Team:Utah State/Modeling
From 2009.igem.org
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<td id="nav"><a href="https://2009.igem.org/Team:Utah_State/Project"><font size = 4>PROJECT</font></a></td> | <td id="nav"><a href="https://2009.igem.org/Team:Utah_State/Project"><font size = 4>PROJECT</font></a></td> | ||
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<td id="nav"><a href="https://2009.igem.org/Team:Utah_State/Parts"><font size = 4>BIOBRICKS</font></a></td> | <td id="nav"><a href="https://2009.igem.org/Team:Utah_State/Parts"><font size = 4>BIOBRICKS</font></a></td> | ||
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- | <td id="nav"><a href="https://2009.igem.org/Team:Utah_State/Achievements"><font size = 4> | + | <td id="nav"><a href="https://2009.igem.org/Team:Utah_State/Achievements"><font size = 4>JUDGING</font></a></td> |
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+ | <td id="nav"><a href="https://2009.igem.org/Team:Utah_State/Contact"><font size = 4>CONTACT</font></a></td> | ||
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- | There | + | There are currently few guidelines for selecting which signal peptide (which determine the secretion pathway taken) should be used for a given recombinant protein (Choi, 2004). The construction of models that evaluate protein secretion may provide a useful framework for studying and attempting to predict factors that affect secretion efficiency. Models of the Sec and Tat translocation pathways were made using MATLAB’s Simbiology toolbox. They were evaluated using the embedded ordinary differential equation solver in the software. Assumptions were made for both models due to lack of time scale information for individual steps for these translocation mechanisms. However, both provide a flexible framework that can become more detailed as additional information is found in literature or discovered in the laboratory. Within both models, a protein is first generated and then carried out of the cytoplasm to the periplasm. Only protein species were tracked, as species involved with making the protein are currently not as useful to monitor. |
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Simulations | Simulations | ||
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- | <p class = "class">Simulations for the Sec pathways were run for 5400 seconds (1.5 hours), which allowed the number of periplasmic to reach approximately steady state. The simulation for the Sec pathway secretion model is shown below as Figure 3. | + | <p class = "class">Simulations for the Sec pathways were run for 5400 seconds (1.5 hours), which allowed the number of periplasmic to reach approximately steady state. The simulation for the Sec pathway secretion model is shown below as Figure 3. |
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Protein generated by the <i>E. coli</i> cell reaches steady state quickly, as it is both produced and translocated in to the periplasm rapidly. The periplasmic protein reaches approximately 90 proteins upon achieving steady state. The long settling time and high number of periplasmic protein are a result of the long protein degradation time.</p> | Protein generated by the <i>E. coli</i> cell reaches steady state quickly, as it is both produced and translocated in to the periplasm rapidly. The periplasmic protein reaches approximately 90 proteins upon achieving steady state. The long settling time and high number of periplasmic protein are a result of the long protein degradation time.</p> | ||
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+ | <div align="center"><img src="https://static.igem.org/mediawiki/2009/a/a3/ModifiedGraph1.jpg" align = "middle" height="400" style="padding:.5px; border-style:solid; border-color:#999" alt="Figure 3"> </div> | ||
+ | <br> | ||
+ | <div align="center"><font size="2.5" face="Helvetica, Arial, San Serif" color =#231f20> | ||
+ | <b>Figure 3.</b> Simulation of protein production and translocation by the Sec pathway | ||
+ | </div> | ||
+ | <br> | ||
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<p class= "class">Simulations for the Tat pathway were run for 3600 seconds (1 hour). The shorter length of simulation relative to the Sec model is used despite having significantly longer protein translocation time because the time-consuming folding step limits the reaction. The results of the ordinary differential equation simulation are shown below in Figure 4. The amount of periplasmic protein is lower than cytoplasmic and folded protein values upon reaching steady state. This is due to the rate-limiting folding step, which also leads to an expected high concentration of folded protein as well. | <p class= "class">Simulations for the Tat pathway were run for 3600 seconds (1 hour). The shorter length of simulation relative to the Sec model is used despite having significantly longer protein translocation time because the time-consuming folding step limits the reaction. The results of the ordinary differential equation simulation are shown below in Figure 4. The amount of periplasmic protein is lower than cytoplasmic and folded protein values upon reaching steady state. This is due to the rate-limiting folding step, which also leads to an expected high concentration of folded protein as well. | ||
</p> | </p> | ||
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+ | <br> | ||
+ | <div align="center"><img src="https://static.igem.org/mediawiki/2009/d/d2/ModifiedGraph2.jpg" align = "middle" height="400" style="padding:.5px; border-style:solid; border-color:#999" alt="Figure 3"> </div> | ||
+ | <br> | ||
+ | <div align="center"><font size="2.5" face="Helvetica, Arial, San Serif" color =#231f20> | ||
+ | <b>Figure 3.</b> Simulation of protein production and translocation by the Sec pathway | ||
+ | </div> | ||
+ | <br> | ||
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<p class = "class"> The accuracy of models, such as those constructed, will increase as more knowledge about the time step parameters for each of the mechanisms is determined. These improvements will allow for a better comparison of the two secretion methods, which can lead to better signal peptide selection (and corresponding pathway selection) for the protein in question. Future work could also include incorporating promoter strength into the model to affect transcription rates to provide a more accurate depiction of what is happening inside the cell. Better correlation between these parameters and the model will result in a more useful aid in when studying protein secretion. | <p class = "class"> The accuracy of models, such as those constructed, will increase as more knowledge about the time step parameters for each of the mechanisms is determined. These improvements will allow for a better comparison of the two secretion methods, which can lead to better signal peptide selection (and corresponding pathway selection) for the protein in question. Future work could also include incorporating promoter strength into the model to affect transcription rates to provide a more accurate depiction of what is happening inside the cell. Better correlation between these parameters and the model will result in a more useful aid in when studying protein secretion. | ||
</p> | </p> |
Latest revision as of 03:06, 22 October 2009
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