Publications

2022

2022 – Combining denoising of RNA-seq data and flux balance analysis for cluster analysis of single cells: Galuzzi, B., Vanoni, M., Damiani, C. (2022). Combining denoising of RNA-seq data and flux balance analysis for cluster analysis of single cells. BMC BIOINFORMATICS, 23(Suppl 6) [10.1186/s12859-022-04967-6].2022 – Machine Learning for Efficient Prediction of Protein Redox Potential: The Flavoproteins Case: Galuzzi, B., Mirarchi, A., Viganò, E., De Gioia, L., Damiani, C., Arrigoni, F. (2022). Machine Learning for Efficient Prediction of Protein Redox Potential: The Flavoproteins Case. JOURNAL OF CHEMICAL INFORMATION AND MODELING, 62(19), 4748-4759 [10.1021/acs.jcim.2c00858].2022 – J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments: Angaroni, F., Guidi, A., Ascolani, G., D’Onofrio, A., Antoniotti, M., Graudenzi, A. (2022). J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments. BMC BIOINFORMATICS, 23(1) [10.1186/s12859-022-04779-8].2022 – Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data: Ramazzotti, D., Maspero, D., Angaroni, F., Spinelli, S., Antoniotti, M., Piazza, R., et al. (2022). Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data. ISCIENCE, 25(6 (17 June 2022)) [10.1016/j.isci.2022.104487].2022 – SparseSignatures: An R package using LASSO-regularized non-negative matrix factorization to identify mutational signatures from human tumor samples: Mella, L., Lal, A., Angaroni, F., Maspero, D., Piazza, R., Sidow, A., et al. (2022). SparseSignatures: An R package using LASSO-regularized non-negative matrix factorization to identify mutational signatures from human tumor samples. STAR PROTOCOLS, 3(3) [10.1016/j.xpro.2022.101513].2022 – Variant calling from scRNA-seq data allows the assessment of cellular identity in patient-derived cell lines: Ramazzotti, D., Angaroni, F., Maspero, D., Ascolani, G., Castiglioni, I., Piazza, R., et al. (2022). Variant calling from scRNA-seq data allows the assessment of cellular identity in patient-derived cell lines. NATURE COMMUNICATIONS, 13(1 (December 2022)) [10.1038/s41467-022-30230-w].2022 – Chemotherapy after PD-1 inhibitors in relapsed/refractory Hodgkin lymphoma: Outcomes and clonal evolution dynamics: Calabretta, E., Guidetti, A., Ricci, F., Di Trani, M., Monfrini, C., Magagnoli, M., et al. (2022). Chemotherapy after PD-1 inhibitors in relapsed/refractory Hodgkin lymphoma: Outcomes and clonal evolution dynamics. BRITISH JOURNAL OF HAEMATOLOGY, 198(1), 82-92 [10.1111/bjh.18183].2022 – Large-Scale Analysis of SARS-CoV-2 Synonymous Mutations Reveals the Adaptation to the Human Codon Usage During the Virus Evolution: Ramazzotti, D., Angaroni, F., Maspero, D., Mauri, M., D’Aliberti, D., Fontana, D., et al. (2022). Large-Scale Analysis of SARS-CoV-2 Synonymous Mutations Reveals the Adaptation to the Human Codon Usage During the Virus Evolution. VIRUS EVOLUTION, 8(1) [10.1093/ve/veac026].2022 – INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation: Di Filippo, M., Pescini, D., Galuzzi, B., Bonanomi, M., Gaglio, D., Mangano, E., et al. (2022). INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation. PLOS COMPUTATIONAL BIOLOGY, 18(2) [10.1371/journal.pcbi.1009337].2022 – LACE: Inference of cancer evolution models from longitudinal single-cell sequencing data: Ramazzotti, D., Angaroni, F., Maspero, D., Ascolani, G., Castiglioni, I., Piazza, R., et al. (2022). LACE: Inference of cancer evolution models from longitudinal single-cell sequencing data. JOURNAL OF COMPUTATIONAL SCIENCE, 58(February 2022) [10.1016/j.jocs.2021.101523].2022 – PMCE: efficient inference of expressive models of cancer evolution with high prognostic power: Angaroni, F., Chen, K., Damiani, C., Caravagna, G., Graudenzi, A., Ramazzotti, D. (2022). PMCE: efficient inference of expressive models of cancer evolution with high prognostic power. BIOINFORMATICS, 38(3), 754-762 [10.1093/bioinformatics/btab717].

2021

2021 – Mutational signatures and heterogeneous host response revealed via large-scale characterization of SARS-CoV-2 genomic diversity: Graudenzi, A., Maspero, D., Angaroni, F., Piazza, R., Ramazzotti, D. (2021). Mutational signatures and heterogeneous host response revealed via large-scale characterization of SARS-CoV-2 genomic diversity. ISCIENCE, 24(2) [10.1016/j.isci.2021.102116].2021 – GPRuler: Metabolic gene-protein-reaction rules automatic reconstruction: Di Filippo, M., Damiani, C., Pescini, D. (2021). GPRuler: Metabolic gene-protein-reaction rules automatic reconstruction. PLOS COMPUTATIONAL BIOLOGY, 17(11) [10.1371/journal.pcbi.1009550].2021 – VirMutSig: Discovery and assignment of viral mutational signatures from sequencing data: Maspero, D., Angaroni, F., Porro, D., Piazza, R., Graudenzi, A., Ramazzotti, D. (2021). VirMutSig: Discovery and assignment of viral mutational signatures from sequencing data. STAR PROTOCOLS, 2(4), 100911 [10.1016/j.xpro.2021.100911].2021 – OG-SPACE: Optimized Stochastic Simulation of Spatial Models of Cancer Evolution: Angaroni, F., Antoniotti, M., Graudenzi, A. (2021). OG-SPACE: Optimized Stochastic Simulation of Spatial Models of Cancer Evolution [Altro].2021 – Optimal Control of a Discrete Time Stochastic Model of an Epidemic Spreading in Arbitrary Networks: Angaroni, F., Damiani, C., Ramunni, G., Antoniotti, M. (2021). Optimal Control of a Discrete Time Stochastic Model of an Epidemic Spreading in Arbitrary Networks. In Proceedings of the 2021 Annual Modeling and Simulation Conference, ANNSIM 2021 (pp.1-12). IEEE [10.23919/ANNSIM52504.2021.9552097].2021 – Combining multi-target regression deep neural networks and kinetic modeling to predict relative fluxes in reaction systems: Patruno, L., Craighero, F., Maspero, D., Graudenzi, A., Damiani, C. (2021). Combining multi-target regression deep neural networks and kinetic modeling to predict relative fluxes in reaction systems. INFORMATION AND COMPUTATION, 281 [10.1016/j.ic.2021.104798].2021 – Accelerated global sensitivity analysis of genome-wide constraint-based metabolic models: Nobile, M., Coelho, V., Pescini, D., Damiani, C. (2021). Accelerated global sensitivity analysis of genome-wide constraint-based metabolic models. BMC BIOINFORMATICS, 22(April 2021) [10.1186/s12859-021-04002-0].2021 – On the use of topological features of metabolic networks for the classification of cancer samples: Machicao, J., Craighero, F., Maspero, D., Angaroni, F., Damiani, C., Graudenzi, A., et al. (2021). On the use of topological features of metabolic networks for the classification of cancer samples. CURRENT GENOMICS, 22(2), 88-97 [10.2174/1389202922666210301084151].2021 – VERSO: a comprehensive framework for the inference of robust phylogenies and the quantification of intra-host genomic diversity of viral samples: Ramazzotti., D., Angaroni, F., Maspero, D., Gambacorti-Passerini, C., Antoniotti, M., Graudenzi, A., et al. (2021). VERSO: a comprehensive framework for the inference of robust phylogenies and the quantification of intra-host genomic diversity of viral samples. PATTERNS, 2(3 (12 March 2021)) [10.1016/j.patter.2021.100212].2021 – A review of computational strategies for denoising and imputation of single-cell transcriptomic data: Patruno, L., Maspero, D., Craighero, F., Angaroni, F., Antoniotti, M., Graudenzi, A. (2021). A review of computational strategies for denoising and imputation of single-cell transcriptomic data. BRIEFINGS IN BIOINFORMATICS, 22(4 (July 2021)) [10.1093/bib/bbaa222].

2020

2020 – The detection of dynamical organization in cancer evolution models: Sani, L., D’Addese, G., Graudenzi, A., Villani, M. (2020). The detection of dynamical organization in cancer evolution models. In Artificial Life and Evolutionary Computation. 14th Italian Workshop, WIVACE 2019, Rende, Italy, September 18–20, 2019, Revised Selected Papers (pp.49-61). Springer [10.1007/978-3-030-45016-8_6].2020 – Read-through transcripts in lung: Germline genetic regulation and correlation with the expression of other genes: Maspero, D., Dassano, A., Pintarelli, G., Noci, S., de Cecco, L., Incarbone, M., et al. (2020). Read-through transcripts in lung: Germline genetic regulation and correlation with the expression of other genes. CARCINOGENESIS, 41(7), 918-926 [10.1093/CARCIN/BGAA020].2020 – Integration of single-cell RNA-sequencing data into flux balance cellular automata: Maspero, D., Di Filippo, M., Angaroni, F., Pescini, D., Mauri, G., Vanoni, M., et al. (2020). Integration of single-cell RNA-sequencing data into flux balance cellular automata. In CIBB 2019 – Computational Intelligence methods for Bioinformatics and Biostatistics, Revised selected papers (pp.207-215). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-63061-4_19].2020 – Understanding deep learning with activation pattern diagrams: Craighero, F., Angaroni, F., Graudenzi, A., Stella, F., Antoniotti, M. (2020). Understanding deep learning with activation pattern diagrams. In CEUR Workshop Proceedings (pp.119-126). CEUR-WS.2020 – A closed-loop optimization framework for personalized cancer therapy design: Angaroni, F., Pennati, M., Patruno, L., Maspero, D., Antoniotti, M., Graudenzi, A. (2020). A closed-loop optimization framework for personalized cancer therapy design. In 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (pp.1-9) [10.1109/CIBCB48159.2020.9277647].2020 – VERSO:A COMPREHENSIVE FRAMEWORK FOR THE INFERENCE OF ROBUST PHYLOGENIES AND THE QUANTIFICATION OF INTRA-HOST GENOMIC DIVERSITY OF VIRAL SAMPLES: Ramazzotti, D., Angaroni, F., Maspero, D., Gambacorti-Passerini, C., Antoniotti, M., Graudenzi, A., et al. (2020). VERSO:A COMPREHENSIVE FRAMEWORK FOR THE INFERENCE OF ROBUST PHYLOGENIES AND THE QUANTIFICATION OF INTRA-HOST GENOMIC DIVERSITY OF VIRAL SAMPLES [Altro] [10.1101/2020.04.22.044404].2020 – Systems metabolomics: from metabolomic snapshots to design principles: Damiani, C., Gaglio, D., Sacco, E., Alberghina, L., Vanoni, M. (2020). Systems metabolomics: from metabolomic snapshots to design principles. CURRENT OPINION IN BIOTECHNOLOGY, 63, 190-199 [10.1016/j.copbio.2020.02.013].2020 – On the automatic calibration of fully analogical spiking neuromorphic chips: Papetti, D., Spolaor, S., Besozzi, D., Cazzaniga, P., Antoniotti, M., Nobile, M. (2020). On the automatic calibration of fully analogical spiking neuromorphic chips. In 2020 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical and Electronics Engineers Inc. [10.1109/IJCNN48605.2020.9206654].2020 – SBML Level 3: an extensible format for the exchange and reuse of biological models: Keating, S., Waltemath, D., König, M., Zhang, F., Dräger, A., Chaouiya, C., et al. (2020). SBML Level 3: an extensible format for the exchange and reuse of biological models. MOLECULAR SYSTEMS BIOLOGY, 16(8) [10.15252/msb.20199110].2020 – An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments: Angaroni, F., Graudenzi, A., Rossignolo, M., Maspero, D., Calarco, T., Piazza, R., et al. (2020). An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments. FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, 8 [10.3389/fbioe.2020.00523].2020 – Investigating the Compositional Structure Of Deep Neural Networks: Craigher, F., Angaroni, F., Graudenzi, A., Stella, F., Antoniotti, M. (2020). Investigating the Compositional Structure Of Deep Neural Networks. In The Sixth International Conference on Machine Learning, Optimization, and Data Science (pp.322-334) [10.1007/978-3-030-64583-0_30].2020 – Characterization of intra-host SARS-CoV-2 variants improves phylogenomic reconstruction and may reveal functionally convergent mutations: Ramazzotti, D., Angaroni, F., Maspero, D., Gambacorti-Passerini, C., Antoniotti, M., Graudenzi, A., et al. (2020). Characterization of intra-host SARS-CoV-2 variants improves phylogenomic reconstruction and may reveal functionally convergent mutations [Rapporto tecnico] [10.1101/2020.04.22.044404].2020 – MaREA4Galaxy: metabolic reaction enrichment analysis and visualization of RNA-seq data using Galaxy: Damiani, C., Rovida, L., Maspero, D., Sala, I., Rosato, L., Di Filippo, M., et al. (2020). MaREA4Galaxy: metabolic reaction enrichment analysis and visualization of RNA-seq data using Galaxy. COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 18, 993-999 [10.1016/j.csbj.2020.04.008].2020 – Application of Random Forest algorithm for Prediction of False positive Insertion sites in gene therapy treated patient’s integrome profile: Omrani, M., Calabria, A., Antoniotti, M., Aiuti, A. (2020). Application of Random Forest algorithm for Prediction of False positive Insertion sites in gene therapy treated patient’s integrome profile. Intervento presentato a: RECOMB – Computational Cancer Biology 2020, Padua, Italy.2020 – Why You Cannot (Yet) Write an “Interval Arithmetic” Library in Common Lisp — or: Hammering Some Sense into :ieee-floating-point: Antoniotti, M. (2020). Why You Cannot (Yet) Write an “Interval Arithmetic” Library in Common Lisp — or: Hammering Some Sense into :ieee-floating-point. Intervento presentato a: European Lisp Symposium 2020, Zurich, Switzerland [10.5281/zenodo.3759522].2020 – Global Sensitivity Analysis of Constraint-Based Metabolic Models: Damiani, C., Pescini, D., Nobile, M. (2020). Global Sensitivity Analysis of Constraint-Based Metabolic Models. In Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2018 (pp.179-186). Cham : Springer [10.1007/978-3-030-34585-3_16].2020 – Single-cell digital twins for cancer preclinical investigation: Di Filippo, M., Damiani, C., Vanoni, M., Maspero, D., Mauri, G., Alberghina, L., et al. (2020). Single-cell digital twins for cancer preclinical investigation. In Nagrath D. (a cura di), Metabolic Flux Analysis in Eukaryotic Cells (pp. 331-343). New York : Humana Press Inc. [10.1007/978-1-0716-0159-4_15].2020 – Longitudinal cancer evolution from single cells: Ramazzotti, D., Angaroni, F., Maspero, D., Ascolani, G., Castiglioni, I., Piazza, R., et al. (2020). Longitudinal cancer evolution from single cells [Rapporto tecnico] [10.1101/2020.01.14.906453].2020 – Network Bioscience: Pellegrini, M., Antoniotti, M., Mishra, M. (a cura di). (2020). Network Bioscience. Frontiers Media SA, Lausanne, CH [10.3389/978-2-88963-289-3].2020 – FBCA, A multiscale modeling framework combining cellular automata and flux balance analysis: Graudenzi, A., Maspero, D., Damiani, C. (2020). FBCA, A multiscale modeling framework combining cellular automata and flux balance analysis. JOURNAL OF CELLULAR AUTOMATA, 15(1-2), 75-95.

2019

2019 – Cigarette smoke alters the transcriptome of non-involved lung tissue in lung adenocarcinoma patients: Pintarelli, G., Noci, S., Maspero, D., Pettinicchio, A., Dugo, M., De Cecco, L., et al. (2019). Cigarette smoke alters the transcriptome of non-involved lung tissue in lung adenocarcinoma patients. SCIENTIFIC REPORTS, 9(1) [10.1038/s41598-019-49648-2].2019 – The Influence of Nutrients Diffusion on a Metabolism-driven Model of a Multi-cellular System: Maspero, D., Damiani, C., Antoniotti, M., Graudenzi, A., Di Filippo, M., Vanoni, M., et al. (2019). The Influence of Nutrients Diffusion on a Metabolism-driven Model of a Multi-cellular System. FUNDAMENTA INFORMATICAE, 171(1-4), 279-295 [10.3233/FI-2020-1883].2019 – Editorial: Network Bioscience: Antoniotti, M., Mishra, B., Pellegrini, M. (2019). Editorial: Network Bioscience. FRONTIERS IN GENETICS, 10 [10.3389/fgene.2019.01160].2019 – ISwap: a bioinformatics tool for index switching detection in vector integration site studies: De Marino, A., Calabria, A., Benedicenti, F., Antoniotti, M., Montini, E. (2019). ISwap: a bioinformatics tool for index switching detection in vector integration site studies. Intervento presentato a: Computational Intelligence methods for Bioinformatics and Biostatistics 2019 (CIBB 2019), Bergamo.2019 – cyTRON and cyTRON/JS: two Cytoscape-based applications for the inference of cancer evolution models: Patruno, L., Galimberti, E., Ramazzotti, D., Caravagna, G., De Sano, L., Antoniotti, M., et al. (2019). cyTRON and cyTRON/JS: two Cytoscape-based applications for the inference of cancer evolution models. Intervento presentato a: Computational Intelligence methods for Bioinformatics and Biostatistics 2019, Bergamo, Italy.2019 – Personalized therapy design for liquid tumors via optimal control theory: Angaroni, F., Graudenzi, A., Rossignolo, M., Maspero, D., Calarco, T., Piazza, R., et al. (2019). Personalized therapy design for liquid tumors via optimal control theory [Altro] [10.1101/662858].2019 – Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data: Ramazzotti, D., Graudenzi, A., De Sano, L., Antoniotti, M., Caravagna, G. (2019). Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data. BMC BIOINFORMATICS, 20(1) [10.1186/s12859-019-2795-4].2019 – Integration of single-cell RNA-seq data into population models to characterize cancer metabolism: Damiani, C., Maspero, D., Di Filippo, M., Colombo, R., Pescini, D., Graudenzi, A., et al. (2019). Integration of single-cell RNA-seq data into population models to characterize cancer metabolism. PLOS COMPUTATIONAL BIOLOGY, 15(2) [10.1371/journal.pcbi.1006733].2019 – Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena: Ramazzotti, D., Nobile, M., Antoniotti, M., Graudenzi, A. (2019). Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena. JOURNAL OF COMPUTATIONAL SCIENCE, 30, 1-10 [10.1016/j.jocs.2018.10.009].2019 – Synchronization effects in a metabolism-driven model of multi-cellular system: Maspero, D., Graudenzi, A., AMARPAL SINGH, A., Pescini, D., Mauri, G., Antoniotti, M., et al. (2019). Synchronization effects in a metabolism-driven model of multi-cellular system. In 13th Italian Workshop, WIVACE 2018, Parma, Italy, September 10–12, 2018, Revised Selected Papers (pp.115-126) [10.1007/978-3-030-21733-4_9].

2018

2018 – Modeling Spatio-Temporal Dynamics of Metabolic Networks with Cellular Automata and Constraint-Based Methods: Graudenzi, A., Maspero, D., Damiani, C. (2018). Modeling Spatio-Temporal Dynamics of Metabolic Networks with Cellular Automata and Constraint-Based Methods. In Cellular Automata 13th International Conference on Cellular Automata for Research and Industry, ACRI 2018, Como, Italy, September 17–21, 2018, Proceedings Part of the book series: Theoretical Computer Science and General Issues (LNTCS, volume 11115) Part of the Lecture Notes in Computer Science book series (LNTCS,volume 11115) Conference series link(s): ACRI: International Conference on Cellular Automata for Research and Industry (pp.16-29). Springer Verlag [10.1007/978-3-319-99813-8_2].2018 – GDNA qPCR is statistically more reliable than mRNA analysis in detecting leukemic cells to monitor CML: Rainero, A., Angaroni, F., Conti, A., Pirrone, C., Micheloni, G., Tarara, L., et al. (2018). GDNA qPCR is statistically more reliable than mRNA analysis in detecting leukemic cells to monitor CML. CELL DEATH & DISEASE, 9(3) [10.1038/s41419-018-0387-2].2018 – Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power: Graudenzi, A., Maspero, D., Di Filippo, M., Gnugnoli, M., Isella, C., Mauri, G., et al. (2018). Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power. JOURNAL OF BIOMEDICAL INFORMATICS, 87, 37-49 [10.1016/j.jbi.2018.09.010].2018 – Modeling cumulative biological phenomena with Suppes-Bayes causal networks: Ramazzotti, D., Graudenzi, A., Caravagna, G., Antoniotti, M. (2018). Modeling cumulative biological phenomena with Suppes-Bayes causal networks. EVOLUTIONARY BIOINFORMATICS ONLINE, 14, 1-10 [10.1177/1176934318785167].2018 – Structural Learning of Probabilistic Graphical Models of Cumulative Phenomena: Ramazzotti, D., Nobile, M., Antoniotti, M., Graudenzi, A. (2018). Structural Learning of Probabilistic Graphical Models of Cumulative Phenomena. In Computational Science 2018 – ICCS 2018 (pp.678-693) [10.1007/978-3-319-93698-7_52].2018 – Omics and Clinical Data Integration: De Sanctis, G., Colombo, R., Damiani, C., Sacco, E., Vanoni, M. (2018). Omics and Clinical Data Integration. In A. Vlahou, H. Mischak, J. Zoidakis, F. Magni (a cura di), Integration of Omics Approaches and Systems Biology for Clinical Applications (pp. 248-273). Hoboken : John Wiley & Sons [10.1002/9781119183952.ch15].2018 – Emerging ensembles of kinetic parameters to characterize observed metabolic phenotypes: Colombo, R., Damiani, C., Gilbert, D., Heiner, M., Mauri, G., Pescini, D. (2018). Emerging ensembles of kinetic parameters to characterize observed metabolic phenotypes. BMC BIOINFORMATICS, 19(Suppl 7), 45-59 [10.1186/s12859-018-2181-7].2018 – MaREA: Metabolic feature extraction, enrichment and visualization of RNAseq data: Graudenzi, A., Maspero, D., Isella, C., Di Filippo, M., Mauri, G., Medico, E., et al. (2018). MaREA: Metabolic feature extraction, enrichment and visualization of RNAseq data [Altro] [10.1101/248724].

2017

2017 – Amplification of the parametric dynamical Casimir effect via optimal control: Hoeb, F., Angaroni, F., Zoller, J., Calarco, T., Strini, G., Montangero, S., et al. (2017). Amplification of the parametric dynamical Casimir effect via optimal control. PHYSICAL REVIEW A, 96(3) [10.1103/PhysRevA.96.033851].2017 – Dynamical regimes in non-ergodic random Boolean networks: Villani, M., Campioli, D., Damiani, C., Roli, A., Filisetti, A., Serra, A. (2017). Dynamical regimes in non-ergodic random Boolean networks. NATURAL COMPUTING, 16(2), 353-363 [10.1007/s11047-016-9552-7].2017 – COSYS: A computational infrastructure for systems biology: Cumbo, F., Nobile, M., Damiani, C., Colombo, R., Mauri, G., Cazzaniga, P. (2017). COSYS: A computational infrastructure for systems biology. In CIBB 2016 – 13th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, Revised Selected Papers (pp.82-92). Springer Verlag [10.1007/978-3-319-67834-4_7].2017 – Constraining mechanism based simulations to identify ensembles of parametrizations to characterize metabolic features: Colombo, R., Damiani, C., Mauri, G., Pescini, D. (2017). Constraining mechanism based simulations to identify ensembles of parametrizations to characterize metabolic features. In Computational Intelligence Methods for Bioinformatics and Biostatistics. 13th International Meeting, CIBB 2016, Stirling, UK, September 1-3, 2016, Revised Selected Papers (pp.107-117). Springer Verlag [10.1007/978-3-319-67834-4_9].2017 – 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: Damiani, C., Colombo, R., Gaglio, D., Mastroianni, F., Pescini, D., Westerhoff, H., et al. (2017). 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), 1-29 [10.1371/journal.pcbi.1005758].2017 – A computational framework to infer the order of accumulating mutations in individual tumors: Ramazzotti, D., Graudenzi, A., De Sano, L., Antoniotti, M., Caravagna, G. (2017). A computational framework to infer the order of accumulating mutations in individual tumors. Intervento presentato a: Base Computational Biology Conference [BC]^2, Basilea.2017 – popFBA: tackling intratumour heterogeneity with Flux Balance Analysis: Damiani, C., Di Filippo, M., Pescini, D., Maspero, D., Colombo, R., Mauri, G. (2017). popFBA: tackling intratumour heterogeneity with Flux Balance Analysis. BIOINFORMATICS, 33(14), i311-i318 [10.1093/bioinformatics/btx251].2017 – Constraint-based modeling and simulation of cell populations: Di Filippo, M., Damiani, C., Colombo, R., Pescini, D., Mauri, G. (2017). Constraint-based modeling and simulation of cell populations. In Advances in Artificial Life, Evolutionary Computation, and Systems Chemistry. 11th Italian Workshop, WIVACE 2016, Fisciano, Italy, October 4-6, 2016, Revised Selected Papers (pp.126-137). Springer Verlag [10.1007/978-3-319-57711-1_11].2017 – Pathway-based classification of breast cancer subtypes: Graudenzi, A., Cava, C., Bertoli, G., Fromm, B., Flatmark, K., Mauri, G., et al. (2017). Pathway-based classification of breast cancer subtypes. FRONTIERS IN BIOSCIENCE, 22(10), 1697-1712 [10.2741/4566].2017 – Linking alterations in metabolic fluxes with shifts in metabolite levels by means of kinetic modeling: Damiani, C., Colombo, R., Di Filippo, M., Pescini, D., Mauri, G. (2017). Linking alterations in metabolic fluxes with shifts in metabolite levels by means of kinetic modeling. In Advances in Artificial Life, Evolutionary Computation, and Systems Chemistry. 11th Italian Workshop, WIVACE 2016, Fisciano, Italy, October 4-6, 2016, Revised Selected Papers (pp.138-148). Springer Verlag [10.1007/978-3-319-57711-1_12].2017 – A Computational Framework To Infer The Order Of Accumulating Mutations In Individual Tumors: Ramazzotti, D., Graudenzi, A., De Sano, L., Antoniotti, M., Caravagna, G. (2017). A Computational Framework To Infer The Order Of Accumulating Mutations In Individual Tumors [Altro] [10.1101/132183].2017 – SpidermiR: An R/bioconductor package for integrative analysis with miRNA data: Cava, C., Colaprico, A., Bertoli, G., Graudenzi, A., Silva, T., Olsen, C., et al. (2017). SpidermiR: An R/bioconductor package for integrative analysis with miRNA data. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 18(2), 1-13 [10.3390/ijms18020274].

2016

2016 – Constraint-based modeling and simulation of cell populations: DI FILIPPO, M., Damiani, C., Colombo, R., Pescini, D., Mauri, G. (2016). Constraint-based modeling and simulation of cell populations [Working paper].2016 – Parallel implementation of efficient search schemes for the inference of cancer progression models: Ramazzotti, D., Nobile, M., Cazzaniga, P., Mauri, G., Antoniotti, M. (2016). Parallel implementation of efficient search schemes for the inference of cancer progression models. In 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (pp.1-6). Institute of Electrical and Electronics Engineers Inc. [10.1109/CIBCB.2016.7758109].2016 – Design of the TRONCO bioconductor package for TRanslational ONCOlogy: Antoniotti, M., Caravagna, G., De Sano, L., Graudenzi, A., Mauri, G., Mishra, B., et al. (2016). Design of the TRONCO bioconductor package for TRanslational ONCOlogy. THE R JOURNAL, 8(2), 39-59 [10.32614/RJ-2016-032].2016 – Divergent in vitro/in vivo responses to drug treatments of highly aggressive NIH-Ras cancer cells: A PET imaging and metabolomics-mass-spectrometry study: Gaglio, D., Valtorta, S., Ripamonti, M., Bonanomi, M., Damiani, C., Todde, S., et al. (2016). Divergent in vitro/in vivo responses to drug treatments of highly aggressive NIH-Ras cancer cells: A PET imaging and metabolomics-mass-spectrometry study. ONCOTARGET, 7(32), 52017-52031 [10.18632/oncotarget.10470].2016 – Algorithmic methods to infer the evolutionary trajectories in cancer progression: Caravagna, G., Graudenzi, A., Ramazzotti, D., Sanz Pamplona, R., De Sano, L., Mauri, G., et al. (2016). Algorithmic methods to infer the evolutionary trajectories in cancer progression. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 113(28), E4025-E4034 [10.1073/pnas.1520213113].2016 – Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models: DI FILIPPO, M., Colombo, R., Damiani, C., Pescini, D., Gaglio, D., Vanoni, M., et al. (2016). Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models. COMPUTATIONAL BIOLOGY AND CHEMISTRY, 62, 60-69 [10.1016/j.compbiolchem.2016.03.002].2016 – CABeRNET: A Cytoscape app for augmented Boolean models of gene regulatory NETworks: Paroni, A., Graudenzi, A., Caravagna, G., Damiani, C., Mauri, G., Antoniotti, M. (2016). CABeRNET: A Cytoscape app for augmented Boolean models of gene regulatory NETworks. BMC BIOINFORMATICS, 17(1) [10.1186/s12859-016-0914-z].2016 – TRONCO: An R package for the inference of cancer progression models from heterogeneous genomic data: De Sano, L., Caravagna, G., Ramazzotti, D., Graudenzi, A., Mauri, G., Mishra, B., et al. (2016). TRONCO: An R package for the inference of cancer progression models from heterogeneous genomic data. BIOINFORMATICS, 32(12), 1911-1913 [10.1093/bioinformatics/btw035].2016 – Different regulation of miR-29a-3p in glomeruli and tubules in an experimental model of angiotensin II-dependent hypertension: Potential role in renal fibrosis: Castoldi, G., Di Gioia, C., Giollo, F., Carletti, R., BOMBARDI ROSA, C., Antoniotti, M., et al. (2016). Different regulation of miR-29a-3p in glomeruli and tubules in an experimental model of angiotensin II-dependent hypertension: Potential role in renal fibrosis. CLINICAL AND EXPERIMENTAL PHARMACOLOGY & PHYSIOLOGY., 43(3), 335-342 [10.1111/1440-1681.12532].2016 – Ordering cancer mutational profiles of cross-sectional copy number alterations: Graudenzi, A., Caravagna, G., Bocicor, I., Cava, C., Antoniotti, M., Mauri, G. (2016). Ordering cancer mutational profiles of cross-sectional copy number alterations. INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, 15(1), 59-83 [10.1504/IJDMB.2016.076017].

2015

2015 – Cognac: A chaste plugin for the multiscale simulation of gene regulatory networks driving the spatial dynamics of tissues and cancer: Rubinacci, S., Graudenzi, A., Caravagna, G., Mauri, G., Osborne, J., Pitt Francis, J., et al. (2015). Cognac: A chaste plugin for the multiscale simulation of gene regulatory networks driving the spatial dynamics of tissues and cancer. CANCER INFORMATICS, 14(Suppl. 4), 53-65 [10.4137/CIN.S19965].2015 – Automatising the analysis of stochastic biochemical time-series: Caravagna, G., De Sano, L., Antoniotti, M. (2015). Automatising the analysis of stochastic biochemical time-series. BMC BIOINFORMATICS, 16(9) [10.1186/1471-2105-16-S9-S8].2015 – CAPRI: Efficient Inference of Cancer Progression Models from Cross-sectional Data: Ramazzotti, D., Caravagna, G., Olde Loohuis, L., Graudenzi, A., Korsunsky, I., Mauri, G., et al. (2015). CAPRI: Efficient Inference of Cancer Progression Models from Cross-sectional Data. BIOINFORMATICS, 31(18), 3016-3026 [10.1093/bioinformatics/btv296].

Publications

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