Synthetic biology enables programmable cell-based biosensors

Maggie Hicks; Till T. Bachmann; Baojun Wang

doi:10.1002/cphc.201901191

Synthetic biology enables programmable cell-based biosensors

Maggie Hicks, Till T. Bachmann, Baojun Wang^*

^*Corresponding author for this work

Research output: Contribution to journal › Comment/debate › peer-review

Abstract

The front cover artwork is provided by the group of Dr. Baojun Wang (The University of Edinburgh). The image shows an engineered bacterial cell containing a genetic amplifier circuit which transforms a weak input signal into a larger easily detectable output signal. The electronics symbols used to illustrate the genetic circuit highlight the programmability of the circuit components enabled by state-of-the-art synthetic biology tools. Read the full text of the Review at 10.1002/cphc.201900739.

Original language	English
Pages (from-to)	132-144
Journal	ChemPhysChem
Volume	21
Issue number	2
DOIs	https://doi.org/10.1002/cphc.201901191
Publication status	Published - 20 Jan 2020

Keywords

Cell-based biosensor
genetic circuits
rational approaches
response curve
synthetic biology

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10.1002/cphc.201901191Licence: Creative Commons: Attribution (CC-BY)

WangCPC2020SyntheticBiologyEnablesProgrammableCellBasedBiosensorsFinal published version, 2.37 MBLicence: Creative Commons: Attribution (CC-BY)

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1 Active
3 Finished

engineering split inteins as scalable tools for synthetic biology
Wang, B.
MRC
1/05/19 → 30/04/23
Project: Research
Low cost paper-based biosensors for point-of-care nucleic acid diagnostics of pathogens
Wang, B.
UK-based charities
31/10/16 → 30/04/18
Project: Research
Programmable single-cell biocomputers with scalable signal processing capacity
Wang, B.
UK-based charities
8/08/16 → 31/12/19
Project: Research

Cite this

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title = "Synthetic biology enables programmable cell-based biosensors",

abstract = "The front cover artwork is provided by the group of Dr. Baojun Wang (The University of Edinburgh). The image shows an engineered bacterial cell containing a genetic amplifier circuit which transforms a weak input signal into a larger easily detectable output signal. The electronics symbols used to illustrate the genetic circuit highlight the programmability of the circuit components enabled by state-of-the-art synthetic biology tools. Read the full text of the Review at 10.1002/cphc.201900739.",

keywords = "Cell-based biosensor, genetic circuits, rational approaches, response curve, synthetic biology",

author = "Maggie Hicks and Bachmann, {Till T.} and Baojun Wang",

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AU - Bachmann, Till T.

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N2 - The front cover artwork is provided by the group of Dr. Baojun Wang (The University of Edinburgh). The image shows an engineered bacterial cell containing a genetic amplifier circuit which transforms a weak input signal into a larger easily detectable output signal. The electronics symbols used to illustrate the genetic circuit highlight the programmability of the circuit components enabled by state-of-the-art synthetic biology tools. Read the full text of the Review at 10.1002/cphc.201900739.

AB - The front cover artwork is provided by the group of Dr. Baojun Wang (The University of Edinburgh). The image shows an engineered bacterial cell containing a genetic amplifier circuit which transforms a weak input signal into a larger easily detectable output signal. The electronics symbols used to illustrate the genetic circuit highlight the programmability of the circuit components enabled by state-of-the-art synthetic biology tools. Read the full text of the Review at 10.1002/cphc.201900739.

KW - Cell-based biosensor

KW - genetic circuits

KW - rational approaches

KW - response curve

KW - synthetic biology

U2 - 10.1002/cphc.201901191

DO - 10.1002/cphc.201901191

M3 - Comment/debate

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EP - 144

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Synthetic biology enables programmable cell-based biosensors

Abstract

Keywords

Access to Document

engineering split inteins as scalable tools for synthetic biology

Low cost paper-based biosensors for point-of-care nucleic acid diagnostics of pathogens

Programmable single-cell biocomputers with scalable signal processing capacity

Cite this