Systems biology is an approach to understanding biological systems at a global scale that is designed to complement traditional reductionist approaches. A major feature of the systems biology approach is the use of computational modelling to generate hypothesis that are then tested in an experimental context. In the systems biology life-cycle both modelling and experimentation are refined in an iterative fashion in order to improve the understanding of the biological domain in question. Data integration is an essential component of the systems biology life cycle since data from experiments must be combined with data from a variety of different sources in order to produce biological systems models.
Our group researches approaches to all phases of this life-cycle with particular strengths in standards for data storage, data integration and modelling.
We research approaches to the integration of data through the generation of integrated networks of various types from semantically rich networks through to probabilistic functional interaction networks and data warehouses. Our group is developing novel approaches that allow biological heterogeneous and complex data from a wide variety of sources to be combined. These data can then be mined to generate hypotheses directly or can be used to provide a framework for the development of dynamic models.
Annotations are used to add meta-data to models in order to incorporate computationally tractable information about their constituents. We are working with the bioinformatics community to help develop standard approaches to the annotation of models. For example, we have helped to develop standards for the annotation of SBML models that allow extra biological information to be attached to aid in their interpretation and also for synthetic biology modeling to aid in the conversion of models to DNA sequences and in their composition. Our approach involves a variety of methods including embedding ontology terms, or links to ontology terms as resource description framework (RDF) data.
Dynamic modelling of biological systems at a molecular level is also strength; we research approaches to model composition and annotation and also model simulation and verification. We have strengths in modeling using CellML, SBML, Petri Nets, P systems and rule based modeling. Considerable research is being put into the development of novel simulation engines to carry out both stochastic and deterministic simulations in an efficient and scalable fashion. We are also researching approaches that allow models to be formally checked for consistency with the data that was used to generate them in a process of system verification that draws on traditional research from computing systems verification.
The biological focus of our systems biology research includes model bacteria, such as Escherichia coli and Bacillus subtilis, ageing and model plants.