Team:ETH Zurich/Modeling/Switch Circuit

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== Implementation ==
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== Implementation and Simulation ==
The model of the switch circuit has been implemented using the Simbiology Toolbox in MATLAB.<br>
The model of the switch circuit has been implemented using the Simbiology Toolbox in MATLAB.<br>

Revision as of 09:05, 28 October 2008

Contents

Switch Circuit

Circuit model.JPG

Detailed Model

diffusion of IPTG

In order to switch on the sircuit, we induce with IPTG. When we add the IPTG into the medium diffuses reversibly between the medium and the cells, where it is slowly degraded. In a chemostat extracellular IPTG is washed away.
Grafik left width=30%

diffusion of tet

The second inducer which is used in our system is tet. This also diffuses into the cell when it is added into the medium and degrades slowly.
Grafik left width=30%

binding of tetR to LacI-promotor and LacIIs-promotor

Binding tetR to LacI.JPG

binding of LacI and LacIIs to GFP-promotor

Binding LacIIs to Pgfp.JPG

Binding LacI to Pgfp.JPG

binding of tet to tetR

Binding tet to tetR.JPG

binding of IPTG to LacI

Binding IPTG to LacI.JPG

transcription and translation of LacI

Transcription of LacI.JPG

Translation of LacI.JPG

transcription and translation of LacIIs

Transcription of LacIIs.JPG

Translation of LacIIs.JPG

transcription and translation of tetR

Transcription of tetR.JPG

Translation of tetR.JPG

transcription and translation of GFP

Transcription of gfp.JPG

Translation of gfp.JPG

dimerization of tetR

Dimerization of tetR.JPG

dimerization and tetramerization of LacI and LacIIs

Dimerization of LacI.JPG

Dimerization of LacIIs.JPG

Tetramerization of LacI.JPG

Tetramerization of LacIIs.JPG

Implementation and Simulation

The model of the switch circuit has been implemented using the Simbiology Toolbox in MATLAB.

For the sake of simplicity, the effects of dimerization and dimerization/tetramerization of tetR and LacI/LacIIs have been neglected. So we did with the additional steps involving the RNApolymerase in the transcription of proteins and the ribosomes in the translation of proteins.

diagram view of the model

Simulation

In order to get some useful results out of this model, we ran deterministic and stochastic simulations using Mass-Action-Kinetics. The stochastic simulations turned out to be computationally very demanding and yielded no additional results compared to the deterministic ones.

Results and Discussion

Parameters

In this section you can find all the parameters used in the simulation.

# Parameter name Value Units Reference
1 k_assoc(IPTG_LacI) 5.0 X
2 k_assoc(LacI) 5.0 X
3 k_assoc(LacIs) 5.0 X
4 k_assoc(tet) 5.0 X
5 k_assoc(tetR) 5.0 X
6 k_dec(IPTG) 0.002 X
7 k_dec(IPTG_ext) 0.005 X
8 k_dec(LacI) 5.0 X
9 k_dec(LacIs) 5.0 X
10 k_dec(gfp) 1.0 X
11 k_dec(mRNA_LacI) 0.1 X
12 k_dec(mRNA_LacIs) 0.1 X
13 k_dec(mRNA_gfp) 0.2 X
14 k_dec(mRNA_tetR) 0.05 X
15 k_dec(tetR) 0.05 X
16 k_dec(tet) 0.002 X
17 k_dec(tet_ext) 0.005 X
18 k_diff(IPTG) 0.1 X
19 k_diff(tet) 0.1 X
20 k_dissoc(IPTG_LacI) 1.0 X
21 k_dissoc(LacI) 1.0 X
22 k_dissoc(LacIs) 1.0 X
23 k_dissoc(tet) 1.0 X
24 k_dissoc(tetR) 1.0 X
25 k_tl(LacI) 10.0 X
26 k_tl(LacIs) 10.0 X
27 k_tl(gfp) 10.0 X
28 k_tl(tetR) 10.0 X
29 k_tr(LacI) 2.0 X
30 k_tr(LacIs) 2.0 X
31 k_tr(gfp) 2.0 X
32 k_tr(tetR) 2.0 X

References

(1) "Spatiotemporal control of gene expression with pulse-generating networks", Basu et al., PNAS, 2004

(2) "Genetic circuit building blocks for cellular computation, communications, and signal processing", Weiss et al., Natural Computing, 2003

(3) "Predicting stochastic gene expression dynamics in single cells", Mettetal et al., PNAS, 2006

(4) "Engineered gene circuits", Hasty et al., Nature, 2002