Internal Dynamics Parameters

When modeling any process, it is essential to use parameters which reflect reality, otherwise the model will not give accurate predictions. Thus we spend a considerable time researching, analysing and estimating parameters so that our model will reflect real life behaviour as accuratly as possible.

Parameter	Description	Value	Unit	Reference
	Rate of production of HSL from LuxI	0.45	1/s	[1]
HSL	Rate of diffusion of HSL in/out of the cell	0.4	1/s	[1]
IPTG	Rate of diffusion of IPTG in/out of the cell	0.014	1/s	[3]
pproduction	Number of plasmids	10		medium copy plasmid number; decided within the team
platch	Number of plasmids	10		medium copy plasmid number; decided within the team
plysis	Number of plasmids	10		medium copy plasmid number; decided within the team
panti-lysis	Number of plasmids	10		medium copy plasmid number; decided within the team
pQS	Number of plasmids	10		medium copy plasmid number; decided within the team
max_production	Maximal production rate of lux box promoter	0.44	pops	[1]
min_production	Minimal production rate of lux box promoter	0.013	pops	estimated
latch	Maximal production rate of latch promoter	0.28	pops	See Below
lysis	Maximal production rate of lysis promoter	0.0426	pops	See Below
anti-lysis	Maximal production rate of anti-lysis promoter	0.0066	pops	See Below
QS	Maximal production rate of QS promoter	0.018	pops	See Below
KLacI	Dissociation constant for LacI to LacO	700	m	See Explanation
KLacI-IPTG	Dissociation constant for IPTG to LacI	1200	m	See Explanation
KTetR	Dissociation constant for TetR to TetO	7000	m	See Explanation
KcI	Dissociation constant for cI to operon	7000	m	See Explanation
KP	Dissociation constant for P to lux box	700	m	See Explanation
nLacI	Hill coefficient	2		[10]
ncI	Hill coefficient	2		[11]
nTetR	Hill coefficient	3		[1]
nP	Hill coefficient	2		[1]
mRNA	Degradation of mRNA	0.00288	1/s	[2]
X	Degradation of X	0.00000802	1/s	[5]
Y	Degradation of Y	0.00000802	1/s	[5]
λ-CI	Degradation of lambda CI	0.002888	1/s	[5]
LacI	Degradation of LacI	0.001155	1/s	[8]
TetR	Degradation of TetR	0.00288811	1/s	tagged; half-life of 4 min
Holin	Degradation of Holin	0.0002	1/s	estmated; half-life of an hour
Endolysin	Degradation of Endolysin	0.0002	1/s	estimated; half-life of an hour
Antiholin	Degradation of Antiholin	0.0002	1/s	estimated; half-life of an hour
LuxI	Degradation of LuxI	0.002888	1/s	tagged; half-life of 4 min
LuxR	Degradation of LuxR	0.0002	1/s	[1]
HSL	Degradation of HSL	0.00016667	1	[8]; value for AHL
Protein	Translation rate of Protein	0.1	1/s	[2]
P	Rate of formation of the HSL-LuxI complex	0.00010	1/ms	[1]
-P	Rate of dissociation of the HSL-LuxI complex	0.003	1/s	[1]

LuxR promoter polymerase per second (PoPS) The following BioBrick F2620 defines the LuxR responsive promoter. This has been assessed as having 6.6 PoPS promoter activity, i.e. 6.6 RNA polymerisation events per second. However, this seemed unlikely; the most active promoter in E.coli has about 2 polymerisations per second. At the iGEM meeting in Edinburgh this year Dr Stansfield attended on behalf of the team were he met Barry Canton, the actual experimenter who deduced the 6.6 PoPS. When questioned he stated that he measured the PoPS value in PoPs per cell; the LuxR promoter/GFP construct was on a ‘15 copies per cell’ plasmid, thus there were 15 copies of the LuxR promoter. The actual PoPS = 6.6/15 = 0.44 PoPS i.e. consistent with known E.coli promoters.

An estimate for the strength of different E.coli promoters, including lambda P(L) is in the following paper:

Journal of Molecular Biology Volume 292, Issue 1, Activities of constitutive promoters in Escherichia coli1

S.-T Liang1, 2, M Bipatnath1, Y.-C Xu1, S.-L Chen2, P Dennis3

It states the PoPs values for several promoters as:

Pspc, 26 polymerase initiations per minute P(L), 17 polymerase initiations per minute Pbla 1.7 polymerase initiations per minute PRNAI, 17 polymerase initiations per minute

Using the P(L) strength of 17 polymerase initiations per minute we can easily calculate the PoPs value.

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References

[1] Goryachev, A.B., D.J. Toh and T. Lee. “System analysis of a quorum sensing network: Design constraints imposed by the functional requirements, network topology and kinetic constant.” BioSystems 2006: 83, 178-187.

[2] Alon, Uri. “An Introduction to Systems Biology Design Principles of Biological Circiuts.” London: Chapman & Hall/CRC, 2007.

[3] Kepes, A., 1960, “Etudes cinetiques sur la galactoside-permease D'Escherichia coli. Biochim.” Biophys. Acta 40, 70-84.

[4] Canton, B. and Anna Labno. “Part: BBa_F2620.” BioBrick Registry. 13th August 2009. <http://partsregistry.org/Part:BBa_F2620>

[5] Andersen JB, Sternberg C, Poulsen LK, Bjorn SP, Givskov M, Molin S. “New Unstable Variants of Green Fluorescent Protein for Studies of Transient Gene Expression in Bacteria.” Appl Environ Microbiol. 1998 Jun; 64(6):2240-6.

[6] Elowitz MB, Leibler S.; “A synthetic oscillatory network of transcriptional regulators.” Nature 2000 Jan; 403(6767):335-8.;

[7] BCCS-Bristol 2008. “Modelling Parameters” iGEM wiki. 13th August 2009. <https://2008.igem.org/Team:BCCS-Bristol/Modeling-Parameters>

[8] Subhayu Basu; “A synthetic multicellular system for programmed pattern formation.” Nature April 2005: 434, 1130-1134

[9] KULeuven 2008. “Cell Death”iGEM wiki. 13th August 2009. <https://2008.igem.org/Team:KULeuven/Model/CellDeath>

[10] ETHZ 2007. “Engineering” iGem wiki. 13th August 2009. <https://2007.igem.org/ETHZ/Engineering>

[11] Bintu, Lacramioara, Terence Hwa. “Transcriptional regulation by the numbers: applications.” Current Opinion in Genetics & Development 2005, 15:125–135.