Automated Design of Programmable Enzyme-Driven DNA Circuits
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- 作者：Hendrik W. H. van Roekel ; Lenny H. H. Meijer ; Saeed Masroor ; Zandra C. F茅lix Garza ; Andr茅 Est茅vez-Torres ; Yannick Rondelez ; Antonios Zagaris ; Mark A. Peletier ; Peter A. J. Hilbers ; Tom F. A. de Greef
- 刊名：ACS Synthetic Biology
- 出版年：2015
- 出版时间：June 19, 2015
- 年：2015
- 卷：4
- 期：6
- 页码：735-745
- 全文大小：568K
- ISSN：2161-5063
文摘
Molecular programming allows for the bottom-up engineering of biochemical reaction networks in a controlled in vitro setting. These engineered biochemical reaction networks yield important insight in the design principles of biological systems and can potentially enrich molecular diagnostic systems. The DNA polymerase鈥搉ickase鈥揺xonuclease (PEN) toolbox has recently been used to program oscillatory and bistable biochemical networks using a minimal number of components. Previous work has reported the automatic construction of in silico descriptions of biochemical networks derived from the PEN toolbox, paving the way for generating networks of arbitrary size and complexity in vitro. Here, we report an automated approach that further bridges the gap between an in silico description and in vitro realization. A biochemical network of arbitrary complexity can be globally screened for parameter values that display the desired function and combining this approach with robustness analysis further increases the chance of successful in vitro implementation. Moreover, we present an automated design procedure for generating optimal DNA sequences, exhibiting key characteristics deduced from the in silico analysis. Our in silico method has been tested on a previously reported network, the Oligator, and has also been applied to the design of a reaction network capable of displaying adaptation in one of its components. Finally, we experimentally characterize unproductive sequestration of the exonuclease to phosphorothioate protected ssDNA strands. The strong nonlinearities in the degradation of active components caused by this unintended cross-coupling are shown computationally to have a positive effect on adaptation quality.