4.6 Article

Biotechnology Applications of Cell-Free Expression Systems

Journal

LIFE-BASEL
Volume 11, Issue 12, Pages -

Publisher

MDPI
DOI: 10.3390/life11121367

Keywords

cell-free expression systems; cell-free protein synthesis; biotechnology applications; synthetic biology; metabolic engineering; prototyping; biomanufacturing; machine learning

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Cell-free systems are rapidly growing in popularity in the field of biological systems engineering due to their increased efficiency, versatility, and cost-effectiveness compared to in vivo systems. The traditional in vivo platforms, constrained by growth cycles, homeostasis, and limited adaptability in production, are gradually being replaced by the more adaptable and versatile cell-free platforms.
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.

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