A comprehensive review on integration of cellular metabolic engineering and cell-free systems for microbial platforms

被引:0
|
作者
Das, Arunangshu [1 ,2 ,3 ]
Verma, Anita [4 ]
Hazarika, Naba [3 ,5 ]
机构
[1] Jawaharlal Nehru Univ, Sch Environm Sci, New Delhi 110067, India
[2] Jawaharlal Nehru Univ, Sch Biotechnol, New Delhi 110067, India
[3] Indian Inst Technol Delhi, Ctr Atmospher Sci, New Delhi 110016, India
[4] Jawaharlal Nehru Univ, Sch Life Sci, New Delhi 110067, India
[5] Mizoram Univ, Sch Earth Sci & Nat Resources Management, Dept Environm Sci, Aizawl 796004, Mizoran, India
关键词
Metabolic Engineering; Cell-free Systems; Ionic liquids; Organometallics; Multiplexing; SOLUBLE IRIDIUM COMPLEX; ESCHERICHIA-COLI; TRANSCRIPTIONAL REGULATION; SYNTHETIC BIOLOGY; PROTEIN; NETWORK; DECARBOXYLASE; REGENERATION; EXPRESSION; SUBSTRATE;
D O I
10.1016/j.procbio.2024.12.010
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
In response to the demand for environment friendly synthesis of chemical feedstocks, two disciplines have emerged: cellular metabolic engineering (CME) and cell-free metabolic engineering (CFME). Cell free systems largely replicate cellular pathways in vitro to bypass the need of live cells to achieve greater control and process simplicity. However, not all cellular biochemical aspects can be replicated in vitro, as some are not hardwired but crucial modulators of biochemical processes. These include metabolic states defined by ensemble of small molecules that influence proteostasis, catalytic activity of enzymes, and redox power, influencing cellular anabolic and catabolic decisions. Despite the advancement of molecular biology techniques engineering such control systems remains largely a challenge for cellular metabolic engineering. This review thoroughly examines these limitations in both fields and explores the potential for implementing non-hardwired control systems in cell-free metabolic engineering, either independently or in combination with cellular metabolic engineering. Further, the integration of chemistry and machine learning models is considered, with a focus on how their combined strengths can be leveraged to develop novel synthetic schemes.
引用
收藏
页码:222 / 236
页数:15
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