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Combinatorial genetic perturbation to refine metabolic circuits for producing biofuels and biochemicals

Paper ID Volume ID Publish Year Pages File Format Full-Text
14357 1210 2013 10 PDF Available
Title
Combinatorial genetic perturbation to refine metabolic circuits for producing biofuels and biochemicals
Abstract

•Identification of gene targets is a central problem of metabolic engineering.•Tractable and transferable genetic libraries are employed to identify target genes.•A hybrid approach using various libraries will confer versatility on microbes.

Recent advances in metabolic engineering have enabled microbial factories to compete with conventional processes for producing fuels and chemicals. Both rational and combinatorial approaches coupled with synthetic and systematic tools play central roles in metabolic engineering to create and improve a selected microbial phenotype. Compared to knowledge-based rational approaches, combinatorial approaches exploiting biological diversity and high-throughput screening have been demonstrated as more effective tools for improving various phenotypes of interest. In particular, identification of unprecedented targets to rewire metabolic circuits for maximizing yield and productivity of a target chemical has been made possible. This review highlights general principles and the features of the combinatorial approaches using various libraries to implement desired phenotypes for strain improvement. In addition, recent applications that harnessed the combinatorial approaches to produce biofuels and biochemicals will be discussed.

Keywords
Combinatorial library; Inverse metabolic engineering; Metabolic circuit; Biofuel; Biochemical
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Combinatorial genetic perturbation to refine metabolic circuits for producing biofuels and biochemicals
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Publisher
Database: Elsevier - ScienceDirect
Journal: Biotechnology Advances - Volume 31, Issue 6, 1 November 2013, Pages 976–985
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
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