Stoichiometric gene-to-reaction associations enhance model-driven analysis performance: Metabolic response to chronic exposure to Aldrin in prostate cancer

Stoichiometric gene-to-reaction associations enhance model-driven analysis performance: Metabolic response to chronic exposure to Aldrin in prostate cancer

Abstract Background Genome-scale metabolic models (GSMM) integrating transcriptomics have been widely used to study cancer metabolism. This integration is achieved through logical rules that describe the association between genes, proteins, and reactions (GPRs). However, current gene-to-reaction for...

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Bibliographic Details
Main Authors: Igor Marín de Mas, Laura Torrents, Carmen Bedia, Lars K. Nielsen, Marta Cascante, Romà Tauler
Format: Article
Language:English
Published: BMC 2019-08-01
Series:BMC Genomics
Subjects:
Genome-scale metabolic model
Prostate Cancer
Transcriptomic data integration
Stoichiometric gene-protein-reaction association
Endocrine disruptors
Online Access:http://link.springer.com/article/10.1186/s12864-019-5979-4

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http://link.springer.com/article/10.1186/s12864-019-5979-4

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