Literature DB >> 27085310

Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models.

Marzia Di Filippo1, Riccardo Colombo1, Chiara Damiani1, Dario Pescini2, Daniela Gaglio3, Marco Vanoni4, Lilia Alberghina4, Giancarlo Mauri1.   

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.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Cancer metabolic rewiring; Core metabolic model; Flux Balance Analysis; Network reconstruction

Mesh:

Year:  2016        PMID: 27085310     DOI: 10.1016/j.compbiolchem.2016.03.002

Source DB:  PubMed          Journal:  Comput Biol Chem        ISSN: 1476-9271            Impact factor:   2.877


  10 in total

1.  Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN.

Authors:  Maria Masid; Meric Ataman; Vassily Hatzimanikatis
Journal:  Nat Commun       Date:  2020-06-04       Impact factor: 14.919

Review 2.  The role of metabolic ecosystem in cancer progression - metabolic plasticity and mTOR hyperactivity in tumor tissues.

Authors:  Anna Sebestyén; Titanilla Dankó; Dániel Sztankovics; Dorottya Moldvai; Regina Raffay; Catherine Cervi; Ildikó Krencz; Viktória Zsiros; András Jeney; Gábor Petővári
Journal:  Cancer Metastasis Rev       Date:  2022-01-14       Impact factor: 9.264

3.  A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling.

Authors:  Supreeta Vijayakumar; Giuseppe Magazzù; Pradip Moon; Annalisa Occhipinti; Claudio Angione
Journal:  Methods Mol Biol       Date:  2022

4.  MitoCore: a curated constraint-based model for simulating human central metabolism.

Authors:  Anthony C Smith; Filmon Eyassu; Jean-Pierre Mazat; Alan J Robinson
Journal:  BMC Syst Biol       Date:  2017-11-25

Review 5.  Towards the routine use of in silico screenings for drug discovery using metabolic modelling.

Authors:  Tamara Bintener; Maria Pires Pacheco; Thomas Sauter
Journal:  Biochem Soc Trans       Date:  2020-06-30       Impact factor: 5.407

6.  Emerging ensembles of kinetic parameters to characterize observed metabolic phenotypes.

Authors:  Riccardo Colombo; Chiara Damiani; David Gilbert; Monika Heiner; Giancarlo Mauri; Dario Pescini
Journal:  BMC Bioinformatics       Date:  2018-07-09       Impact factor: 3.169

7.  GPRuler: Metabolic gene-protein-reaction rules automatic reconstruction.

Authors:  Marzia Di Filippo; Chiara Damiani; Dario Pescini
Journal:  PLoS Comput Biol       Date:  2021-11-08       Impact factor: 4.475

8.  INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation.

Authors:  Marzia Di Filippo; Dario Pescini; Bruno Giovanni Galuzzi; Marcella Bonanomi; Daniela Gaglio; Eleonora Mangano; Clarissa Consolandi; Lilia Alberghina; Marco Vanoni; Chiara Damiani
Journal:  PLoS Comput Biol       Date:  2022-02-07       Impact factor: 4.475

Review 9.  Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer.

Authors:  Rachel H Ng; Jihoon W Lee; Priyanka Baloni; Christian Diener; James R Heath; Yapeng Su
Journal:  Front Oncol       Date:  2022-07-07       Impact factor: 5.738

10.  popFBA: tackling intratumour heterogeneity with Flux Balance Analysis.

Authors:  Chiara Damiani; Marzia Di Filippo; Dario Pescini; Davide Maspero; Riccardo Colombo; Giancarlo Mauri
Journal:  Bioinformatics       Date:  2017-07-15       Impact factor: 6.937

  10 in total

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