期刊
ACS SYNTHETIC BIOLOGY
卷 1, 期 8, 页码 299-316出版社
AMER CHEMICAL SOC
DOI: 10.1021/sb300018h
关键词
in vitro; synthetic biology; systems biology; reductionism; transcriptional circuits
资金
- National Science Foundation awards [0103002, 0608889, 0832824]
- Human Frontiers Science Program award [RGY0074/2006-C]
- Caltech Center for Biological Circuit Design
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [832824] Funding Source: National Science Foundation
- Directorate For Engineering
- Div Of Chem, Bioeng, Env, & Transp Sys [0103002] Funding Source: National Science Foundation
- Div Of Chem, Bioeng, Env, & Transp Sys
- Directorate For Engineering [0608889] Funding Source: National Science Foundation
An overarching goal of synthetic and systems biology is to engineer and understand complex biochemical systems by rationally designing and analyzing their basic component interactions. Practically, the extent to which such reductionist approaches can be applied is unclear especially as the complexity of the system increases. Toward gradually increasing the complexity of systematically engineered systems, programmable synthetic circuits operating in cell-free in vitro environments offer a valuable testing ground for principles for the design, characterization, and analysis of complex biochemical systems. Here we illustrate this approach using in vitro transcriptional circuits (genelets) while developing an activatable transcriptional switch motif and configuring it as a bistable autoregulatory circuit, using just four synthetic DNA strands and three essential enzymes, bacteriophage T7 RNA polymerase, Escherichia coli ribonuclease H, and ribonuclease Fulfilling the promise of predictable system design, the thermodynamic and kinetic constraints prescribed at the sequence level were enough to experimentally demonstrate intended bistable dynamics for the synthetic autoregulatory switch. A simple mathematical model was constructed based on the mechanistic understanding of elementary reactions, and a Monte Carlo Bayesian inference approach was employed to find parameter sets compatible with a training set of experimental results; this ensemble of parameter sets was then used to predict a test set of additional experiments with reasonable agreement and to provide a rigorous basis for confidence in the mechanistic model. Our work demonstrates that programmable in vitro biochemical circuits can serve as a testing ground for evaluating methods for the design and analysis of more complex biochemical systems such as living cells.
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