Part:BBa_K332032:Experience
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Applications of BBa_K332032
To test the efficiency of the temperature induced RBS and the interaction between tetR gene and ptet promoter, we replaced the crystal protein gene with GFP (green fluorescence protein) gene. In this scenario, GFP will simulate the crystal protein’s production, as it is easily detected by flow cytometry.
To conclude, we have verified that our strategy works. It demonstrates the regulation of GFP in a temperature dependent fashion, as it is evident the mean GFP values are significantly lower at T>37° C.
We expect our circuit design to allow steady bacteria growth and inhibit crystal protein at T>37° C, and high protein production and low bacteria growth rate at T<37° C
We constructed series circuits' test as following article.
First, we made A+B circuit in the PSB3K3 plasmid.
in this way we can use it to test by Using Flow Cytometry to obtain the data of A+B
(for40°C, 37°C, 30°C, and 25°C)
and the following figures is our testing results. To test the efficiency of the temperature induced RBS and the interaction between tetR gene and ptet promoter, we replaced the crystal protein gene with GFP (green fluorescence protein) gene. In this scenario, GFP will simulate the crystal protein’s production, as it is easily detected by flow cytometry.
To conclude, we have verified that our strategy works. It demonstrates the regulation of GFP in a temperature dependent fashion, as it is evident the mean GFP values are significantly lower at T>37° C.
We expect our circuit design to allow steady bacteria growth and inhibit crystal protein at T>37° C, and high protein production and low bacteria growth rate at T<37° C
To make short of the matter,
at 25°C and 30°C (below 37° C): this RBS (BBa K115002) do not worked.
at 37°C and 40°C (above 37° C): this RBS (BBa K115002) do worked.
and we using those circits to make a modeling.
The first equation describes the temperature control in strand A (Fig. 1).
Alpha-Temp is the production rates corresponding to transcriptional rate of constitutive promoter and the translation rate of the RBS BBa_K115002 which is a temperature sensitive post-transcriptional regulator.
The second equation describes the concentration of GFP change with time. Alpha-B is production rates of the GFP, which are assumed to be given constants.
To describe transition during log phase and stationary phase, the alpha-Temp and alpha-B and is assumed to zero when the Terminator in stationary phase. Gamma-TetR, and gamma-GFP are decay rates of the corresponding proteins.
When bacteria divide, the molecular in a bacterium will be dilute. Because bacteria grow faster, the dilution rate d(t) is included in this model and can be calculated from OD ratio of medium (Fig. 2).
For an inhibition of TetR protein, Hill function is an S-shaped curve which can be described in the form 1 / (1 +x^n) (Alon, 2007).
The values of the kinetic parameters used in the simulation were initially obtained from the literature and experimental data.
Data computations were performed with Matlab software. A program was written and used as a subroutine in Matlab for parameter optimization using nonlinear regression (Fig. 3)
Fig. 2: The OD ratio is increased faster in log phase than it in stationary phase. The dilution rate d(t) can be calculated from OD ratio and used in out model.
Fig. 3: The behavior of low temperature release circuit at 25°C, 30°C, 37 °C and 40°C. Experimental data (dot) and simulated results (line) of the model suggest this temperature-dependent genetic circuit can control the expression level of the target protein by the host cell’s incubation.
The fitting results indicate our dynamic model can quantitatively assess the relative translational activity of RBS during log phase and stationary phase.
Fig. 4: The relative translation activity of this RBS (BBa_K115002) at 25°C, 30°C, 37 °C and 40°C estimated using least squares estimation from experimental data.
This means our dynamic model can accurately quantify the translational activity of the RBS from experimental data.
According to the fitting results (Fig. 3), the dynamic model successfully approximated the behavior of our low-temperature release system.
The model equations present interesting mathematical properties that can be used to explore how qualitative features of the genetic circuit depend on reaction parameters.
This method of dynamic modeling can be used to guide the choice of genetic ‘parts’ for implementation in circuit design in the future.
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