High-throughput screening for high-efficiency small-molecule biosynthesis
Systems metabolic engineering faces the formidable task of rewiring microbial metabolism to cost-effectively generate high-value molecules from a variety of inexpensive feedstocks for many different applications. Because these cellular systems are still too complex to model accurately, vast collecti...
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Published in: | Metabolic engineering Vol. 63; pp. 102 - 125 |
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Main Authors: | , , , , , , , , |
Format: | Journal Article |
Language: | English |
Published: |
Belgium
Elsevier Inc
01-01-2021
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Subjects: | |
Online Access: | Get full text |
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Summary: | Systems metabolic engineering faces the formidable task of rewiring microbial metabolism to cost-effectively generate high-value molecules from a variety of inexpensive feedstocks for many different applications. Because these cellular systems are still too complex to model accurately, vast collections of engineered organism variants must be systematically created and evaluated through an enormous trial-and-error process in order to identify a manufacturing-ready strain. The high-throughput screening of strains to optimize their scalable manufacturing potential requires execution of many carefully controlled, parallel, miniature fermentations, followed by high-precision analysis of the resulting complex mixtures. This review discusses strategies for the design of high-throughput, small-scale fermentation models to predict improved strain performance at large commercial scale. Established and promising approaches from industrial and academic groups are presented for both cell culture and analysis, with primary focus on microplate- and microfluidics-based screening systems.
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•High-throughput screening should be targeted to maximize strain scale-up potential.•Bioreactor control measures are discussed as context for model system design.•Sample extractions and assays are compared for throughput and data density.•Emergent tools for microfluidic screening of engineered libraries are presented. |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-3 content type line 23 ObjectType-Review-1 |
ISSN: | 1096-7176 1096-7184 |
DOI: | 10.1016/j.ymben.2020.09.004 |