Over the last two decades, biological sciences have undergone radical transformation through the development of new research technologies that have produced a real explosion in the amount of data available. It is therefore essential to use effective informatics solutions capable of managing, analyzing and integrating these biological "Big Data".

In this view the aim of the BIOSYS research unit is the development of algorithms and methodologies to study and analyze biological data and systems, and their application in bio-medicine. The biological research interests include biological networks, genomics, proteomics, epigenomics and system biology. The activity of our group is based on the integration of specific and different skills, that constitute a plus in this multidisciplinary area.

The study and analysis at OMICS level (Genomics, Proteomics, Metabolomics, Epigenomics etc.,) of biological systems is performed through the application of existing bioinformatic tools and the design of novel ones. Primary structures of nucleic acids and proteins are studied through string analysis methodologies.  

The techniques used in this area are customized depending on the issue to address and range from discrete mathematics to differential equation systems.

The representation of entities and interactions between them through networks or graphs constitutes a valid instrument to study features and relevant topological characteristics of complex biological systems.

The biological networks research line focuses on modeling and analysis, through graphs, applied to i) Protein Contact networks ii) Protein Protein Interactions networks iii) networks involving MiRNA and lncRNA iv) Metabolic networks or subnetworks derived by specific pathologies.

In this context graph theory-based and network analysis methodologies, such as graph community detection, similarity or centrality measures, clustering algorithms, optimization on graphs, etc. are used.

Dynamic models are also studied for simulating several biological processes  through differential equation systems.