Chiara Damiani
Chiara Damiani
Department of Biotechnology and Biosciences
Verified email at - Homepage
Cited by
Cited by
A metabolic core model elucidates how enhanced utilization of glucose and glutamine, with enhanced glutamine-dependent lactate production, promotes cancer cell growth: The …
C Damiani, R Colombo, D Gaglio, F Mastroianni, D Pescini, ...
PLoS computational biology 13 (9), e1005758, 2017
Integration of single-cell RNA-seq data into population models to characterize cancer metabolism
C Damiani, D Maspero, M Di Filippo, R Colombo, D Pescini, A Graudenzi, ...
PLoS computational biology 15 (2), e1006733, 2019
Dynamical properties of a Boolean model of gene regulatory network with memory
A Graudenzi, R Serra, M Villani, C Damiani, A Colacci, SA Kauffman
Journal of Computational Biology 18 (10), 1291-1303, 2011
Computational strategies for a system-level understanding of metabolism
P Cazzaniga, C Damiani, D Besozzi, R Colombo, MS Nobile, D Gaglio, ...
Metabolites 4 (4), 1034-1087, 2014
Systems metabolomics: From metabolomic snapshots to design principles
C Damiani, D Gaglio, E Sacco, L Alberghina, M Vanoni
Current opinion in biotechnology 63, 190-199, 2020
Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models
M Di Filippo, R Colombo, C Damiani, D Pescini, D Gaglio, M Vanoni, ...
Computational biology and chemistry 62, 60-69, 2016
Cell–cell interaction and diversity of emergent behaviours
C Damiani, R Serra, M Villani, SA Kauffman, A Colacci
IET systems biology 5 (2), 137-144, 2011
Dynamical criticality in gene regulatory networks
M Villani, L La Rocca, SA Kauffman, R Serra
Complexity 2018 (1), 5980636, 2018
popFBA: tackling intratumour heterogeneity with Flux Balance Analysis
C Damiani, M Di Filippo, D Pescini, D Maspero, R Colombo, G Mauri
Bioinformatics 33 (14), i311-i318, 2017
The diffusion of perturbations in a model of coupled random boolean networks
R Serra, M Villani, C Damiani, A Graudenzi, A Colacci
Lecture Notes in Computer Science, 315-322, 2008
Growth and division in a dynamic protocell model
M Villani, A Filisetti, A Graudenzi, C Damiani, T Carletti, R Serra
Life 4 (4), 837-864, 2014
Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power
A Graudenzi, D Maspero, M Di Filippo, M Gnugnoli, C Isella, G Mauri, ...
Journal of biomedical informatics, 2018
INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation
M Di Filippo, D Pescini, BG Galuzzi, M Bonanomi, D Gaglio, E Mangano, ...
PLoS computational biology 18 (2), e1009337, 2022
Single-cell digital twins for cancer preclinical investigation
MD Filippo, C Damiani, M Vanoni, D Maspero, G Mauri, L Alberghina, ...
Metabolic Flux Analysis in Eukaryotic Cells: Methods and Protocols, 331-343, 2020
CABeRNET: a Cytoscape app for augmented Boolean models of gene regulatory NETworks
A Paroni, A Graudenzi, G Caravagna, C Damiani, G Mauri, M Antoniotti
BMC bioinformatics 17, 1-12, 2016
Information transfer among coupled random boolean networks
C Damiani, SA Kauffman, R Serra, M Villani, A Colacci
Cellular Automata: 9th International Conference on Cellular Automata for …, 2010
An ensemble evolutionary constraint-based approach to understand the emergence of metabolic phenotypes
C Damiani, D Pescini, R Colombo, S Molinari, L Alberghina, M Vanoni, ...
Natural Computing 13 (3), 321-331, 2014
Parameter sensitivity analysis of stochastic models: Application to catalytic reaction networks
C Damiani, A Filisetti, A Graudenzi, P Lecca
Computational biology and chemistry 42, 5-17, 2013
Interacting random boolean networks
R Serra, M Villani, C Damiani, A Graudenzi, A Colacci, SA Kauffman
Proceedings of ECCS07: European Conference on Complex Systems, 1-15, 2007
Tumor heterogeneity: preclinical models, emerging technologies, and future applications
M Proietto, M Crippa, C Damiani, V Pasquale, E Sacco, M Vanoni, ...
Frontiers in Oncology 13, 1164535, 2023
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