Team:KULeuven/Modeling
From 2011.igem.org
Revision as of 14:36, 16 September 2011 by Henrybeautyer (Talk | contribs)
Modeling Overview
1. Description of the whole system
To make predictions for are plasmid transformed E.coli, a structured segregated model is designed in Simbiology. A graphical representation of the model was build in the block diagram editor . Afterwards reaction equations and parameters were added. We designed one model for the whole system and 3 models for 3 subsystems. The 3 subsystems are antifreeze, freeze and cell death. For more information about these 3 subsystems, we refer to the extended project description and the 3 modelling pages: freeze, antifreeze and cell death.2. Full Model
There are in total 5 different kinetic equations we used in the model Transcription equation For most promoters, hill kinetics is used, it is a way of quantitatively describing cooperative binding processes, it was developed for hemoglobin in 1913. A Hill coefficient (n) is a measure for the cooperativity. Translation equation RNA degradation Protein degration Assimiliation FULL MODEL3. Simulation tests
Different amounts of lactose and arabinose are used to check the efficiency of the model. Lactose is the inducing compound involved in the freeze system, which can result in the production of ice nucleating protein (INP), while antifreeze system is repressed by lactose. On the other hand, L-arabinose is repressing the system by inducing the production of LuxI, and yet, in the antifreeze model, AFP production is induced by it.The results reveal that the kinetics of synthesis of AFP and CeaB are much higher than that of INP formation, for example, the difference of the concentrations of AFP and INP can reach 10E15 in Fig. 1. The main reason is the efficiency of the formation of AHL complex. From mathematical modeling, we can find INP gene functions after AHL complex, and they are in same series reaction. Therefore, the low activity of AHL directly leads to the limited amount of INP formation.
To stimulate the INP production, we can increase the amount of lactose, e.g. 100 for lactose and 1 for arabinose (Fig.2). As a result, the INP production dramatically increases by 10E14.
!! I AM FIG1.picture !!
!! I AM FIG2.picture !!
4. Sensitivity Analysis and parameter scan
5. Kinetic Constants
The parameters used in this model are:
Parameter | Value | Description | Reference |
---|---|---|---|
0 | NA | Notation convention | |
dRNA_LuxR | 0.00227 | did not find this value | [1] |
dLuxR | 1e-3 - 1e-4 [per sec+D4] (used in model: 0,0005) | http://parts.mit.edu/igem07/index.php/ETHZ/Engineering | [2] |
dRNA_LuxR | 0.00227 s-1 | did not find this value | [1] |
dLuxR | 1e-3 - 1e-4 [per sec+D4] (used in model: 0,0005) | http://parts.mit.edu/igem07/index.php/ETHZ/Engineering | [2] |
dLuxI | 2.31e-3 [per sec] | http://parts.mit.edu/igem07/index.php/ETHZ/Engineering | [3] |
dHSL | 1.02E-6 s-1 | very stable in the medium, average lifetime of 185h | [4] |
dLuxR_HSL | 0.0010 s-1 | complex of HSL and LuxR degrades, giving back HSL | (estimation) |
Association/Dissociation/Reaction Rates | |||
kass (HSL+LuxR) | 0.002372 s-1 (used in model 100= 0,002372*42100, why? to remove molar dimension) | association rate of HSL with LuxR (estimate from KM but recalculated to remove molar dimension) | [5] |
kdiss (HSL-LuxR) | 1.0 s-1 (used in model 42100, why?) | dissociation rate of the HSL-LuxR complex (estimate from KM) | [5] |
kass (HSL+lactonase) | 0.002372 s-1 (used in model 0,1, why?) | association rate of HSL with lactonase (estimate from KM but recalculated to remove molar dimension) | [6] |
kdiss (HSL-lactonase) | 4470.0 s-1 (used in model 188428.3, why?) | dissociation rate of the HSL-lactonase complex (estimate from Kd) | [6] |
kcat (HSL>>hydroxy-acid) | 29 s-1 (same in model) | lactonase catalyzed transformation of HSL to a hydroxy-acid | [6] |
Dissociation Constants | |||
KHSL_LuxR | 1E-6 [M] | HSL binding to LuxR | [5] |
Transcription Rates | |||
kmRNA_luxR | 0.025 | estimation ( constitutive promotor ?) | |
kmRNA_ompA_AFP | 0.025 | ||
kmRNA_luxI | 0.025 | ||
kmRNA_cIrep | 0.025 | ||
kmRNA_melA | 0.025 | ||
Translation Rates | |||
kluxR translation | 0.556 s-1 (0.03888888 in model?) | Translation rate for LuxR, B0034 RBS (relative efficiency 1.0) | [6] |
kluxI translation | 0.167 s-1 | translation rate for B0032 RBS [11] | [7] |
Hill cooperativity | |||
nluxR (luxR activator) | 2 | [10] | [2] |
nAHL (AHL-luxR activator) | 1 | [10] | [8] |
nHSL_LuxR | 2.08 | ||
CeaB transcription | 0,025 | ||
CeaB mRNA degradation | 0,002311 | ||
hybrid promotor kinetics | [9] | ||
k_transcr | 0.003 | ||
Km | 0.1099 | ||
vol | 0,000000000000001 | ||
N | 6,023E+29 | ||
hill | 2.08 | hil coefficient | |
Km_luxR | 0.00000405 | ||
k_l | 0.000504 | ||
other: protein degradations | 0.001 | estimation | |
other: mRNA degrade | 0.0025 | estimation | |
all other translation | 0.167 | same as kLuxI translation | [7] |
all other transcription | 0.025 | constitutive promotor value | [9] |
References
- J.A. Bernstein et al., “Global analysis of mRNA decay and abundance in Escherichia coli at single-gene resolution using two-color fluorescent DNA microarrays,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, Jul. 2002, pp. 9697–9702.
- "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
- Tuttle L.M., Salis H., Tomshine J., and Kaznessis Y.N., "Model-Driven Designs of an Oscillating Gene Network", Biophysical Journal, vol. 89, no. 6, pp. 3873--3883, 2005.
- Y. Wang and J.R. Leadbetter, “Rapid Acyl-Homoserine Lactone Quorum Signal Biodegradation in Diverse Soils,” Appl. Environ. Microbiol., vol. 71, Mar. 2005, pp. 1291-1299.
- N. Qin et al., “Analysis of LuxR Regulon Gene Expression during Quorum Sensing in Vibrio fischeri,” J. Bacteriol., vol. 189, Jun. 2007, pp. 4127-4134.
- L. Wang et al., “Specificity and enzyme kinetics of the quorum-quenching AHL-lactonase,” J. Biol. Chem., Jan. 2004, p. M311194200.
- http://partsregistry.org/Part:BBa_B0032
- Parameter Estimation for Two Synthetic Gene Networks: A Case Study", ICASSP 5:769-772, 2005
- https://2008.igem.org/Team:KULeuven/Model/CellDeath