title: Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models.
abstract: The metabolic rearrangements occurring in cancer cells can be effectively investigated with a Systems Biology approach supported by metabolic network modeling. We here present tissue-specific constraint-based core models for three different types of tumors (liver, breast and lung) that serve this purpose. The core models were extracted and manually curated from the corresponding genome-scale metabolic models in the Human Metabolic Atlas database with a focus on the pathways that are known to play a key role in cancer growth and proliferation. Along similar lines, we also reconstructed a core model from the original general human metabolic network to be used as a reference model. A comparative Flux Balance Analysis between the reference and the cancer models highlighted both a clear distinction between the two conditions and a heterogeneity within the three different cancer types in terms of metabolic flux distribution. These results emphasize the need for modeling approaches able to keep up with this tumoral heterogeneity in order to identify more suitable drug targets and develop effective treatments. According to this perspective, we identified key points able to reverse the tumoral phenotype toward the reference one or vice-versa.
authors.affiliation: SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy.
authors.forename: Marzia
authors.initials: M
authors.lastname: Di Filippo
authors.affiliation: SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy.
authors.forename: Riccardo
authors.initials: R
authors.lastname: Colombo
authors.affiliation: SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy.
authors.forename: Chiara
authors.initials: C
authors.lastname: Damiani
authors.affiliation: SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Statistica e Metodi Quantitativi, Università degli Studi di Milano-Bicocca, Via Bicocca degli Arcimboldi 8, 20126 Milano, Italy. Electronic address: dario.pescini@unimib.it.
authors.forename: Dario
authors.initials: D
authors.lastname: Pescini
authors.affiliation: SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Istituto di Bioimmagini e Fisiologia Molecolare, Consiglio Nazionale delle Ricerche, Via F.lli Cervi 93, 20090 Segrate (MI), Italy.
authors.forename: Daniela
authors.initials: D
authors.lastname: Gaglio
authors.affiliation: SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Biotecnologie e Bioscienze, Università degli Studi di Milano-Bicocca, Piazza della Scienza 2, 20126 Milano, Italy.
authors.forename: Marco
authors.initials: M
authors.lastname: Vanoni
authors.affiliation: SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Biotecnologie e Bioscienze, Università degli Studi di Milano-Bicocca, Piazza della Scienza 2, 20126 Milano, Italy.
authors.forename: Lilia
authors.initials: L
authors.lastname: Alberghina
authors.affiliation: SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy.
authors.forename: Giancarlo
authors.initials: G
authors.lastname: Mauri
mesh_terms: D019295:Computational Biology; D006801:Humans; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D009369:Neoplasms / Q000378:metabolism* / Q000503:physiopathology
keywords: Cancer metabolic rewiring; Core metabolic model; Flux Balance Analysis; Network reconstruction