Associate Professor in Computer Science (INF/01) at Department of Biotechnology and Biosciences, University of Milan-Bicocca
Affiliated with ISBE-it/SYSBIO Center of Systems Biology
Affiliated with CNR-IBSBC Institute of Bioimaging and Complex Systems
Associated editor of BMC Bioinformatics
Coordinator of Sysmod
PhD in Multiscale Modeling, Computational Simulations and Characterization in Material and Life Sciences from University of Modena and Reggio Emilia (year 2011)
Research assitant at the Department of Biotechnology and Bioscences, University of Milan-Bicocca
Post-doc research associate at the Department of Informatics, Systems and Communication, University of Milan-Bicocca
Junior Researcher at The Microsoft Research - University of Trento Centre for Computational and Systems Biology
Visiting PhD research student under the supervision of Prof. Stuart Kauffman at Institute for Biocomplexity and Informatics, University of Calgary
Omics data analysis
Databases
Computer Science and Statistics
Bioinformatics
Computational Systems Biology
Machine learning
Constraint-based modeling
Sensitivty analysis
Boolean networks
Autocatalytic cycles
Cancer Metabolism
Embryo development
M. Di Filippo, D. Pescini, B.G. Galuzzi, M. Bonanomi, D. Gaglio, E. Mangano, L. Alberghina, M. Vanoni, C. Damiani*. INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation. PLoS Computational Biology 18(2): e1009337, 2022. DOI: https://doi.org/10.1371/journal.pcbi.1009337
M. Di Filippo, C. Damiani, D. Pescini. GPRuler: Metabolic gene-protein-reaction rules automatic reconstruction. PLoS Computational Biology, 17(11), e1009550, 2021. DOI: https://doi.org/10.1371/journal.pcbi.1009550
M. S. Nobile; V Coelho; D. Pescini; C Damiani*. Accelerated global sensitivity analysis of genome-wide constraint-based metabolic models. Bmc Bioinformatics 78, 2021. DOI: https://doi.org/10.1186/s12859-021-04002-0
C. Damiani, D. Gaglio, E., Sacco, L. Alberghina & M. Vanoni. Systems metabolomics: from metabolomic snapshots to design principles. Current Opinion in Biotechnology, 63, 190-199, 2020. DOI: https://doi.org/10.1016/j.copbio.2020.02.013
C. Damiani*, L. Rovida, D. Maspero, I. Sala, L. Rosato, M. Di Filippo & G. Mauri G. MaREA4Galaxy: Metabolic reaction enrichment analysis and visualization of RNA-seq data within Galaxy. Computational and Structural Biotechnology Journal, 18, 993, 2020. DOI: 10.1016/j.csbj.2020.04.008
C. Damiani*, D. Maspero, M. Di Filippo, R. Colombo, D. Pescini, A. Graudenzi, H. V. Westerhoff, L. Alberghina, M. Vanoni e G. Mauri. Integration of single-cell RNA-seq data into population models to characterize cancer metabolism, PLoS Computational Biology, 15(2), e1006733, 2019. DOI: 10.1371/journal.pcbi.1006733
A. Graudenzi, D. Maspero, M. Di Filippo, M. Gnugnoli, C. Isella, G. Mauri, E. Medico, M Antoniotti e C. Damiani*. Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power. Journal of Biomedical Informatics, 87: 37-49, 2018. DOI: 10.1016/j.jbi.2018.09.010
C. Damiani, R. Colombo, D. Gaglio, F. Mastroianni, D. Pescini, H.V. Westerhoff, G. Mauri, M. Vanoni, L. Alberghina. A metabolic core model elucidates how enhanced utilization of glucose and glutamine, with enhanced glutamine-dependent lactate production, promotes cancer cell growth: The WarburQ effect. PLoS computational biology, 13(9), e1005758, 2017. DOI: 10.1371/journal.pcbi.1005758
C. Damiani*, M. Di Filippo, D. Pescini, D. Maspero, R. Colombo and G. Mauri. popFBA: tackling intratumour heterogeneity with Flux Balance Analysis. Bioinformatics, 33(14): i311-i318, 2017. DOI: 10.1093/bioinformatics/btx251