TY - GEN
T1 - Frequency domain analysis of small non-coding RNAs shows summing junction-like behaviour
AU - Steel, Harrison
AU - Harris, Andreas W.K.
AU - Hancock, Edward J.
AU - Kelly, Ciaran L.
AU - Papachristodoulou, Antonis
N1 - Funding Information:
†: Authors with the Department of Engineering Science, University of Oxford, Oxford, OX1 3PJ, UK. e-mail: ({harrison.steel, andreas.harris, antonis}@eng.ox.ac.uk). ‡: Author with The School of Mathematics and Statistics & The Charles Perkins Centre, University of Sydney, NSW, 2006, Australia. e-mail: [email protected] ★: Author with the Centre for Synthetic Biology and Innovation, Imperial College London, London, SW7 2AZ, UK. email: [email protected]. H. Steel is supported by the General Sir John Monash Foundation. A. Harris is supported by the Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Centre in Systems Biology, University of Oxford. A. Papachristodoulou is supported in part by EPSRC project EP/M002454/1.
PY - 2017/12/15
Y1 - 2017/12/15
N2 - Small non-coding RNAs (sRNA) are a key bacterial regulatory mechanism that has yet to be fully exploited in synthetic gene regulatory networks. In this paper a linear design methodology for gene regulatory networks presented previously is extended for application to sRNAs. Standard models of both sRNA inhibition and activation are presented, linearised and transformed into the frequency domain. We demonstrate how these mechanisms can emulate subtraction and minimum comparator functions in specific parameter regimes. Finally, the design of a genetic feedback circuit is included, illustrating that sRNAs can be used to improve the performance of a range of synthetic biological systems.
AB - Small non-coding RNAs (sRNA) are a key bacterial regulatory mechanism that has yet to be fully exploited in synthetic gene regulatory networks. In this paper a linear design methodology for gene regulatory networks presented previously is extended for application to sRNAs. Standard models of both sRNA inhibition and activation are presented, linearised and transformed into the frequency domain. We demonstrate how these mechanisms can emulate subtraction and minimum comparator functions in specific parameter regimes. Finally, the design of a genetic feedback circuit is included, illustrating that sRNAs can be used to improve the performance of a range of synthetic biological systems.
KW - Proteins
KW - Junctions
KW - Degradation
KW - Steady-state
KW - RNA
KW - Mathematical model
UR - http://www.scopus.com/inward/record.url?scp=85046133579&partnerID=8YFLogxK
U2 - 10.1109/CDC.2017.8264448
DO - 10.1109/CDC.2017.8264448
M3 - Conference contribution
AN - SCOPUS:85046133579
SN - 9781509028740
T3 - 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
SP - 5328
EP - 5333
BT - 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
CY - Piscataway, NJ
T2 - 56th IEEE Annual Conference on Decision and Control, CDC 2017
Y2 - 12 December 2017 through 15 December 2017
ER -