Bioinformatics aims to develop and improve methods for storing, retrieving, organizing and analyzing biological data. One of our key goals is the development of software tools that generate biological knowledge by combining the data from sequencing technologies and microarray profiling with information accessible through multiple online repositories. On one hand, we seek to facilitate the translation of molecular and clinical data into effective and personalized treatments for complex diseases. In addition, our knowledge-based tools identify molecule interactions and pharmacological actions underlying the observed process, thus providing answers to important questions in basic research. For more information see the Bioinformatics page

Historically, mathematics has been used extensively in the sciences to describe, explain, and ultimately predict the behaviour of complex systems. Starting from the seventies, models have been widely used also to study complex biological systems in order to understand the fundamental biological mechanisms. Independently from the specific application and the specific modelling/computational techniques, the common denominator in this field is the use of tools coming from the statistics, the mathematics and the artificial intelligence togheter with the biological/physiological knowledge. The integration of these elements allows to derive qualitative information about the phenomena under investigation or to make quantitative prediction of the main variables of a biological system.

Our activities cope with both methodological and applied issues.  Bayesian techniques (and Markov Chain Monte Carlo algorithms), population analysis and deconvolution methods are some examples of our interests and advanced expertise. At the present, the most important application field is the support of drug development and registration (in vitro, preclinical, clinical studies) by a quantitative assessment of drug efficacy and safety. A key objective in this area is to characterize the pharmacokinetic and pharmacodynamic (PK-PD) properties of new drugs, based on pre-clinical and/or clinical data. Therefore, our research activity focuses on the development of PK-PD models to quantitatively describe kinetics, mechanism of action and the effects on relevant endpoints of new compounds currently under investigation. For that we use several software tools such as Matlab, NONMEM, Monolix, WinBUGS. For more information see the Mathematical Modelling page.

Synthetic Biology is a novel discipline that aims at designing and realizing novel functionalities in living organisms as well as redesigning already existing natural biological systems. These tasks are achieved through a rational approach, borrowed from engineering, and using laboratory techniques such as the recombinant DNA methods. Up to the present, several applications concerning biosafety, bioremediation, health, pharmacology and biofuels have been developed using this approach.

Our laboratory focuses on:

• Study of biological parts and devices predictability via mathematical modelling and in-vivo evaluation;
• Development of genetic and computational tools to support the design of novel living systems;
• Re-engineering of bacterial communication mechanisms to carry out novel engineering-inspired functions;
• Characterization of CRISPR-interference mechanism and application of CIRPSR-mediated gene regulation in living controllers for microbiome applications;
• Construction of microorganisms able to convert industrial waste into high-value biocommodities such as biofuels and biopolymers.

For more information see the Synthetic Biology page.