Computational Modelling and Simulation is concerned with identifying mathematical models for computational problems originating from Science and Engineering. This is accompanied by the design and implementation of associated algorithms that enable the user to follow and understand the problem-solving process under runtime and accuracy constraints.
We focus on discrete and combinatorial modelling for problems that have been shown to be NP-hard, where the associated algorithmic solutions utilise the concept of objective function landscapes and local search-based methods, such as evolutionary algorithms and population-based strategies.
At a general scale, our current research covers diverse problems from biomolecular structure prediction and graph-based combinatorial optimisation. The aim is to devise new algorithmic methodologies that may have significance for problems belonging to the same complexity level, but being outside the particular application area.
Currently, we are working on RNA structure prediction and energy landscapes, inverse RNA folding as part of Synthetic Biology, microRNA target prediction, protein structure prediction in simplified energy models, and energy-efficient scheduling and routing. The computational implements we are using include quantum computing applied to structure prediction and various visualisation techniques.
The research group has close collaborative links with Dr Steinhofel's research group at King's College London.