Chiara Damiani is Assistant Professor (RTD-b) in Computer Science at the Department of Biotechnology and Biosciences of the University of Milan-Bicocca. She is associated editor of BMC Bioinformatics. She is expert in -omics data integration and modeling and simulation of biological systems.
She previously was post-doc fellow at the Department of Informatics, Systems and Communication of the University of Milano-Bicocca (2013-2019) and Junior Researcher at The Microsoft Research - University of Trento Centre for Computational and Systems Biology (2011-2012). She obtained a PhD is in Multiscale Modeling, Computational Simulations and Characterization in Material and Life Sciences from the University of Modena and Reggio Emilia. She was visiting PhD student at the Institute for Biocomplexity and Informatics (Calgary University) under the supervision of Stuart Kauffman.
My main research interest concerns the development of data science solutions to extract knowledge form -omics data, taking into account emergent properties arising from the interaction of the many components composing biological systems.
Main research activities: metabolic network reconstruction modeling and simulation, transcriptomics metabolomics and single-cell RNAseq data analysis, multi-omics data integration methods, multi-scale simulation of multicellular organisms and cancer cells populations.
- CHRONOS (active)
- “ITFOC, Information Technology: The Future of Cancer Treatment”, within the FLAG-ERA Joint Transnational Call (JTC) 2016 (active)
- SysBioNet, a MIUR initiative for the Italian Roadmap of European Strategy Forum on Research Infrastructures (ESFRI), from 2012 to 2016 (finished)
-C. Damiani*, D. Maspero, M. Di Filippo, R. Colombo, D. Pescini, A. Graudenzi, H. V. Westerhoff, L. Alberghina, M. Vanoni and 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 and 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 and 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: i311–i318, 2017. doi: 10.1093/bioinformatics/btx251
-Hans V. Westerhoff, Manchester Centre for Integrative Systems Biology and University of Amsterdam
-Odemir M. Bruno, University of Sao Paulo
-Stuart Kauffman, Institute of Systems Biology (Seattle)
-Enzo Medico e Claudio Isella, Istituto di Candiolo – IRCCS
-Roberto Serra e Marco Villani, Università di Modena e Reggio Emilia
-Alex Graudenzi e Daniela Gaglio, Institute of Molecular Bioimaging and Physiology, Consiglio Nazionale delle Ricerche (IBFM-CNR)
-Marco Nobile, Eindhoven University of Technology
-Giulio Caravagna, Università di Trieste