Engineering Robust Production Microbes for Large-Scale Cultivation. Issue 6 (June 2019)

• Record Type: Journal Article
• Authors: Wehrs, Maren; Tanjore, Deepti; Eng, Thomas; Lievense, Jeff; Pray, Todd R.; Mukhopadhyay, Aindrila
• Is Part Of: Trends in microbiology. Volume 27:Issue 6(2019)
• Page Start: 524
• Page End: 537
• Publication Date: 2019-06
• Subjects: scale-up -- bioreactor -- mixing -- strain selection -- bioproduct -- biofuel -- microbial hosts -- heterogeneity; Microbiology -- Periodicals; Infection -- Periodicals; Virulence (Microbiology) -- Periodicals; Infection -- Periodicals; Microbiology -- Periodicals; Virulence -- Periodicals; Microbiologie -- Périodiques; Infection -- Périodiques; Virulence (Microbiologie) -- Périodiques; Infection; Microbiology; Virulence (Microbiology); 579
• DOI: 10.1016/j.tim.2019.01.006
• Languages: English
• ISSNs: 0966-842X

Abstract

Abstract : Systems biology and synthetic biology are increasingly used to examine and modulate complex biological systems. As such, many issues arising during scaling-up microbial production processes can be addressed using these approaches. We review differences between laboratory-scale cultures and larger-scale processes to provide a perspective on those strain characteristics that are especially important during scaling. Systems biology has been used to examine a range of microbial systems for their response in bioreactors to fluctuations in nutrients, dissolved gases, and other stresses. Synthetic biology has been used both to assess and modulate strain response, and to engineer strains to improve production. We discuss these approaches and tools in the context of their use in engineering robust microbes for applications in large-scale production. Highlights: Strain engineering in the laboratory often does not consider process requirements in larger-scale bioreactors. Systems and synthetic biology can be applied to design microbial strains that allow reliable and robust production on a large scale. Commercial microbial platforms should be selected and developed based on their relevance to final process goals.