Author
Listed:
- Haoqian Zhang
(Peking-Tsinghua Joint Centre for Life Sciences, Peking University
Peking University Team for the International Genetically Engineered Machine Competition (iGEM), Peking University
Centre for Quantitative Biology, Peking University)
- Min Lin
(Peking University Team for the International Genetically Engineered Machine Competition (iGEM), Peking University)
- Handuo Shi
(Peking University Team for the International Genetically Engineered Machine Competition (iGEM), Peking University)
- Weiyue Ji
(Peking University Team for the International Genetically Engineered Machine Competition (iGEM), Peking University)
- Longwen Huang
(Peking University Team for the International Genetically Engineered Machine Competition (iGEM), Peking University)
- Xiaomeng Zhang
(Peking University Team for the International Genetically Engineered Machine Competition (iGEM), Peking University
Centre for Quantitative Biology, Peking University)
- Shan Shen
(Peking University Team for the International Genetically Engineered Machine Competition (iGEM), Peking University)
- Rencheng Gao
(Peking University Team for the International Genetically Engineered Machine Competition (iGEM), Peking University)
- Shuke Wu
(Peking University Team for the International Genetically Engineered Machine Competition (iGEM), Peking University)
- Chengzhe Tian
(Peking University Team for the International Genetically Engineered Machine Competition (iGEM), Peking University)
- Zhenglin Yang
(Peking University Team for the International Genetically Engineered Machine Competition (iGEM), Peking University)
- Guosheng Zhang
(Peking University Team for the International Genetically Engineered Machine Competition (iGEM), Peking University)
- Siheng He
(Peking University Team for the International Genetically Engineered Machine Competition (iGEM), Peking University)
- Hao Wang
(Peking University Team for the International Genetically Engineered Machine Competition (iGEM), Peking University)
- Tiffany Saw
(Peking University Team for the International Genetically Engineered Machine Competition (iGEM), Peking University)
- Yiwei Chen
(Peking University Team for the International Genetically Engineered Machine Competition (iGEM), Peking University)
- Qi Ouyang
(Peking-Tsinghua Joint Centre for Life Sciences, Peking University
Centre for Quantitative Biology, Peking University
The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University)
Abstract
Synthetic genetic circuits are programmed in living cells to perform predetermined cellular functions. However, designing higher-order genetic circuits for sophisticated cellular activities remains a substantial challenge. Here we program a genetic circuit that executes Pavlovian-like conditioning, an archetypical sequential-logic function, in Escherichia coli. The circuit design is first specified by the subfunctions that are necessary for the single simultaneous conditioning, and is further genetically implemented using four function modules. During this process, quantitative analysis is applied to the optimization of the modules and fine-tuning of the interconnections. Analogous to classical Pavlovian conditioning, the resultant circuit enables the cells to respond to a certain stimulus only after a conditioning process. We show that, although the conditioning is digital in single cells, a dynamically progressive conditioning process emerges at the population level. This circuit, together with its rational design strategy, is a key step towards the implementation of more sophisticated cellular computing.
Suggested Citation
Haoqian Zhang & Min Lin & Handuo Shi & Weiyue Ji & Longwen Huang & Xiaomeng Zhang & Shan Shen & Rencheng Gao & Shuke Wu & Chengzhe Tian & Zhenglin Yang & Guosheng Zhang & Siheng He & Hao Wang & Tiffan, 2014.
"Programming a Pavlovian-like conditioning circuit in Escherichia coli,"
Nature Communications, Nature, vol. 5(1), pages 1-10, May.
Handle:
RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms4102
DOI: 10.1038/ncomms4102
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