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Team:TUDelft/Modeling Parameters
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| Estimate | | Estimate | ||
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| + | | λ | ||
| + | | 5.76 | ||
| + | | intensity (CFU/mm<sup>2</sup>) | ||
| + | | Calculated using N<sub>r</sub>/A. | ||
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Revision as of 17:59, 17 October 2009
Modeling Parameters
Transcriptional Cascade
| Parameter | Values | Description | Ref |
| cpLac | 5e-10 | maximum transcription rate (M/min) | [1] |
| cpTet | 1.5e-7 | maximum transcription rate (M/min) | [2] |
| cpλ | 1.5e-7 | maximum transcription rate (M/min) | estimate |
| K50IPTG | 1.3e-6 | dissociation constant (M) | [3] |
| K50LacI | 800e-9 | dissociation constant (M) | [3] |
| K50TetR | 179e-12 | dissociation constant (M) | [3] |
| K50CI | 8e-12 | dissociation constant (M) | [3] |
| nIPTG | 2 | Hills coefficient | [3] |
| nLacI | 2 | Hills coefficient | [3] |
| nTetR | 3 | Hills coefficient | [3] |
| nCI | 2 | Hills coefficient | [3] |
| dLacI | 0.1386 | degradation rate (M/min) | [3] |
| dTetR | 0.1386 | degradation rate (M/min) | [3] |
| dCI | 0.042 | degradation rate (M/min) | [3] |
| dRFP | 6.3e-3 | degradation rate (M/min) | [3] |
| dGFP | 6.3e-3 | degradation rate (M/min) | [3] |
| dmRNA | 0.029 | degradation rate (M/min) | [4] |
| α | 16 - 57 | translation rate (translations/min/mRNA), depends on growth rate (a default value of 30 is used) | [5] |
| kIPTG | 0.92 | rate constant for IPTG diffusion into cell | [6] |
Conjugation
| Parameter | Values | Description | Ref |
| A | 1735 | Surface area available (mm2) | Area of 0.2μm filter used in conjugation tests. |
| r0 | 0.8 | initial colony radius (μm) | [7] |
| gn | 0.99 | specific growth rate (1/hr) | Determined experimentally for R751 containing cells. |
| gr | 30 | colony radial specific growth rate (μm/hr) | [8] |
| Nd | 10000 | initial number of donors | Estimate |
| Nr | 10000 | initial number of donors | Estimate |
| λ | 5.76 | intensity (CFU/mm2) | Calculated using Nr/A. |
References
1. M. Santillan, M.C. Mackey, E.S. Zeron, Origin of Bistability in the lac Operon, Biophysical Journal, Volume 92, Issue 11, 1 June 2007, Pages 3830-3842, ISSN 0006-3495, DOI: 10.1529/biophysj.106.101717. (http://www.sciencedirect.com/science/article/B94RW-4V9YVV6-8/2/cd4c5ba8d532a314eed932d787f56a35)
2. Thuc T. Le, Calin C. Guet, Philippe Cluzel, Protein expression enhancement in efflux-deleted mutant bacteria, Protein Expression and Purification, Volume 48, Issue 1, July 2006, Pages 28-31, ISSN 1046-5928, DOI: 10.1016/j.pep.2005.11.018. (http://www.sciencedirect.com/science/article/B6WPJ-4HV73GJ-1/2/36f5ff80fe56e2533f1ec0699bf12a7e)
3. http://parts.mit.edu/igem07/index.php?title=ETHZ/Parameters
4. Barrio M, Burrage K, Leier A, Tian T. Oscillatory regulation of Hes1: Discrete stochastic delay modelling and simulation. PLoS Comput Biol. 2006 Sep 8; 2(9): e117.
5. Liang, S.-T., Xu, Y.-C., Dennis, P., Bremer, H. mRNA Composition and Control of Bacterial Gene Expression. J. Bacteriol. 2000 182: 3037-3044.
6. Michail Stamatakis, Nikos V. Mantzaris, Comparison of Deterministic and Stochastic Models of the lac Operon Genetic Network, Biophysical Journal, Volume 96, Issue 3, 4 February 2009, Pages 887-906, ISSN 0006-3495, DOI: 10.1016/j.bpj.2008.10.028. (http://www.sciencedirect.com/science/article/B94RW-4VGGB7R-D/2/bd642ea418c1213f10259b778907a1ee)
7. E. coli Statistics http://gchelpdesk.ualberta.ca/CCDB/cgi-bin/STAT_NEW.cgi
8. Julian W. T. Wimpenny. CRC handbook of laboratory model systems for microbial ecosystems, Volume 2. page 127. http://books.google.com/books?id=wy4WFX3_7bMC
