Load Capacity Improvements in Nucleic Acid Based Systems Using Partially Open Feedback Control

• Published in: ACS synthetic biology Vol. 3; no. 8; pp. 617 - 626
• Main Authors: Kulkarni, Vishwesh, Kharisov, Evgeny, Hovakimyan, Naira, Kim, Jongmin
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 15.08.2014
• Subjects: Computer Simulation; Computers, Molecular; Enzymes - chemistry; Enzymes - metabolism; Feedback, Physiological; Genes, Synthetic; Models, Genetic; Models, Theoretical; Nucleic Acids; Synthetic Biology - methods; feedback; load capacity improvement; nucleic acid circuits; adaptive control; DNA strand displacement; genelet; molecular programming
• ISSN: 2161-5063; 2161-5063
• DOI: 10.1021/sb5000675

Synthetic biology is facilitating novel methods and components to build in vivo and in vitro circuits to better understand and re-engineer biological networks. Recently, Kim and Winfree have synthesized a remarkably elegant network of transcriptional oscillators in vitro using a modular architecture of synthetic gene analogues and a few enzymes that, in turn, could be used to drive a variety of downstream circuits and nanodevices. However, these oscillators are sensitive to initial conditions and downstream load processes. Furthermore, the oscillations are not sustained since the inherently closed design suffers from enzyme deactivation, NTP fuel exhaustion, and waste product build up. In this paper, we show that a partially open architecture in which an L 1 adaptive controller, implemented inside an in silico computer that resides outside the wet-lab apparatus, can ensure sustained tunable oscillations in two specific designs of the Kim–Winfree oscillator networks. We consider two broad cases of operation: (1) the oscillator network operating in isolation and (2) the oscillator network driving a DNA tweezer subject to a variable load. In both scenarios, our simulation results show a significant improvement in the tunability and robustness of these oscillator networks. Our approach can be easily adopted to improve the loading capacity of a wide range of synthetic biological devices.