Silvio Bicciato
Silvio Bicciato
website: tplr7361
affiliation: Università di Modena-Reggio Emilia
research area(s): Computational Biology, Genetics And Genomics
Course: Molecular and Regenerative Medicine
University/Istitution: Università di Modena-Reggio Emilia
− Place of birth: Abano Terme (Padova), Italy
− Date of birth: 08-02-1967
− Office address: Dept. of Biomedical Sciences, University of Modena, via Campi 287, 41100 Modena
− Phone: +39-059-205-5219 (office); +39-059-205-5454 (lab); fax: +39-059-205-5410
− E-mail:
− Citizenship: Italian
− Current position: Associate professor, Faculty of Biosciences and Biotechnologies, University of Modena
Silvio Bicciato receives the BS degree with honors in Chemical Engineering in 1992 and the Ph.D. degree in
Chemical Engineering in 1997. From 1996 to 1998, funded by a NATO Advanced fellowship and by the MIT
Consortium for Fermentation Diagnosis and Control, he joins Prof. G.N. Stephanopoulos’ research group at
MIT (Cambridge, USA) as a post-doctoral associate. Since 1997 he has been collaborating with the
Bioinformatic and Metabolic Engineering Laboratory at MIT, designing and applying database-mining
algorithms based on statistical analysis, pattern recognition, and artificial neural networks, and bioinformatic
tools for the analysis of microarray gene expression data. From 1998 to 2000 he works as a post-doctoral
associate at the Department of Chemical Process Engineering – University of Padova – and participates at
European grant BMH4989580 (Automated system for biopolymer synthesis), developing statistical process
control procedures for the automatic control of biopolymer synthesis. From 2004 to 2007 he is assistant
professor of Industrial Bioengineering at the University of Padova. In November 2007 he joins the Dept. of
Biomedical Sciences of the University of Modena as assistant professor of Industrial Bioengineering. Since
November 2010 he is associate professor of Industrial Bioengineering at the same University.
His principal research interest is the design and application of computational and bioinformatics tools
for the analysis of data coming from high-throughput technologies, and particularly microarray
genotyping and gene expression profiling. His research activity is currently carried out in
collaboration with the several national and international academic laboratories. In particular, he
developed computational methods for the bioinformatics analysis of high throughput data (Bicciato et
al. 2003; Callegaro et al., 2006; Ferrari et al., 2007; Coppe et al., 2009; Bicciato et al., 2009;
Bisognin et al., 2009; Biasiolo et al., 2010; Sales et al., 2010). The methods found applications in the
molecular analysis of angiogenesis (Cline et al., 2002), head-and-neck tumors (Hasina et al., 2003),
leukemia (De Zen et al., 2003; Te Kronnie et al., 2004; Te Kronnie et al., 2006; Zangrando et al.,
2006; Tagliafico et al., 2006; Bungaro et al., 2009; Molteni et al., 2010; Mosca et al., 2010), multiple
myeloma (Mattioli et al., 2005; Vacca et al., 2005; Agnelli et al., 2005; Agnelli et al., 2007, Fabris et
al., 2007; Lombardi et al., 2007; Lionetti et al., 2009), melanoma (Mandruzzato et al., 2006; Magnoni
et al., 2007), breast cancer (Adorno et al., 2009; Martello et al., 2010). He has been collaborating
since 2003 with the other proponents through several grants (FIRB RBAU01935A and
PRIN2005069853) and research activities (Gallina et al., 2006; Orabona et al., 2006; Marigo et al.,
2010; Fallarino et al., 2010).
In the field of bioinformatics and functional genomics, he has been the scientific coordinator of the
University of Padova Operating Unit in the following projects: FIRB RBNE01TZZ8 (Biomolecules,
fluids, systems and data handling in Bio- chip technology), FIRB RBAU01935A (Genome-wide
analysis of accessory cells that control the immune response), PRIN2005069853 (Immune response
against prostate cancer: molecular basis for novel therapeutic strategies), ONCOSUISSE OCS
01517–02–2004 (Cancer genes involved in genetic progression of germinal centre B cell
lymphomas), ONCOSUISSE OCS 1939-8-2006 (Genome wide DNA profiling as outcome predictor
in diffuse large B cell lymphoma), PRIN2007Y84HTJ (A systems biology approach to reconstruct the
networks of molecular interaction and the monocyte/macrophage activation process in response to
inflammation under physiological conditions using omics technologies). He has been the principal
investigator of the research project “A computational approach to the study of skeletal muscle
genomic expression in health and disease” financed in 2007 by Fondazione Cassa di Risparmio di
Padova e Rovigo and of the project "Development of a bioinformatics framework for the analysis of
complex biological systems: application to the study of myeloid differentiation" funded in 2008 by
Fondazione Cassa di Risparmio di Modena. He is the scientific coordinator of the University of
Modena Operating Unit in the project “Molecular basis for triple negative breast cancer metastasis:
new tools for diagnosis and therapy” funded by 2010 AIRC grant “Special Program Molecular
Clinical Oncology 5 per mille”. He is member of EuGESMA, a COST Action (BM0801) established to
link European clinical and science groups with the aim of translating genomics technologies into a
clinical environment for the benefit of Myelodysplastic Syndrome and Acute Myeloid Leukemia.

1: Dupont S, Morsut L, Aragona M, Enzo E, Giulitti S, Cordenonsi M, Zanconato F,
Le Digabel J, Forcato M, Bicciato S, Elvassore N, Piccolo S. Role of YAP/TAZ in
mechanotransduction. Nature. 2011 Jun 8;474(7350):179-83

2: Mosca L, Fabris S, Lionetti M, Todoerti K, Agnelli L, Morabito F, Cutrona G,
Andronache A, Matis S, Ferrari F, Gentile M, Spriano M, Callea V, Festini G,
Molica S, Deliliers GL, Bicciato S, Ferrarini M, Neri A. Integrative genomics
analyses reveal molecularly distinct subgroups of B-cell chronic lymphocytic
leukemia patients with 13q14 deletion. Clin Cancer Res. 2010 Dec

3: Fallarino F, Volpi C, Fazio F, Notartomaso S, Vacca C, Busceti C, Bicciato S,
Battaglia G, Bruno V, Puccetti P, Fioretti MC, Nicoletti F, Grohmann U, Di Marco
R. Metabotropic glutamate receptor-4 modulates adaptive immunity and restrains
neuroinflammation. Nat Med. 2010 Aug;16(8):897-902

4: Martello G, Rosato A, Ferrari F, Manfrin A, Cordenonsi M, Dupont S, Enzo E,
Guzzardo V, Rondina M, Spruce T, Parenti AR, Daidone MG, Bicciato S, Piccolo S. A
MicroRNA targeting dicer for metastasis control. Cell. 2010 Jun

5: Marigo I, Bosio E, Solito S, Mesa C, Fernandez A, Dolcetti L, Ugel S, Sonda N,
Bicciato S, Falisi E, Calabrese F, Basso G, Zanovello P, Cozzi E, Mandruzzato S,
Bronte V. Tumor-induced tolerance and immune suppression depend on the C/EBPbeta
transcription factor. Immunity. 2010 Jun 25;32(6):790-802

6: Sales G, Coppe A, Bicciato S, Bortoluzzi S, Romualdi C. Impact of probe
annotation on the integration of miRNA-mRNA expression profiles for miRNA target
detection. Nucleic Acids Res. 2010 Apr;38(7):e97

7: Biasiolo M, Forcato M, Possamai L, Ferrari F, Agnelli L, Lionetti M, Todoerti
K, Neri A, Marchiori M, Bortoluzzi S, Bicciato S. Critical analysis of
transcriptional and post-transcriptional regulatory networks in multiple myeloma.
Pac Symp Biocomput. 2010:397-408

8: Lionetti M, Biasiolo M, Agnelli L, Todoerti K, Mosca L, Fabris S, Sales G,
Deliliers GL, Bicciato S, Lombardi L, Bortoluzzi S, Neri A. Identification of
microRNA expression patterns and definition of a microRNA/mRNA regulatory network
in distinct molecular groups of multiple myeloma. Blood. 2009 Dec

9: Bisognin A, Coppe A, Ferrari F, Risso D, Romualdi C, Bicciato S, Bortoluzzi S.
A-MADMAN: annotation-based microarray data meta-analysis tool. BMC
Bioinformatics. 2009 Jun 29;10:201

10: Adorno M, Cordenonsi M, Montagner M, Dupont S, Wong C, Hann B, Solari A,
Bobisse S, Rondina MB, Guzzardo V, Parenti AR, Rosato A, Bicciato S, Balmain A,
Piccolo S. A Mutant-p53/Smad complex opposes p63 to empower TGFbeta-induced
metastasis. Cell. 2009 Apr 3;137(1):87-98
Project Title:
Bioinformatics and systems biology approaches to the analysis of molecular networks
Bioinformatics has for a long time been a discipline based on the comparison of gene and protein sequences, with the aim of discovering evolutionary relationships (and hence comparable function). There is now a growing interest in a more systems-based approach, where the focus is on how molecules and pathways interrelate and on the identification of the 'minimal building blocks', i.e. molecules that exchange a common substrate or participate in the formation of large supramolecular complexes. Systems biology elevates the study of biological systems from the single entity level (genes, proteins) to higher hierarchies, such as entire genomic regions, groups of co-expressed genes, functional modules, and networks of interactions. In this context, the availability of high-throughput gene expression data, coupled with bioinformatics tools for their analysis, represents a scientific breakthrough. A plethora of computational methods have been developed in the last several years to analyze microarray data, proving the effectiveness of expression signatures, in many fields. However, the massive accumulation of high-quality structural and functional annotations of genomes imposes the development of computational frameworks able, not only to analyze gene expression profiles per se, but to merge any genomic information. The integration of different types of genomic data (gene sequences, transcriptional levels, functional characteristics) is a fundamental step in the identification of networks of molecular interaction which will allow turning genomic researches into accurate biological hypotheses. Standard methodologies for the analysis of gene expression profiles, which aim at identifying molecular mechanisms from the statistical analysis of the sole microarray signal, are clearly limited. Bioinformatics and computational biology need to overcome this limitation and develop approaches for the integration of high-throughput transcriptional data with gene structural and functional characteristics and for the identification of regulatory networks. These computational methods should help deciphering how the structural elements of genomes impact the molecular mechanisms of functional utilization. Network analysis has emerged as a powerful approach to understand complex phenomena and organization in social, technological and biological systems. In particular, it is increasingly recognized the role played by the topology of cellular networks, the intricate web of interactions among genes, proteins and other molecules regulating cell activity, in unveiling the function and the evolution of living organisms. In this view, the regulation of gene expression at genome level may be studied by dissecting different relationships among genes, deducible by integrated analysis of different data like co-expression in different biological contests, genomic co-localization, co-regulation of transcription (by means of promoter analyses) and post-transcriptional co-regulation (principally by miRNA-mediated regulation). The basic idea underlining this approach is that co-expression is essential to sustain the normal function of cells and tissues. On the other hand, genes can be co-expressed because they are co-regulated. Genes may be co-regulated because they share similar promoters (e.g. enhancers acting locally on a limited chromosomal region), because they are co-localized (i.e. under the effect of local epigenetic control, due to specific chromatin modifications) or because their mRNA are target of overlapping miRNA regulators. Moreover, part of co-expressed genes may be functionally related and thus to be under the control of similar gene circuits. Thus, the integrated study of co-expression, co-regulation, co-localization and functional similarity should help understanding basic and general rules governing genomic expression and identifying interaction mechanisms, circuit characteristics and specific switches of expression in the considered biological model.
The goal of our research is establishing the methodological bases and developing the computational infrastructures to investigate, through a systems biology approach, the networks of molecular interactions characterizing complex biological systems. The methodological aim is to develop algorithms to study complex biological processes, considering structural organization of chromosomal regions, classes of genes homogeneous for expression characteristics, functional modules, and regulatory networks. The applicative aim regards the study of genetic and epigenetic mechanisms of regulation of genome expression in model systems, e.g. myeloid differentiation and skeletal muscle.