elements. In some cases, the primary task is to create a model from observed data which best encapsulates the current knowledge of the system, to aid our understanding of the natural sciences, the life sciences and, increasingly, the social sciences – e.g. communities of insects, gene expression networks and interacting nerve cells. Where the system under study is designed or engineered, the principal aim is to construct a model which abstracts from detail but which will nevertheless facilitate reasoning and analysis.
In the context of computer systems, the development and analysis of models complement traditional design review and testing. Artefacts built in software and hardware must often be scrutinised by appropriate models to ensure the appropriateness of their behaviour. This is increasingly seen as crucial if complex computer systems – on which so much of the modern high-tech economy depends – are to function reliably, safely and efficiently.
Computational modelling is now an established approach to understanding the nervous system, and work in Scotland ranges from foundational modelling to neuroscience and systems biology, focusing on scalable analysis, not just to deal with very complex systems but because the models themselves are so vast that they require new analysis techniques, system description languages, methods and tools.
Scottish researchers already collaborate on the application of process algebras to systems biology, and on model checking techniques. By bringing all this work together, and by stimulating neuroscience modelling and visualisation, SICSA aims for scientists in Scotland to take a leading international role in developing scalable analysis.