Dynamical model fitting to a synthetic positive feedback circuit in
E. coli
biology computing
circuit feedback
complex dynamics
differential equations
dynamical model fitting
feedback
genetics
modular genetic components
ordinary differential equation model
phage shock promoter
positive feedback circuit
synthetic biology
synthetic positive feedback circuit
time series
time‐series data
Journal
Engineering biology
ISSN: 2398-6182
Titre abrégé: Eng Biol
Pays: United States
ID NLM: 9918539388906676
Informations de publication
Date de publication:
Jun 2020
Jun 2020
Historique:
received:
21
04
2020
revised:
22
05
2020
accepted:
22
05
2020
entrez:
27
3
2023
pubmed:
23
6
2020
medline:
23
6
2020
Statut:
epublish
Résumé
Applying the principles of engineering to Synthetic Biology relies on the development of robust and modular genetic components, as well as underlying quantitative dynamical models that closely predict their behaviour. This study looks at a simple positive feedback circuit built by placing filamentous phage secretin pIV under a phage shock promoter. A single-equation ordinary differential equation model is developed to closely replicate the behaviour of the circuit, and its response to inhibition by TetR. A stepwise approach is employed to fit the model's parameters to time-series data for the circuit. This approach allows the dissection of the role of different parameters and leads to the identification of dependencies and redundancies between parameters. The developed genetic circuit and associated model may be used as a building block for larger circuits with more complex dynamics, which require tight quantitative control or tuning.
Identifiants
pubmed: 36970395
doi: 10.1049/enb.2020.0009
pii: ENB2BF00029
pmc: PMC9996705
doi:
Types de publication
Journal Article
Langues
eng
Pagination
25-31Informations de copyright
© 2020 The Institution of Engineering and Technology.
Références
Proc Natl Acad Sci U S A. 2018 Jun 26;115(26):6572-6577
pubmed: 29891706
Nat Genet. 2002 May;31(1):69-73
pubmed: 11967532
Sci Adv. 2020 Jun 10;6(24):eaba2728
pubmed: 32577520
ACS Synth Biol. 2019 Jul 19;8(7):1484-1497
pubmed: 31035759
Syst Synth Biol. 2007 Aug;1(3):119-28
pubmed: 19003446
ACS Synth Biol. 2017 Jul 21;6(7):1115-1119
pubmed: 27744689
Nat Commun. 2014 Sep 23;5:4905
pubmed: 25247316
Nature. 2005 Apr 28;434(7037):1130-4
pubmed: 15858574
Annu Rev Virol. 2018 Sep 29;5(1):453-476
pubmed: 30001182
BMC Syst Biol. 2015 Oct 29;9:74
pubmed: 26515482
Curr Opin Chem Biol. 2017 Oct;40:1-7
pubmed: 28463802
J R Soc Interface. 2009 Feb 6;6(31):187-202
pubmed: 19205079
J Biol Chem. 2004 Dec 31;279(53):55707-14
pubmed: 15485810
Nat Rev Genet. 2005 Jun;6(6):451-64
pubmed: 15883588
PLoS Comput Biol. 2007 Oct;3(10):1871-78
pubmed: 17922568
J Bacteriol. 1996 Apr;178(7):1936-45
pubmed: 8606168
Nature. 2000 Jan 20;403(6767):335-8
pubmed: 10659856
Proc Natl Acad Sci U S A. 1990 Feb;87(3):862-6
pubmed: 2105503
Nucleic Acids Res. 2015 Oct 15;43(18):9086-94
pubmed: 26384562
FASEB J. 1997 Sep;11(11):835-41
pubmed: 9285481
Integr Biol (Camb). 2018 Aug 1;10(8):474-482
pubmed: 30039143
Nature. 2008 Nov 27;456(7221):516-9
pubmed: 18971928
Nature. 2000 Jan 20;403(6767):339-42
pubmed: 10659857
Curr Issues Mol Biol. 2011;13(2):51-76
pubmed: 21502666
J Biol Chem. 2006 Nov 17;281(46):34997-5007
pubmed: 16973614
Nucleic Acids Res. 2004 Feb 04;32(2):842-7
pubmed: 14764926
Gene. 1997 Oct 1;198(1-2):99-103
pubmed: 9370269
Science. 2016 Apr 1;352(6281):aac7341
pubmed: 27034378
Nat Commun. 2018 Dec 21;9(1):5456
pubmed: 30575724
Front Bioeng Biotechnol. 2019 Aug 07;7:175
pubmed: 31448268
Annu Rev Microbiol. 2016 Sep 8;70:83-101
pubmed: 27297125
Cell. 2003 May 30;113(5):597-607
pubmed: 12787501
Nat Chem Biol. 2014 Mar;10(3):216-22
pubmed: 24487694
Curr Opin Cell Biol. 2002 Apr;14(2):140-8
pubmed: 11891111
Nat Commun. 2016 Dec 16;7:13858
pubmed: 27982027
Nature. 2016 May 5;533(7601):58-63
pubmed: 27120167