globalchange  > 气候变化与战略
DOI: 10.1073/pnas.1715806115
论文题名:
Rapid acquisition and model-based analysis of cell-free transcription–translation reactions from nonmodel bacteria
作者: Moore S.J.; MacDonald J.T.; Wienecke S.; Ishwarbhai A.; Tsipa A.; Aw R.; Kylilis N.; Bell D.J.; McClymont D.W.; Jensen K.; Polizzi K.M.; Biedendieck R.; Freemont P.S.
刊名: Proceedings of the National Academy of Sciences of the United States of America
ISSN: 0027-8424
出版年: 2018
卷: 115, 期:19
起始页码: 4340
结束页码: 4349
语种: 英语
英文关键词: Automation ; Bacillus ; Cell-free synthetic biology ; In vitro transcription–translation ; Modeling
Scopus关键词: amino acid ; nucleotide ; RNA polymerase ; Article ; Bacillus megaterium ; bacterial strain ; Bayes theorem ; binding site ; cell free transcription translation ; controlled study ; genetic transcription and translation ; in vitro study ; in vivo study ; liquid chromatography-mass spectrometry ; mathematical model ; nonhuman ; plasmid ; priority journal ; promoter region ; proteomics ; ribosome ; robotics ; biological model ; cell free system ; chemistry ; genetic transcription ; genetics ; metabolism ; protein synthesis ; Bacillus megaterium ; Cell-Free System ; Models, Biological ; Protein Biosynthesis ; Transcription, Genetic
英文摘要: Native cell-free transcription–translation systems offer a rapid route to characterize the regulatory elements (promoters, transcription factors) for gene expression from nonmodel microbial hosts, which can be difficult to assess through traditional in vivo approaches. One such host, Bacillus megaterium, is a giant Gram-positive bacterium with potential biotechnology applications, although many of its regulatory elements remain uncharacterized. Here, we have developed a rapid automated platform for measuring and modeling in vitro cell-free reactions and have applied this to B. megaterium to quantify a range of ribosome binding site variants and previously uncharacterized endogenous constitutive and inducible promoters. To provide quantitative models for cell-free systems, we have also applied a Bayesian approach to infer ordinary differential equation model parameters by simultaneously using time-course data from multiple experimental conditions. Using this modeling framework, we were able to infer previously unknown transcription factor binding affinities and quantify the sharing of cell-free transcription–translation resources (energy, ribosomes, RNA polymerases, nucleotides, and amino acids) using a promoter competition experiment. This allows insights into resource limiting-factors in batch cell-free synthesis mode. Our combined automated and modeling platform allows for the rapid acquisition and model-based analysis of cell-free transcription–translation data from uncharacterized microbial cell hosts, as well as resource competition within cell-free systems, which potentially can be applied to a range of cell-free synthetic biology and biotechnology applications. © 2018 National Academy of Sciences. All rights reserved.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/163720
Appears in Collections:气候变化与战略

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作者单位: Moore, S.J., Centre for Synthetic Biology and Innovation, Imperial College London, London, SW7 2AZ, United Kingdom, Section for Structural Biology, Department of Medicine, Imperial College London, London, SW7 2AZ, United Kingdom; MacDonald, J.T., Centre for Synthetic Biology and Innovation, Imperial College London, London, SW7 2AZ, United Kingdom, Section for Structural Biology, Department of Medicine, Imperial College London, London, SW7 2AZ, United Kingdom; Wienecke, S., Braunschweig Integrated Centre of Systems Biology, Institute of Microbiology, Technische Universität Braunschweig, Braunschweig, 38106, Germany; Ishwarbhai, A., London DNA Foundry, Imperial College London, London, SW7 2AZ, United Kingdom, Department of Bioengineering, Imperial College London, London, SW7 2AZ, United Kingdom; Tsipa, A., London DNA Foundry, Imperial College London, London, SW7 2AZ, United Kingdom, Department of Bioengineering, Imperial College London, London, SW7 2AZ, United Kingdom; Aw, R., Centre for Synthetic Biology and Innovation, Imperial College London, London, SW7 2AZ, United Kingdom, Department of Life Sciences, Imperial College London, London, SW7 2AZ, United Kingdom; Kylilis, N., Centre for Synthetic Biology and Innovation, Imperial College London, London, SW7 2AZ, United Kingdom, Section for Structural Biology, Department of Medicine, Imperial College London, London, SW7 2AZ, United Kingdom; Bell, D.J., Section for Structural Biology, Department of Medicine, Imperial College London, London, SW7 2AZ, United Kingdom, London DNA Foundry, Imperial College London, London, SW7 2AZ, United Kingdom; McClymont, D.W., Section for Structural Biology, Department of Medicine, Imperial College London, London, SW7 2AZ, United Kingdom, London DNA Foundry, Imperial College London, London, SW7 2AZ, United Kingdom; Jensen, K., Centre for Synthetic Biology and Innovation, Imperial College London, London, SW7 2AZ, United Kingdom, Section for Structural Biology, Department of Medicine, Imperial College London, London, SW7 2AZ, United Kingdom, London DNA Foundry, Imperial College London, London, SW7 2AZ, United Kingdom; Polizzi, K.M., Centre for Synthetic Biology and Innovation, Imperial College London, London, SW7 2AZ, United Kingdom, Department of Life Sciences, Imperial College London, London, SW7 2AZ, United Kingdom; Biedendieck, R., Braunschweig Integrated Centre of Systems Biology, Institute of Microbiology, Technische Universität Braunschweig, Braunschweig, 38106, Germany; Freemont, P.S., Centre for Synthetic Biology and Innovation, Imperial College London, London, SW7 2AZ, United Kingdom, Section for Structural Biology, Department of Medicine, Imperial College London, London, SW7 2AZ, United Kingdom, London DNA Foundry, Imperial College London, London, SW7 2AZ, United Kingdom

Recommended Citation:
Moore S.J.,MacDonald J.T.,Wienecke S.,et al. Rapid acquisition and model-based analysis of cell-free transcription–translation reactions from nonmodel bacteria[J]. Proceedings of the National Academy of Sciences of the United States of America,2018-01-01,115(19)
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