Integration of enzymatic data in Bacillus subtilis genome-scale metabolic model improves phenotype predictions and enables in silico design of poly-γ-glutamic acid production strains

Integration of enzymatic data in Bacillus subtilis genome-scale metabolic model improves phenotype predictions and enables in silico design of poly-γ-glutamic acid production strains

Abstract Background Genome-scale metabolic models (GEMs) allow predicting metabolic phenotypes from limited data on uptake and secretion fluxes by defining the space of all the feasible solutions and excluding physio-chemically and biologically unfeasible behaviors. The integration of additional bio...

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Bibliographic Details
Main Authors: Ilaria Massaiu, Lorenzo Pasotti, Nikolaus Sonnenschein, Erlinda Rama, Matteo Cavaletti, Paolo Magni, Cinzia Calvio, Markus J. Herrgård
Format: Article
Language:English
Published: BMC 2019-01-01
Series:Microbial Cell Factories
Subjects:
Genome-scale metabolic model
Enzymatic data
Bacillus subtilis
Constraint-based methods
Poly-γ-glutamic acid
Online Access:http://link.springer.com/article/10.1186/s12934-018-1052-2

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