PID and State Feedback Controllers Using DNA Strand Displacement Reactions

被引:38
作者
Paulino, Nuno M. G. [1 ]
Foo, Mathias [2 ]
Kim, Jongmin [3 ]
Bates, Declan G. [1 ]
机构
[1] Univ Warwick, Warwick Integrat Synthet Biol Ctr, Sch Engn, Coventry CV4 7AL, W Midlands, England
[2] Coventry Univ, Sch Mech Aerosp & Automot Engn, Coventry CV1 5FB, W Midlands, England
[3] Pohang Univ Sci & Technol, Dept Integrat Biosci & Biotechnol, Pohang 37673, South Korea
来源
IEEE CONTROL SYSTEMS LETTERS | 2019年 / 3卷 / 04期
基金
英国生物技术与生命科学研究理事会; 英国工程与自然科学研究理事会;
关键词
Biomolecular systems; PID control; control applications; MAMMALIAN-CELLS; DESIGN;
D O I
10.1109/LCSYS.2019.2918977
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Nucleic acid-based chemistry is a strong candidate framework for the construction of future synthetic biomolecular control circuits. Previous work has demonstrated the capacity of circuits based on DNA strand displacement (DSD) reactions to implement digital and analogue signal processing in vivo, including in mammalian cells. To date, however, feedback control system designs attempted within this framework have been restricted to extremely simple proportional or proportional-integral controller architectures. In this letter, we significantly extend the potential complexity of such controllers by showing how time-delays, numerical differentiation (to allow PID control), and state feedback may be implemented via chemical reaction network-based designs. Our controllers are implemented and tested using VisualDSD, a rapid-prototyping tool that allows precise analysis of computational devices implemented using nucleic acids, via both deterministic and stochastic simulations of the DSD reactions.
引用
收藏
页码:805 / 810
页数:6
相关论文
共 21 条
[1]  
Astrom K.J., 2021, Feedback Systems: An Introduction for Scientists and Engineers
[2]   Design of a Synthetic Integral Feedback Circuit: Dynamic Analysis and DNA Implementation [J].
Briat, Corentin ;
Zechner, Christoph ;
Khammash, Mustafa .
ACS SYNTHETIC BIOLOGY, 2016, 5 (10) :1108-1116
[3]   Computing with chemical reaction networks: a tutorial [J].
Brijder, Robert .
NATURAL COMPUTING, 2019, 18 (01) :119-137
[4]   Computing Algebraic Functions with Biochemical Reaction Networks [J].
Buisman, H. J. ;
ten Eikelder, H. M. M. ;
Hilbers, P. A. J. ;
Liekens, A. M. L. .
ARTIFICIAL LIFE, 2009, 15 (01) :5-19
[5]   Nucleic Acid Strand Displacement with Synthetic mRNA Inputs in Living Mammalian Cells [J].
Chatterjee, Gourab ;
Chen, Yuan-Jyue ;
Seelig, Georg .
ACS SYNTHETIC BIOLOGY, 2018, 7 (12) :2737-2741
[6]  
Chevalier M, 2018, DESIGN ANAL PROPORTI, DOI [10.1101/303545, DOI 10.1101/303545]
[7]   Synthesizing Configurable Biochemical Implementation of Linear Systems from Their Transfer Function Specifications [J].
Chiu, Tai-Yin ;
Chiang, Hui-Ju K. ;
Huang, Ruei-Yang ;
Jiang, Jie-Hong R. ;
Fages, Francois .
PLOS ONE, 2015, 10 (09)
[8]   Design of an embedded inverse-feedforward biomolecular tracking controller for enzymatic reaction processes [J].
Foo, Mathias ;
Kim, Jongrae ;
Sawlekar, Rucha ;
Bates, Declan G. .
COMPUTERS & CHEMICAL ENGINEERING, 2017, 99 :145-157
[9]  
Groves B, 2016, NAT NANOTECHNOL, V11, P287, DOI [10.1038/nnano.2015.278, 10.1038/NNANO.2015.278]
[10]   Signal differentiation with genetic networks [J].
Halter, Wolfgang ;
Tuza, Zoltan A. ;
Allgoewer, Frank .
IFAC PAPERSONLINE, 2017, 50 (01) :10938-10943