Diego Di Bernardo
Diego Di Bernardo
affiliation: Università di Napoli Federico II
research area(s): Computational Biology
Course: Computational Biology and Bioinformatics
University/Istitution: Università di Napoli Federico II
Diego di Bernardo was born in Naples on 26 June 1972. He was awarded a "Laurea cum laude" Degree in Electronic Engineering from the University of Naples "Federico II" (5 yrs. University study, equivalent to an M.Sc.) in January 1997. In his graduation thesis he developed a mathematical model of the electrical activity of the heart (Prof. F. Garofalo in University of Naples; Prof. S. Cerutti and Porf. M.G. Signorini at the "Politecnico" University of Milano). In June 2001, thanks to a 3-year European Commission "Marie Curie" Fellowship, he was awarded a Ph.D. degree from the School of Medicine of the University of Newcastle, UK, with the supervision of Prof. Alan Murray. His Ph.D. thesis describes an experimental and computational approach to cardiac repolarisation and T wave morphology. Until May 2002 he was a PostDoc at the Wellcome Trust Sanger Center in Cambridge (UK) in the group of Dr Tim Hubbard. From June 2002 to December 2002 he was a PostDoc in the lab of Prof. James Collins at the Department of BioEngineering in Boston University. Since January 2003 he is a Principal Investigatpr at the Telethon Institute of Genetics and Medicine in Naples (Italy). In January 2004 he also became a lecturer for the "European School of Molecular Medicine" (Italy), a graduate school funded by the Ministry of Health in Italy. Since December 2007 he is a Research Assistant Professor ("Ricercatore") at the University of Naples "Federico II" in the Dept. of Computer Science and Systems. His research interests are in systems and synthetic biology with particular emphasis in reverse-engineering of gene networks to elucidate disease gene function and the mode-of-action of novel pharmacological compounds, and building novel synthetic circuits to modulate gene expression. He is author of more than 30 publications in peer reviewed journals. He co-organised several international workshops and conferences in the area of computational and systems biology. He has been Academic Editor for PLoS One. He is currently in the Editorial Board of IEEE Trans. Comp. Biol. and Bioinformatics.
In Systems Biology, our reserch aims at developing and applying experimental protocols and computational algorithms to infer gene regulatory networks and the mode of action of small molecules.
The algorithms we have developed use measurements of mRNA concentration of the genes that are part of the network. From these measurements it is then possible to reconstruct the causal relationships among the different genes, and therefore the regulatory network.
We have applied our reverse-engineering approach to elucidate the transcriptional network regulated by the transcription factor p63, whose mutations are causative of human malformaton syndromes, in primary keratinocytes.
We extended our reverse-engineering approach to infer a "consensus" regulatory network in human and mouse species for all the known genes by using the available microarray data in public databases. The inferred model was used both to identify new protein-protein interactions, to predict protein localisation and to elucidate the function of the disease gene GRN (granulin) as a mediator of lysosomal activity.
We are also working on the problem of the identification of Drug Mode of Action via an integrated experimental and computational approach. We were able to show that starting from gene expression data, it is possible to reconstruct a "drug network" which recapitulates known similarity in MOA between drugs. Using the drug network we identified the MOA of novel chemotherapeutic compounds and repositioned Fasudil as an autophagy inducer in cells.Synthetic biology aims at building novel biological "circuits" (synthetic network) in the cell able to perform specific tasks, or to change the behaviour of the biological process in a desired way. Our research aims at developing a mammalian oscillator to transcribe a gene of interest at specific time intervals from a population of engineered cells carrying the oscillator circuit in their genome.
We aim at achieving this objective by using a transcriptional activator, ttA-VP16, regulating its own transcription and the transcription of a microRNA directed against the transcriptional activator. This circuit will thus contain a positive self-feedback loop (ttA-VP16 on itself) and negative feedback loop (microRNA against ttA-VP16). We have also used Synthetic Biology to characterise the dynamic behaviour of a typical regulatory motif in mammalian cells: the Positive FeedBack Loop (PFL). We demonstrated experimentally that it acts as a "persistance detector" in that the inducer molecule has to be present for a minimum time interval before the PFL will respond to the molecule presence. In the absence of the PFL, on the contrary, the response starts as soon as the inducer molecule is present.
" Vincenzo Belcastro, Velia Siciliano, Francesco Gregoretti, Francesco Iorio, Pratibha Mithbaokar, Gennaro Oliva, R Polishchuk, Nicola Brunetti, Diego di Bernardo. Reverse-engineering a consensus gene regulatory network from large-scale gene expression profiles in human and mouse. Nucleic Acid Research, in press.

" Velia Siciliano, Filippo Menolascina*, Lucia Marucci*, Chiara Fracassi, Immacolata Garzilli, Maria Nicoletta Moretti, Diego di Bernardo. Construction and modelling of an inducible positive feedback loop stably integrated in a mammalian cell-line. PLoS Computational Biology , in press. *These authors contributed equally.

" Menolascina F, di Bernardo M, di Bernardo D. Analysis, design and implementation of a novel scheme for in-vivo control of synthetic gene regulatory network. Automatica 47 1265-1270, 2011. Abstract

" Belcastro V*, Gregoretti F*, Siciliano V, Santoro M, D"Angelo G, Oliva G+ and di Bernardo D+ Reverse-engineering and analysis of genome-wide gene regulatory networks from gene expression profiles using high-performance computing. IEEE Trans. in Computational Biology and Bioinformatics. 2011, in press. *Equal contributors; +corresponding authors abstract

" Cuccato G, Polynikis A, Siciliano V, Graziano M, di Bernardo M, di Bernardo D. Modeling RNA interference in mammalian cells. BMC Syst Biol. 2011 Jan 27;5(1):19. PDFAbstract

" Polynikis A, Cuccato G, Criscuolo S, Hogan SJ, di Bernardo M, di Bernardo D. Design and construction of a versatile synthetic network for bistable gene expression in Mammalian systems. J Comput Biol. 2011 Feb;18(2):195-203. abstract

" Iorio F, Bosotti R, Scacheri E, Belcastro V, Mithbaokar P, Ferriero R, Murino L, Tagliaferri R, Brunetti-Pierri N, Isacchi A, di Bernardo D. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc Natl Acad Sci U S A 2010 Aug 17;107(33):14621-6. Epub 2010 Aug 2. Abstract PDF

" Iorio F, Isacchi A, di Bernardo D, Brunetti-Pierri N. Identification of small molecules enhancing autophagic function from drug network analysis. Autophagy 2010 Nov 16;6(8):1204-5. Epub 2010 Nov 16. PMID: 20930556 [News & Views] [2]

" Cacciottolo M, Belcastro V, Laval S, Bushby K, Di Bernardo D, Nigro V. Reverse-engineering gene network identifies new dysferlin interacting proteins. J Biol Chem. 2010 Nov 30. PDF

" Marucci L, Santini S, di Bernardo M, di Bernardo D. Derivation, identification and validation of a computational model of a novel synthetic regulatory network in yeast. J Math Biol2010 Abstract PDF.

" Gregoretti F, Belcastro V, di Bernardo D, Oliva G. A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks. PLoS One, 2010 [3]
Project Title:
Identification and control of gene networks in living cells.
This project aims at identifying dynamical models of gene regulatory networks and in
controlling their behavior (i.e. expression of its genes) using an integrated experimental
and control engineering approach. We will make use of a computer-controlled microfluidics
device to achieve an automatic control strategy to identify and regulate the dynamics of
gene expression across a population of yeast and mammalian cells in real-time. The
closed-loop control is achieved via time-lapse fluorescence microscopy, which is used to
monitor reporter gene expression and present it to the control algorithm, which in turn
moves the syringes to control the amount of inducer molecules delivered to the cells.
This strategy will be applied to two study cases: (1) Identification and control of a
synthetic network in yeast, previously built in the lab [Cantone I et al, Cell, 2009]; and
(2) Identification and control of the Notch-Delta signaling pathway in mammalian cells.
This project is funded by a three year HFSP grant to Diego di Bernardo.