Software and Systems Engineering
Complex Systems Design and Implementation
We have been involved in the design and implementation of complex software systems for industrial applications and made significant contributions to a major international project on intelligent (holonic) manufacturing systems. The underlying software architecture has also been applied to a number of information integration applications and provides a basis for many data-intensive systems. Our work in complex systems is beyond traditional elelectronic and mechanical systems. We also carry out research in the areas of synthetic and systems biology to engineer and understand complex biological systems.
Complex Systems Simulation
We work on software engineering for simulation of complex systems, building on the CoSMoS approach to principled modelling and simulation of complex systems. Two principle areas of interest are model-driven engineering for systems defined by behaviours, and capturing the fitness for purpose of simulations using argumentation. Software simulations developed using our work have been used to study aspects of immune systems and cancer, as well as robotic simulation; we are currently looking at aspects of whole systems simulation for smart energy.
Computational Synthetic and Systems Biology
We work at the boundaries of computing science and biology, and apply mathematical modelling techniques to understand, and design, complex biological systems in the areas of synthetic and systems biology. Synthetic biology is an emerging engineering discipline and borrows existing design principles such as abstraction, standardisation and modularity to facilitate the development of novel applications. The UK government declared synthetic biology as one of the "eight great technologies", and the global value of this field is expected to reach to £62bn by 2020. It is crucial that computational methodologies are developed to create large-scale applications.
We are particularly interested in genetic design automation and the application of computational approaches. One of our strengths is developing model-driven design methodologies and creating composable and molecular models of biological components to derive predictable physical systems. These models are currently available at virtualparts.org, and one of our papers demonstrating model-driven design of biological systems was selected as the best paper in the IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology in 2014. We are interested in knowledge representation for biology using Semantic Web, and heavily use ontologies and semantic reasoning in our research. Moreover, we are involved in the development of programming languages for biology and data standards to electronically exchange genetic circuit designs.
We also research in the development of knowledge representation techniques to utilise huge amount of ever growing information. Our research in this area includes the bridging of Semantic Web technologies and the representation of biological data. We apply ontological data integration and mining methodologies to infer useful information. We also contribute to the development of data standards such as the Synthetic Biology Open Language (SBOL), the Systems Biology Markup Language (SBML) and Kappa. SBOL has recently emerged to electronically capture information about genetic circuits and is being adopted worldwide. SBML and Kappa are widely adopted mathematical modelling languages and we extend these languages to store metadata in order to enable further developments such as computational composition, verification and understanding of mathematical models.
Please get in touch if you want to learn more about our research in Complex Systems.