Chiara Damiani, P.h.D

Current Position:

Fixed-term researcher (RTD-B in INF/01) at Department of Biotechnology and BiosciencesUniversity of Milan Bicocca

Affiliated with ISBE-it/SYSBIO Center of Systems Biology

Associated editor of BMC Bioinformatics


PhD in Multiscale Modeling, Computational Simulations and Characterization in Material and Life Sciences from University of Modena and Reggio Emilia (year 2011)

Previous Positions:

  • Post-doc research associate at the Department of Informatics, Systems and Communication

  • 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


  • Databases

  • Computer Science


New data integration framework to characterize the landscape of metabolic regulation in various biological samples

Upcoming events

Ongoing projects

Combining multi-target regression deep neural networks and kinetic modeling to predict relative fluxes in reaction systems

Selected Publications:

  • 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:

  • M. Di Filippo, C. Damiani, D. Pescini. GPRuler: Metabolic gene-protein-reaction rules automatic reconstruction. PLoS Computational Biology, 17(11), e1009550, 2021. DOI:

  • 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:

  • 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:

  • 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

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