Diego Di Bernardo
Genomic Medicine Program Coordinator, TIGEM
Head of Bioinformatics Core, TIGEM
Associate Professor of Control Engineering, Department of Chemical Engineering, University of Naples "Federico II", Italy
One of the main challenges in the field of post-genomic research is developing methods to extract information from the vast amounts of data generated by High Throughput technologies. The aim is to elucidate biological mechanisms at the ‘network’ level, i.e. how genes, proteins non-coding RNAs and metabolites interact with each other to perform a specific function. Modeling and analysis of gene networks is of major importance in order to understand the working mechanisms of the cell in patho-physiological conditions and to predict their responses to drug treatments. Our research aims at developing and applying experimental protocols and computational algorithms to elucidate mechanisms of genetic diseases and develop therapeutic treatments via small molecules. Indeed, Systems Biology approaches can be used to develop novel pharmacological treatments for rare genetic diseases by identifying the most suitable compounds with the highest therapeutic efficacy.
Synthetic biology aims at building novel biological ‘circuits’,synthetic networks, which can alter cell behavior by performing specific desirable tasks.. Additionally it can be used to build simplified models of complex biological pathways in order to better understand their working mechanisms. We developed computer-controlled microfluidics devices to regulate and observe the dynamics of gene expression across single cells in real-time. A microfluidics device (or chip) consists of a standard microscopy glass-slide to which a polymer (PDMS) is irreversibly bound. The polymer is transparent and elastic, and using lithography techniques makes it possible to print micrometric channels and chambers in the chip, allowing cells to grow and proliferate using only microliters of reagent. This allows cells to be exposed to desired time-varying concentrations of a compound and for their behavior to be followed in real-time. We applied this innovative technology to study gene regulation in single cells of both yeast and mammals.
The dramatic evolution of new technologies and their impact on research in the fields of genetics and molecular biology have made the Bioinformatics Core (BC) an indispensable resource for TIGEM. The mission of this Core is to enhance the quality of research through the provision of bioinformatics and statistical expertise in hypotheses formulation, modelling, large-scale data analysis and biological interpretation.
The Bioinformatics Core supports TIGEM’s research community in addressing a wide variety of biological questions from genomics to transcriptomics using state-of-the-art computational approaches. The activity of the BC can be either driven by requests from the TIGEM community on specific projects or experiments, or proceed in background for the development of software tools of general interest, which are aimed at performing frequently requested tasks. These tools are then made available to the TIGEM community on the BC website. Moreover, the BC regularly organizes hand-on training courses to disseminate awareness on bioinformatics and statistics within the TIGEM community. Documentation is also published and updated regularly on the BC website.
Individual research groups can nowadays easily perform genome-wide experiments, although the specific expertise needed to analyze the resulting large-scale data requires interdisciplinary expertise that is not usually available within the research group. The BC provides such expertise and keeps up-to-date with cutting edge technologies and solutions. BC members work closely with the researchers as a team and interact with them during the entire experiment and the subsequent analytical process.
Next Generation Sequencing (NGS) technologies are now available for TIGEM researchers. With its ability to simultaneously sequence hundreds of thousands of DNA or RNA fragments, NGS has dramatically changed the landscape of genetics and genomic studies and opened up a broad range of genome-wide applications. NGS technologies produce data on an unprecedented scale and thus analysis requires combined computational and molecular biology expertise, plus an adequate hardware equipment for parallel computing and terabytes of storage space to manage and analyze such data. With time, the BC has acquired skills and computational power required to analyze NGS data and now provides complete support for a number of NGS applications (RNA-, ChIP- and Exome-seq).
The Bioinformatics Core expertise can be broadly divided in the following main areas, although significant overlap exists within them:
1) Statistical data analysis.
2) Genomics and proteomics analysis including Next-Generation Sequencing applications.
3) Image processing.
Siciliano V, Garzilli I, Fracassi C, Criscuolo S, Ventre S, di Bernardo D (2013) MiRNAs confer phenotypic robustness to gene networks by suppressing biological noise. Nature Commun. 4:2364. doi: 10.1038/ncomms3364.
Gambardella G, Moretti MN, de Cegli R, Cardone L, Peron A, di Bernardo D (2013). Differential network analysis for the identification of condition-specific pathway activity and regulation. Bioinformatics. 29(14):1776-85. doi: 10.1093/bioinformatics/btt290.
Belcastro V, Siciliano V, Gregoretti F, Mithbaokar P, Dharmalingam G, Berlingieri S, Iorio F, Oliva G, Polishchuck R, Brunetti-Pierri N, di Bernardo D (2011). Transcriptional gene network inference from a massive dataset elucidates transcriptome organization and gene function. Nucleic Acids Res 39(20):8677-88. doi: 10.1093/nar/gkr593.
Siciliano V, Menolascina F, Marucci L, Fracassi C, Garzilli I, Moretti MN, di Bernardo D (2011). Construction and modelling of an inducible positive feedback loop stably integrated in a mammalian cell-line. PLoS Comput Biol. 7(6):e1002074. doi: 10.1371/journal.pcbi.1002074.
Iorio F, Bosotti R, Scacheri E, Belcastro V, Mithbaokar P, Ferriero R, Murino L, Tagliaferri R, Brunetti-Pierri N, Isacchi A, di Bernardo D (2010). Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc Natl Acad Sci USA .107:14621-14626. doi: 10.1073/pnas.1000138107.