Group Research Interests
Our group is primarily interested in deciphering the complex biological processes underlying Cancer and Chronic Inflammation. Although apparently different, these two pathological processes share many characteristics and in many scenarios coexist in a pathological situation. We predominantly use a systems biology approach which starts from the identification of important molecular components and aims to infer the underlying structure of the molecular networks connecting these components. For this reason we are highly interdisciplinary group which develop and apply a wide range of computational and experimental approaches. The group include people with a theoretical physics, computer science, Bioinformatics and Biology backgrounds. We also have interest in a number of related research areas which we choose to maximize the benefits of the methods we develop. These are primarily Environmental Biology where we have pioneered the application of advanced statistical modelling and network inference techniques for the development of mechanistic biomarkers of environmental pollution. We are also actively involved in Bacterial Pathogenesis where we have developed models representative of stress response associated to Host colonization.
Methodology development in Systems Biology
Our group has a strong interest in the development of novel computational methods to address open questions in systems biology. These primarily involve the development of statistical modelling techniques for the identification of important components linked to phenotypic outcome (ref), the development of network inference methods for the identification and simulation of underlying molecular networks from observational data and the identification of functional modules within complex biological networks. In Statistical Modelling we are developing advanced search procedure for the identification of molecular components potentially involved in determining phenotypic changes. We have successfully applied these methods at a number of biological problems integrating gene expression data, proteomics and metabolomics with physiology measurements. We have previously contributed at the development of network inference methods based on State Space Models and applied these to a number of biological problems. More recently, we have developed an ODE based modelling platform that incorporates existing knowledge in the parameter fitting. Complex networks tend to be organized in relatively independent functional units called modules. Our group is interested in the development of computational methods that allow the identification of such modules by integrating multi-level functional data within the topology of a large molecular network.