ETHZ/Parameters

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- Introduction Section + Introduction - Model Overview Section + Model Overview - Detailed Model Section + Detailed Model - Final Model Section + Final Model Modeling Basics Page Modeling Basics Page - Mathematical Model Section + Mathematical Model FSM View Page FSM View Page Flip-Flop View Page Flip-Flop View Page Line 54: Line 54: Line 83: Line 85: - = Parameters for the EducatETH E. coli system = + = Parameters for the educatETH E.coli system =

- In order to provide as realistic simulation results as possible, and to find good estimates for the simulation parameters, we performed an intensive literature review. However, not all parameters could be found in the literature. Furthermore, one has to take into account that biological parameters cannot be estimated to a very high precision. + In order to ensure that our simulation results match the biology as close as possible, we tried to find good estimates for the biological system parameters. To this end, we performed an extensive literature review. However, not all parameters were found in the literature, especially those which do not refer to vastly used proteins. Whenever this was the case, logical estimates were provided by our biologists based on empirical values.

Line 237: Line 239: | 0.577 [µM] | 0.577 [µM] | P22CII repressor dissociation constant | P22CII repressor dissociation constant - | Ref. [11]. Note that they use a protein CII and we have P22CII. Does that match? + | Ref. [11] |- |- |} |} Line 290: Line 292: | 4 | 4 | P22CII repressor Hill cooperativity | P22CII repressor Hill cooperativity - | Ref. [11]. Note that they use a protein CII and we have P22CII. Does that match? + | Ref. [11] |- |- |} |}

Parameters for the educatETH E.coli system

In order to ensure that our simulation results match the biology as close as possible, we tried to find good estimates for the biological system parameters. To this end, we performed an extensive literature review. However, not all parameters were found in the literature, especially those which do not refer to vastly used proteins. Whenever this was the case, logical estimates were provided by our biologists based on empirical values.

Model Parameters

General parameters

c1max 0.01 [mM/h] max. transcription rate of constitutive promoter (per gene) promoter no. J23105; Estimate
c2max 0.01 [mM/h] max. transcription rate of LuxR-activated promoter (per gene) Estimate
lhi 25 high-copy plasmid number Estimate
llo 5 low-copy plasmid number Estimate
a 1% basic production levels Estimate

dLacI 2.31e-3 [1/s] degradation of LacI Ref. [10]
dTetR
• 1e-5 [1/s]
• 2.31e-3 [1/s]
• Ref. [9]
• Ref. [10]
dLuxR 1e-3 - 1e-4 [1/s] degradation of LuxR Ref: [6]
dCI 7e-4 [1/s] degradation of CI Ref. [7]
dYFP 6.3e-3 [1/min] degradation of YFP suppl. mat. to Ref. [8] corresponding to a half life of 110min
dGFP 6.3e-3 [1/min] degradation of GFP in analogy to YFP
dRFP 6.3e-3 [1/min] degradation of RFP in analogy to YFP
dCFP 6.3e-3 [1/min] degradation of CFP in analogy to YFP

Dissociation constants

KLacI
• 0.1 - 1 [pM]
• 800 [nM]
LacI repressor dissociation constant
• Ref. [2]
• Ref. [12]
KIPTG 1.3 [µM] IPTG-LacI repressor dissociation constant Ref. [2]
KTetR 179 [pM] TetR repressor dissociation constant Ref. [1]
KATC 893 [pM] ATC-TetR repressor dissociation constant Ref. [1]
KLuxR 55 - 520 [nM] LuxR activator dissociation constant Ref: [6]
KAHL 0.09 - 1 [µM] AHL-LuxR activator dissociation constant Ref: [6]
KCI
• 8 [pM]
• 50 [nM]
CI repressor dissociation constant
• Ref. [12]
• starting with values of Ref. [6] and using Ref. [3]
KP22CII 0.577 [µM] P22CII repressor dissociation constant Ref. [11]

Hill cooperativity

nLacI
• 1
• 2
LacI repressor Hill cooperativity
• Ref. [5]
• Ref. [12]
nIPTG 2 IPTG-LacI repressor Hill cooperativity Ref. [5]
nTetR 3 TetR repressor Hill cooperativity Ref. [3]
nATC 2 (1.5-2.5) ATC-TetR repressor Hill cooperativity Ref. [3]
nLuxR 2 LuxR activator Hill cooperativity Ref: [6]
nAHL 1 AHL-LuxR activator Hill cooperativity Ref. [3]
nCI 2 CI repressor Hill cooperativity Ref. [12]
nP22CII 4 P22CII repressor Hill cooperativity Ref. [11]

References

[1] Weber W et al. "A synthetic time-delay circuit in mammalian cells and mice", P Natl Acad Sci USA 104(8):2643-2648, 2007
[2] Setty Y et al. "Detailed map of a cis-regulatory input function", P Natl Acad Sci USA 100(13):7702-7707, 2003
[3] Braun D et al. "Parameter Estimation for Two Synthetic Gene Networks: A Case Study", ICASSP 5:769-772, 2005
[4] Fung E et al. "A synthetic gene--metabolic oscillator", Nature 435:118-122, 2005 (supplementary material)
[5] Iadevaia S and Mantzais NV "Genetic network driven control of PHBV copolymer composition", J Biotechnol 122(1):99-121, 2006
[6] Goryachev AB et al. "Systems analysis of a quorum sensing network: Design constraints imposed by the functional requirements, network topology and kinetic constants", Biosystems 83(2-3):178-187, 2004
[7] Arkin A et al. "Stochastic kinetic analysis of developmental pathway bifurcation in phage λ-Infected Escherichia coli cells", Genetics 149: 1633-1648, 1998
[8] Colman-Lerner A et al. "Yeast Cbk1 and Mob2 Activate Daughter-Specific Genetic Programs to Induce Asymmetric Cell Fates", Cell 107(6): 739-750, 2001 (supplementary material)
[9] Becskei A and Serrano L "Engineering stability in gene networks by autoregulation", Nature 405: 590-593, 2000
[10] Tuttle et al. "Model-Driven Designs of an Oscillating Gene Network", Biophys J 89(6):3873-3883, 2005
[11] McMillen LM et al. "Synchronizing genetic relaxation oscillators by intercell signaling", P Natl Acad Sci USA 99(2):679-684, 2002
[12] Basu S et al. "A synthetic multicellular system for programmed pattern formation", Nature 434:1130-1134, 2005