Escherichia coli as a model organism for systems metabolic engineering

Societal demands for fuels and chemical products can be met using microbial biofactories, but this requires an increased understanding of microbial systems. To increase supply of these fuels and chemicals, the design-build-test cycle of metabolic engineering needs to be faster and more programmatic....

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Bibliographic Details
Published in:Current opinion in systems biology 2017-12, Vol.6 (C), p.80-88
Main Authors: Adamczyk, Paul A., Reed, Jennifer L.
Format: Article
Language:English
Subjects:
Adaptive laboratory evolution
Biofuels
Build
Computational
Cycle
Design
E. coli
Fluxomics
Genome-scale
Genomics
In silico
Kinetic
Metabolic
Metabolomics
Model organism
Modeling
Omics
Proteomics
Regulatory
Simulated
Stoichiometric
Synthetic biology
Systems biology
Systems metabolic engineering
Test
Transcriptional
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Summary:Societal demands for fuels and chemical products can be met using microbial biofactories, but this requires an increased understanding of microbial systems. To increase supply of these fuels and chemicals, the design-build-test cycle of metabolic engineering needs to be faster and more programmatic. However, this cycle is often hampered by a limited understanding of the intricate relationships between environments, genes, transcripts, proteins, metabolites, and fluxes—even for model organisms such as Escherichia coli. The developing field of systems biology is well-suited for identifying these global interactions to enable microbes, such as E. coli—arguably the most widely used and relatively well understood—to be rapidly developed into chemical production strains. Recent systems biology tools including genome-scale modeling, ‘omics datasets, and adaptive laboratory evolution will be detailed in this review along with examples of how these tools have been successfully used to metabolically engineer E. coli for chemical production. [Display omitted] •Integrated systems-level approaches are needed to rapidly construct robust strains.•‘omics technologies are effective at probing systems/pathway-level phenomena.•ALE is a fast technique to improve fitness and production phenotypes.•Models with kinetic and transcriptional information are becoming more prevalent.•Systems metabolic engineering tools need increased accessibility and usage.
ISSN:2452-3100
2452-3100
DOI:10.1016/j.coisb.2017.11.001