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

Metab Eng. 2012 May;14(3):212-22. doi: 10.1016/j.ymben.2011.09.004. Epub 2011 Sep 18.


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.

Publication types

  • Review

MeSH terms

  • Biosensing Techniques / methods*
  • Biosensing Techniques / trends
  • Genes, Reporter / physiology
  • Genetic Markers / physiology
  • Metabolic Engineering / methods*
  • Metabolic Engineering / trends
  • Organisms, Genetically Modified / genetics
  • Organisms, Genetically Modified / metabolism
  • Synthetic Biology / methods*
  • Synthetic Biology / trends


  • Genetic Markers