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Development and validation of an updated computational model of Streptomyces coelicolor primary and secondary metabolism.


ABSTRACT:

Background

Streptomyces species produce a vast diversity of secondary metabolites of clinical and biotechnological importance, in particular antibiotics. Recent developments in metabolic engineering, synthetic and systems biology have opened new opportunities to exploit Streptomyces secondary metabolism, but achieving industry-level production without time-consuming optimization has remained challenging. Genome-scale metabolic modelling has been shown to be a powerful tool to guide metabolic engineering strategies for accelerated strain optimization, and several generations of models of Streptomyces metabolism have been developed for this purpose.

Results

Here, we present the most recent update of a genome-scale stoichiometric constraint-based model of the metabolism of Streptomyces coelicolor, the major model organism for the production of antibiotics in the genus. We show that the updated model enables better metabolic flux and biomass predictions and facilitates the integrative analysis of multi-omics data such as transcriptomics, proteomics and metabolomics.

Conclusions

The updated model presented here provides an enhanced basis for the next generation of metabolic engineering attempts in Streptomyces.

SUBMITTER: Amara A 

PROVIDER: S-EPMC6040156 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Development and validation of an updated computational model of Streptomyces coelicolor primary and secondary metabolism.

Amara Adam A   Takano Eriko E   Breitling Rainer R  

BMC genomics 20180704 1


<h4>Background</h4>Streptomyces species produce a vast diversity of secondary metabolites of clinical and biotechnological importance, in particular antibiotics. Recent developments in metabolic engineering, synthetic and systems biology have opened new opportunities to exploit Streptomyces secondary metabolism, but achieving industry-level production without time-consuming optimization has remained challenging. Genome-scale metabolic modelling has been shown to be a powerful tool to guide metab  ...[more]

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