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<br><b>Figure 3: The time-course expression results were measured with time for 12 sets of GFP expression devices.</b> (a) promoter-RBS devices which contained the same promoter J23105 combined with three different RBSs (B0030, B0032, and B0034) were selected for measuring. (b) promoter-RBS devices which contained the same promoter J23106 combined with three different RBSs (B0030, B0032, and B0034) were selected for measuring. (c) promoter-RBS devices which contain the same promoter J23114 combined with three different RBSs (B0030, B0032, and B0034) were selected for measuring. (d) promoter-RBS devices which contained the same repressible promoter R0040 combined with three different RBSs (B0030, B0032, and B0034) were selected for measuring. Each measuring even was detected every 15 minutes. And all of the data represented the average of three independent measurements. Error bars indicated standard deviations. X-axis indicated the time units, and Y-axis indicated the fluorescence units with different scales. Furthermore, the fluorescent signals changed with time per cell were measured by using a flow cytometer. | <br><b>Figure 3: The time-course expression results were measured with time for 12 sets of GFP expression devices.</b> (a) promoter-RBS devices which contained the same promoter J23105 combined with three different RBSs (B0030, B0032, and B0034) were selected for measuring. (b) promoter-RBS devices which contained the same promoter J23106 combined with three different RBSs (B0030, B0032, and B0034) were selected for measuring. (c) promoter-RBS devices which contain the same promoter J23114 combined with three different RBSs (B0030, B0032, and B0034) were selected for measuring. (d) promoter-RBS devices which contained the same repressible promoter R0040 combined with three different RBSs (B0030, B0032, and B0034) were selected for measuring. Each measuring even was detected every 15 minutes. And all of the data represented the average of three independent measurements. Error bars indicated standard deviations. X-axis indicated the time units, and Y-axis indicated the fluorescence units with different scales. Furthermore, the fluorescent signals changed with time per cell were measured by using a flow cytometer. | ||
<br><b>2.Build a Mathematical Model to Characterize Protein Expression Ability of Promoter-RBS Devices.<b> | <br><b>2.Build a Mathematical Model to Characterize Protein Expression Ability of Promoter-RBS Devices.<b> | ||
| - | The logistic growth model is commonly used to describe the bacterium growth curve under the nutrient limited condition as follow [1-2]: | + | <p><br>The logistic growth model is commonly used to describe the bacterium growth curve under the nutrient limited condition as follow [1-2]:<br></p> |
<div><img src = "https://static.igem.org/mediawiki/2011/b/b4/P-4.jpg" width="350"></div> (1) | <div><img src = "https://static.igem.org/mediawiki/2011/b/b4/P-4.jpg" width="350"></div> (1) | ||
<p><br>Where s denotes the cell density parameter (optical density at 600 nm (O.D. 600)), smax denotes the maximum value of cell density, and ks denotes growth rate constant. The cell density time course data of the bacteria with different GFP expression devices were used to solve Eq. (1) (Fig. 4). The growth rate constant ks was calculated and listed in Table 2.<br></p> | <p><br>Where s denotes the cell density parameter (optical density at 600 nm (O.D. 600)), smax denotes the maximum value of cell density, and ks denotes growth rate constant. The cell density time course data of the bacteria with different GFP expression devices were used to solve Eq. (1) (Fig. 4). The growth rate constant ks was calculated and listed in Table 2.<br></p> | ||
Revision as of 16:27, 4 October 2011
Measurement
A new measurement method can calculate the protein expression rate in the different E. coli population density for the protein expression device
Summary
In previous studies, the transcriptional strengths of promoter and transcriptional strengths of RBSs was defined as a constant values. But in biological concepts, we know the expression rates of most proteins decreased dramatically while the bacteria at the stationary phase. To overcome this problem, our team developed a new measurement method can calculate the protein expression activity of promoter_RBS device changes with cell density in the culture tube directly.
We provide a simple polynomial equation which can describe linear relationship between the protein expression activity p(s) of promoter_RBS device and cell desity s (OD600).
where p0 denotes zero-order coefficient, p1 denotes first-order coefficient. We found that using this linear function to describe the activity of a protein expression device during log phase and stationary phase improved simulation results significantly. That means that the simple model equation can character a protein expression device (a promoter combined with a RBS) during cell grow from log phase to stationary phase. The fitting results indicate this hypothesis is reasonable. Furthermore, this model enable us to rational connect a promote_RBS device with different strength to obtain a target protein expression level in a synthetic genetic circuit.
Motivation
In previous studies to model a protein expression, the transcription rates of promoters and translation rates of ribosome binding sites (RBSs) are defined as a constant value. However, the values are not constant during cell growth. For example, the transcription rates of promoters and the translation rates of RBSs are lager in log phase than bacteria growth in stationary phase. To overcome this problem, we selected four promoters and three RBSs with different regulation strength and constructed 12 protein expression devices which combine promoter, RBS and green fluorescent protein (GFP) in Escherichia coli. The GFP expression levels with time were measured using a flow cytometry, and the experimental data can used to characterize a protein expression rate of a protein expression device which contains a promoters and a RBS. A dynamic model that captured the experimentally observed differences for each protein expression device was developed in this study. Using this method, we can measurement the protein expression rate in the different E. coli population density for the protein expression device.
Methods
Two strategies will be applied in this method:
1.Construction of a Promoter-RBS Library and Assay of GFP Expression
We selected four promoters and three RBSs with different regulation strength and constructed 12 protein expression devices which combine promoter, RBS and green fluorescent protein (GFP) in E. coli (Fig.1 &table.1). The GFP expression level with time was measured using a flow cytometry (Fig.3 &Fig.4)
Figure 1: Combinatorial promoter-RBS architecture reveals constitutive expression.
The reporter gene, green fluorescence protein (GFP) locates downstream of promoter-RBS devices as output. The output expression is controlled by differently combinatorial promoter-RBS devices. Each promoter-RBS device is expected to show the significant expression diversity. The combinatorial library contains 12 sets of promoter-RBS devices. Each set consists of different promoters and RBSs.
Table 1: The name of 12 green fluorescence protein devices and their composed biobricks
A (*) refers to construct in two different backbone.
Figure 2: The green fliorescence intensity changed with time provided the useful hints of the interactiofluorescence intensity per cell was measured using a flow cytometer.
The O.D.ratio and the fluorescence data n function between promoters and RBSs.
Figure 3: The time-course expression results were measured with time for 12 sets of GFP expression devices. (a) promoter-RBS devices which contained the same promoter J23105 combined with three different RBSs (B0030, B0032, and B0034) were selected for measuring. (b) promoter-RBS devices which contained the same promoter J23106 combined with three different RBSs (B0030, B0032, and B0034) were selected for measuring. (c) promoter-RBS devices which contain the same promoter J23114 combined with three different RBSs (B0030, B0032, and B0034) were selected for measuring. (d) promoter-RBS devices which contained the same repressible promoter R0040 combined with three different RBSs (B0030, B0032, and B0034) were selected for measuring. Each measuring even was detected every 15 minutes. And all of the data represented the average of three independent measurements. Error bars indicated standard deviations. X-axis indicated the time units, and Y-axis indicated the fluorescence units with different scales. Furthermore, the fluorescent signals changed with time per cell were measured by using a flow cytometer.
2.Build a Mathematical Model to Characterize Protein Expression Ability of Promoter-RBS Devices.
The logistic growth model is commonly used to describe the bacterium growth curve under the nutrient limited condition as follow [1-2]:
Where s denotes the cell density parameter (optical density at 600 nm (O.D. 600)), smax denotes the maximum value of cell density, and ks denotes growth rate constant. The cell density time course data of the bacteria with different GFP expression devices were used to solve Eq. (1) (Fig. 4). The growth rate constant ks was calculated and listed in Table 2.
