The Transcriptional Landscape

RNA is implicated in proteins biosynthesis as well as in several biological processes, which were not even imagined few years ago. RNA, indeed, is now recognized for its role in protein synthesis inhibition and for its contribution to genomic complexity: alternative transcription, splicing and translation and RNA editing allow single genes to encode for multiple proteins and a central role for non-coding RNA is beginning to emerge in cell signaling and regulation of gene expression. For these reasons my principal research interests and efforts are devoted to the understanding of Transcriptome complexity, by coupling DNA sequences annotation and gene expression profiling, supported by robust statistics and computation.


Millions of cells are dividing every minute in our human body. Normally, this process is well coordinated and cell divisions are followed by ordered patterning and cell specialization, allowing the maintenance of tissues and organs internal equilibrium (homeostasis). Cancer can be regarded as a deviation from this balanced behavior: a single cell, that has undergone mutations in its DNA, instead of maturing and dying normally reproduces without restraint. This is usually accomplished by incessant rather than faster dividing and it gives rise to a progeny that usually fails to mature. In this perspective it is clear that cancer is a very complex disease, involving not only cancerous cells, but also surrounding and distant normal cells, which engage an infinite number of complex interactions. Thus the effort of unraveling such a complexity by using genomic approaches is among my principal research interests.

Gene Expression analysis

During my past research experience I had my hands on gene expression analysis by cDNA microarray and I was responsible for the set up of the two microarray facilities, which are currently operating at IFOM (Milan) and at LNCIB (Trieste). This gave me the chance to follow each step of the procedure, from clones inoculation in LB medium to data mining, giving me the clear feeling about the complexity of the technology and boosting in me a strong interest in statistical and computational approaches to control data variability and noise. In fact the management and analysis of the millions of data points resulting from this kind of experiments requires optimal experimental design and meaningful data analysis. Last but not least another crucial issue in gene expression studies is the need of larger samples in order to control the variability due to inter-individual differences. However, in the mean time while waiting for more data points, a concrete improvement is the possibility of performing cross-validation of independent studies across different laboratories and platform. In this context I am glad to be part of the GIANT Working Group

Systems Biology

Systems biology is the study of living organisms in terms of their underlying network structure rather than simply their individual molecular components. In this perspective a "system" can be anything from a gene regulatory network to a cell, a tissue, or an entire organism. Unlike molecular biology which focus on molecules, such as sequence of nucleotide acids and proteins, Systems Biology focus on systems that are composed of molecular components and try to understand their underlying dynamics. Systems Biology, for instance, studies how genes and proteins interact, how fluxes distribute in a metabolic pathway, or how does a particular system reacts to external perturbations, trying to identify the mechanisms underlying the different phenomena and to describe them using suitable mathematical models. In such an investigation high-throughput quantitative technologies are essential and must be supported by robust computational approaches. In the past years genome sequencing projects and other molecular biology projects accumulated in-depth knowledge that is now publicly available, so that system-level analysis can now be grounded at the molecular-level. In this boom of information a central role can be ascribed to bioinformatics (see below). Although Systems Biology is still in its infancy, it will probably become one of the main area in biological sciences in the near future.


Bioinformatics can be regarded as the field of intersection between biology and computer sciences. It uses the power of computer sciences to store, analyze, integrate and visualize biological data, in order to answer biologists questions. This field of research is concerned with organizing biomolecular databases, managing the quality of data input, getting useful information out of such databases, and integrating information from disparate sources. Genomics, systems biology and high-throughput biological research are based on such sophisticated computer-based tools and improvements in these fields would not have been impossible without the parallel development of Bioinformatics.