Applications of genetically-encoded biosensors for the construction and control of biosynthetic pathways

被引:110
作者
Michener, Joshua K. [2 ]
Thodey, Kate [1 ]
Liang, Joe C. [3 ]
Smolke, Christina D. [1 ]
机构
[1] Stanford Univ, Dept Bioengn, Stanford, CA 94305 USA
[2] CALTECH, Dept Bioengn, Pasadena, CA 91125 USA
[3] CALTECH, Div Chem & Chem Engn, Pasadena, CA 91125 USA
基金
美国国家科学基金会;
关键词
Synthetic biology; Biosensors; Controller; HIGH-THROUGHPUT SELECTION; DIRECTED EVOLUTION; CHEMICAL COMPLEMENTATION; ESCHERICHIA-COLI; IN-VIVO; SYNTHETIC RIBOSWITCHES; SMALL MOLECULES; TRANSCRIPTIONAL ACTIVATOR; FLUORESCENT NANOSENSORS; NATURAL RIBOSWITCH;
D O I
10.1016/j.ymben.2011.09.004
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Cells are filled with biosensors, molecular systems that measure the state of the cell and respond by regulating host processes. In much the same way that an engineer would monitor a chemical reactor, the cell uses these sensors to monitor changing intracellular environments and produce consistent behavior despite the variable environment. While natural systems derive a clear benefit from pathway regulation, past research efforts in engineering cellular metabolism have focused on introducing new pathways and removing existing pathway regulation. Synthetic biology is a rapidly growing field that focuses on the development of new tools that support the design, construction, and optimization of biological systems. Recent advances have been made in the design of genetically-encoded biosensors and the application of this class of molecular tools for optimizing and regulating heterologous pathways. Biosensors to cellular metabolites can be taken directly from natural systems, engineered from natural sensors, or constructed entirely in vitro. When linked to reporters, such as antibiotic resistance markers, these metabolite sensors can be used to report on pathway productivity, allowing high-throughput screening for pathway optimization. Future directions will focus on the application of biosensors to introduce feedback control into metabolic pathways, providing dynamic control strategies to increase the efficient use of cellular resources and pathway reliability. (C) 2011 Elsevier Inc. All rights reserved.
引用
收藏
页码:212 / 222
页数:11
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