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.
journal_name
Comput Biol Chemjournal_title
Computational biology and chemistryauthors
Di Filippo M,Colombo R,Damiani C,Pescini D,Gaglio D,Vanoni M,Alberghina L,Mauri Gdoi
10.1016/j.compbiolchem.2016.03.002subject
Has Abstractpub_date
2016-06-01 00:00:00pages
60-9eissn
1476-9271issn
1476-928Xpii
S1476-9271(15)30014-1journal_volume
62pub_type
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journal_title：Computational biology and chemistry
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