Biotechnology Applications of Cell-Free Expression Systems

被引:31
|
作者
Brookwell, August [1 ]
Oza, Javin P. [1 ]
Caschera, Filippo [2 ]
机构
[1] Calif Polytech State Univ San Luis Obispo, Coll Sci & Math, Dept Chem & Biochem, San Luis Obispo, CA 93407 USA
[2] Nucl Nucle Ltd, Cambridge CB4 0GD, England
来源
LIFE-BASEL | 2021年 / 11卷 / 12期
关键词
cell-free expression systems; cell-free protein synthesis; biotechnology applications; synthetic biology; metabolic engineering; prototyping; biomanufacturing; machine learning; FREE PROTEIN-SYNTHESIS; TRANSCRIPTION-TRANSLATION SYSTEM; FREE SYNTHETIC BIOLOGY; IN-VITRO; COLI; PROTOTYPE; EXTRACTS; THERAPY;
D O I
10.3390/life11121367
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Cell-free systems are a rapidly expanding platform technology with an important role in the engineering of biological systems. The key advantages that drive their broad adoption are increased efficiency, versatility, and low cost compared to in vivo systems. Traditionally, in vivo platforms have been used to synthesize novel and industrially relevant proteins and serve as a testbed for prototyping numerous biotechnologies such as genetic circuits and biosensors. Although in vivo platforms currently have many applications within biotechnology, they are hindered by time-constraining growth cycles, homeostatic considerations, and limited adaptability in production. Conversely, cell-free platforms are not hindered by constraints for supporting life and are therefore highly adaptable to a broad range of production and testing schemes. The advantages of cell-free platforms are being leveraged more commonly by the biotechnology community, and cell-free applications are expected to grow exponentially in the next decade. In this study, new and emerging applications of cell-free platforms, with a specific focus on cell-free protein synthesis (CFPS), will be examined. The current and near-future role of CFPS within metabolic engineering, prototyping, and biomanufacturing will be investigated as well as how the integration of machine learning is beneficial to these applications.
引用
收藏
页数:23
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