Difference between revisions of "Part:BBa K098995:Experience"

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<p> This equation describes the concentration of GFP in <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a>change with time (Figure. 1). Alpha-Temp is the protein expression rates corresponding to <a href=" https://parts.igem.org/Part:BBa_K098995">BBa_K098995</a>which is a temperature sensitive expression device. To describe transition during log phase and stationary phase, the alpha-Temp is assumed to zero in stationary phase. Gamma-GFP are decay rates of the GFP 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 (Figure. 2). 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 (Figure. 3).</p>
 
<p> This equation describes the concentration of GFP in <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a>change with time (Figure. 1). Alpha-Temp is the protein expression rates corresponding to <a href=" https://parts.igem.org/Part:BBa_K098995">BBa_K098995</a>which is a temperature sensitive expression device. To describe transition during log phase and stationary phase, the alpha-Temp is assumed to zero in stationary phase. Gamma-GFP are decay rates of the GFP 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 (Figure. 2). 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 (Figure. 3).</p>
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<br><b>Figure 2. </b> 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.
 
<br><b>Figure 2. </b> 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.
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<br><b>Figure 3. </b>The behavior of high temperature induced device <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a> at 25°C, 37 °C and 42°C. Experimental data (dot) and simulated results (line) of the model  suggest this temperature-dependent device 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 protein expression activity of <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a>during log phase and stationary phase.
 
<br><b>Figure 3. </b>The behavior of high temperature induced device <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a> at 25°C, 37 °C and 42°C. Experimental data (dot) and simulated results (line) of the model  suggest this temperature-dependent device 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 protein expression activity of <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a>during log phase and stationary phase.
 
<p><br>Using least squares estimation from experimental data, the relative the protein expression activity of <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a>  at 25°C, 37 °C and 42°C were estimated (Figure. 4).<br></p>
 
<p><br>Using least squares estimation from experimental data, the relative the protein expression activity of <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a>  at 25°C, 37 °C and 42°C were estimated (Figure. 4).<br></p>
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<div><img src="https://static.igem.org/mediawiki/2011/b/b2/M-4.JPG" width="450"></div>
 
<br><b>Figure 4. </b>The relative the protein expression activity of  <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a>at 25°C, 37 °C and 42°C estimated using least squares estimation from experimental data. The protein expression activity at 42°C is higher than 25°C, 37 °C
 
<br><b>Figure 4. </b>The relative the protein expression activity of  <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a>at 25°C, 37 °C and 42°C estimated using least squares estimation from experimental data. The protein expression activity at 42°C is higher than 25°C, 37 °C
 
<p><br>According to the fitting results (Figure. 3), the dynamic model successfully approximated the behavior of our high-temperature induced system. The model equation presents 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.</p><br>
 
<p><br>According to the fitting results (Figure. 3), the dynamic model successfully approximated the behavior of our high-temperature induced system. The model equation presents 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.</p><br>

Revision as of 16:08, 5 October 2011

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High Temperature Induced System – cI Promoter & cI repressor

Modeling and simulations of high temperature induced device BBa_K098995 – cI promoter & cI repressor

In order to characterize this high temperature induced device <a href=" https://parts.igem.org/Part:BBa_K098995">BBa_K098995</a>, the fluorescence intensity of<a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a> is measured by the flow cytometry (Figure. 1).

<img src = M-1.1.JPG>


Figure1. Part <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a> Design. The heat induced device <a href=" https://parts.igem.org/Part:BBa_K098995">BBa_K098995</a>uses gene <a href=" https://parts.igem.org/Part:BBa_K098997">BBa_K098997</a> coding for cI repressor to inhibit the cI promoter <a href=" https://parts.igem.org/Part:BBa_R0051">BBa_R0051</a>. The activity of cI repressor is decreased by elevating temperature from 30 ℃ to 42 ℃. A differential equation is used to calculate protein expression activity of <a href=" https://parts.igem.org/Part:BBa_K098995">BBa_K098995</a> as follows.


<img src="M-5.JPG" width="450">
<p> This equation describes the concentration of GFP in <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a>change with time (Figure. 1). Alpha-Temp is the protein expression rates corresponding to <a href=" https://parts.igem.org/Part:BBa_K098995">BBa_K098995</a>which is a temperature sensitive expression device. To describe transition during log phase and stationary phase, the alpha-Temp is assumed to zero in stationary phase. Gamma-GFP are decay rates of the GFP 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 (Figure. 2). 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 (Figure. 3).

<img src="M-2.jpg" width="450">


Figure 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.

<img src="M-3.jpg" width="450">


Figure 3. The behavior of high temperature induced device <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a> at 25°C, 37 °C and 42°C. Experimental data (dot) and simulated results (line) of the model suggest this temperature-dependent device 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 protein expression activity of <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a>during log phase and stationary phase.


Using least squares estimation from experimental data, the relative the protein expression activity of <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a> at 25°C, 37 °C and 42°C were estimated (Figure. 4).

<img src="M-4.JPG" width="450">


Figure 4. The relative the protein expression activity of <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a>at 25°C, 37 °C and 42°C estimated using least squares estimation from experimental data. The protein expression activity at 42°C is higher than 25°C, 37 °C


According to the fitting results (Figure. 3), the dynamic model successfully approximated the behavior of our high-temperature induced system. The model equation presents 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.



References

Alon, U. (2007) An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/CRC.


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