- Multiscale modelling of molecules-cell-tissue-organ interactions: we believe that tissue modelling is the missing link between basic research and clinical practice. We aim at developing a framework of methodologies for for an efficient multi-scale analysis and modeling between the cell and tissue levels.

- Developing and testing methodologies for modeling biomedical systems (including bioinformatics and systems biology); we are interested in methodological cross-comparison (ODEs, Bayesian inference, formal methods) and multiscale approaches;  integration of multiscale modeling of biological systems and machine  techniques.

- Methods for Integrated analysis of Multiple Omics datasets. We aim at developing robust and efficient statistical methods for the integrated analysis of metabolomics, glycomics, proteomic and genomic datasets in large studies. For the proof of principle we have chosen to focus on metabolic health. A particular interest is on handling NGS data.

- Modelling the Nuclear Architecture: Advances in molecular methods offer the ability to model the 3D organization of the nucleus. We aim to develop new methods and models to explain the phenotype (our observable traits) with nuclear properties and to decipher the relationships between the nuclear architecture and main biological processes such as gene regulation, transcription and replication. Models that will capture the dynamical, spatial and multi-omic aspects of the nuclear environment are bound to have a major impact with applications to Synthetic Biology and personalized medicine.

- Clinical Bioinformatics
(this involves both statistical bioinformatics and mathematical modeling);
on going collaborations focus on osteomyelitis, Hiv, flu, haematopoietic stem cells development.





A focus is on how both pathogen's molecular characteristics and social networks structure influence epidemics spreading and viceversa and on the conditions for epidemics spreading.








 
Bioinspired technology

The growing relevance of bioinspired and synthetic biology will depend on our understanding and skill in modeling biological systems and viceversa; social networks are influenced by Information Communications Technology and viceversa.