RBS and Promoter Strengths Determine the Cell-Growth-Dependent Protein Mass Fractions and Their Optimal Synthesis Rates.
RBS strength
burden
gene expression
growth rate
promoter strength
protein synthesis mass fractions
resources allocation
Journal
ACS synthetic biology
ISSN: 2161-5063
Titre abrégé: ACS Synth Biol
Pays: United States
ID NLM: 101575075
Informations de publication
Date de publication:
17 12 2021
17 12 2021
Historique:
pubmed:
13
11
2021
medline:
1
4
2022
entrez:
12
11
2021
Statut:
ppublish
Résumé
Models of gene expression considering host-circuit interactions are relevant for understanding both the strategies and associated trade-offs that cell endogenous genes have evolved and for the efficient design of heterologous protein expression systems and synthetic genetic circuits. Here, we consider a small-size model of gene expression dynamics in bacterial cells accounting for host-circuit interactions due to limited cellular resources. We define the cellular resources recruitment strength as a key functional coefficient that explains the distribution of resources among the host and the genes of interest and the relationship between the usage of resources and cell growth. This functional coefficient explicitly takes into account lab-accessible gene expression characteristics, such as promoter and ribosome binding site (RBS) strengths, capturing their interplay with the growth-dependent flux of available free cell resources. Despite its simplicity, the model captures the differential role of promoter and RBS strengths in the distribution of protein mass fractions as a function of growth rate and the optimal protein synthesis rate with remarkable fit to the experimental data from the literature for
Identifiants
pubmed: 34767708
doi: 10.1021/acssynbio.1c00131
pmc: PMC8689641
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3290-3303Références
Nat Microbiol. 2020 Aug;5(8):995-1001
pubmed: 32424336
ACS Synth Biol. 2019 Jun 21;8(6):1231-1240
pubmed: 31181895
ACS Synth Biol. 2013 Aug 16;2(8):431-41
pubmed: 23654274
Curr Biol. 2017 May 8;27(9):1278-1287
pubmed: 28416114
Mol Syst Biol. 2019 May 3;15(5):e8719
pubmed: 31053575
ACS Synth Biol. 2016 Jul 15;5(7):710-20
pubmed: 27112032
Life Sci Soc Policy. 2018 Aug 12;14(1):18
pubmed: 30099657
Nat Methods. 2015 May;12(5):415-8
pubmed: 25849635
Nucleic Acids Res. 2010 Jan;38(Database issue):D750-3
pubmed: 19854939
BMC Bioinformatics. 2014 May 10;15:136
pubmed: 24885957
Nucleic Acids Res. 2016 Jan 8;44(1):496-507
pubmed: 26656950
Nat Commun. 2019 Jan 8;10(1):68
pubmed: 30622246
Nat Commun. 2017 Jan 19;8:14123
pubmed: 28102224
ACS Synth Biol. 2017 Jul 21;6(7):1263-1272
pubmed: 28350160
Elife. 2017 Aug 31;6:
pubmed: 28857745
Biophys J. 2018 Feb 6;114(3):737-746
pubmed: 29414718
PLoS One. 2014 Oct 06;9(10):e109105
pubmed: 25286161
Nat Commun. 2020 Sep 15;11(1):4641
pubmed: 32934213
Trends Biochem Sci. 2020 Aug;45(8):681-692
pubmed: 32448596
Proc Natl Acad Sci U S A. 2015 Mar 3;112(9):E1038-47
pubmed: 25695966
J R Soc Interface. 2019 May 31;16(154):20180887
pubmed: 31113334
Front Microbiol. 2014 Apr 17;5:172
pubmed: 24860555
FEBS J. 2015 May;282(10):2029-44
pubmed: 25754869
Science. 2020 Jul 24;369(6502):
pubmed: 32703847
Nucleic Acids Res. 2000 Jan 1;28(1):27-30
pubmed: 10592173
Biotechnol J. 2012 Jul;7(7):856-66
pubmed: 22649052
EcoSal Plus. 2008 Sep;3(1):
pubmed: 26443740
Science. 2010 Nov 19;330(6007):1099-102
pubmed: 21097934
J R Soc Interface. 2013 Jan 6;10(78):20120671
pubmed: 23054953