The main goals are the definition of linguistic specification environments for the dynamics of biological systems, the definition of expression data analysis techniques that leverage biological knowledge expressed in computational models, and the design and development of prototypes for the simulation and the analysis of both data and behaviours of biological systems.
We are active in the following research areas: analysis of biological data; modelling and simulation of biological systems; probabilistic and stochastic formal methods; investigation of bio-inspired interaction paradigms.
The goal of the data analysis is to develop and validate the innovative analysis techniques for post-genomics biology. The strategy is the application of state-of-art techniques of machine learning and data mining as well as active collaboration with biologists for the experimental design and data analysis phases. We focus mainly on the analysis of microarray expression data, miRNA target prediction and inference of causal relationship. Specific areas of interest are quality control, in particular the detection of mislabelled data from genetic expression profiling, and the integration between system biology and high-throughput data.
Our activities in modelling and simulating biological systems closely reflect the post-genomics shift from structure (genes) to function (behaviour). Up until recently, the studies of the information content and information flow in biological processes and systems were mainly focussed on contents rather than on functions. As a result t, research in bioinformatics mainly addressed algorithms and static databases. On the side of behaviours, the bulk of the effort in reconstructing cell molecular mechanisms aimed at genetic regulatory networks, i.e., graphs just tracing the mutual influences of genes. As the scope of investigation extends to system behaviours, more and deeper details must be accommodated, such as protein-protein and protein-DNA interactions. Since any gene and any protein can be viewed as a functional unit that operates concurrently with hundreds of thousands of other functional units possibly interacting with them by exchanging chemical messages, biological systems can be considered as information devices with their own computational models. Several models have been proposed over the latest few years to provide the basis for modelling the dynamics of biological systems. Examples are various kinds of Petri Nets, hybrid automata, state charts, rewriting systems, and process calculi, i.e. specification languages developed as dynamic models of communication in decentralised computational systems. We focus on the latest approach, and apply stochastic process calculi to formally shape complex biological behaviours, and to underpin the development of semantically-based tools for their simulation and analysis.
The main application area of our research in data analysis and in modelling is systems biology, the new frontier of biological research which addresses the understanding of organisms as complex interacting systems. More generally, our results on probabilistic and stochastic formal methods are relevant to quantitative reasoning (e.g., resource usage, and quality of service) in many areas.
We actively collaborate with:
- CIBIO, Centre for Integrative Biology,
- CoSBi, The Microsoft Research University of Trento Centre for Computational and Systems Biology, and
- Fondazione Edmund Mach.
|Enrico Blanzieri||Paola Quaglia|
Further InformationResearch Program's Technical reports
Research Program's Published papers