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

Background:

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

Teaching:

  • Databases

  • Computer Science


HIGHLIGHTS

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: 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

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