"
Team:Colombia Uniandes/Parameters
From 2013.igem.org
(Difference between revisions)
| (10 intermediate revisions not shown) | |||
| Line 1: | Line 1: | ||
{{Http://2013.igem.org/Team:Colombia Uniandes/Bootstrap}} | {{Http://2013.igem.org/Team:Colombia Uniandes/Bootstrap}} | ||
| - | + | <html> | |
<div class="container"> | <div class="container"> | ||
<div class="span11"> | <div class="span11"> | ||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
| - | |||
<center><h1 id="param"> Parameters</h1></center> | <center><h1 id="param"> Parameters</h1></center> | ||
| - | + | <p align="justify"> | |
The second step in the deterministic model is to find the parameters that define the system. There are 3 possible ways to do this: | The second step in the deterministic model is to find the parameters that define the system. There are 3 possible ways to do this: | ||
| - | + | <p align="justify"> | |
<ol> | <ol> | ||
<li><b> Literature: </b> Many of the genes that we use in iGEM have been characterised before, and the constants that model their behaviour have been found. Sometimes an exact value is not found, so we have to find at least the order of magnitude or a range where the parameter can be found </li> | <li><b> Literature: </b> Many of the genes that we use in iGEM have been characterised before, and the constants that model their behaviour have been found. Sometimes an exact value is not found, so we have to find at least the order of magnitude or a range where the parameter can be found </li> | ||
| - | + | </p> | |
| - | <li><b> Experiments: </b> This is the most reliable way of knowing the parameters. In our case we need | + | <p align="justify"> |
| - | + | <li><b> Experiments: </b> This is the most reliable way of knowing the parameters. In our case, we need them to perform the deterministic model first, in order to see if the system works as expected in the lab. If there is an estimated order of magnitude the model can be run while the exact value is found experimentally </li> | |
| - | <li><b> Screening: </b> If the parameters we are looking of are not found in literature we proceed to perform a screening of parameters as described in [https://2012.igem.org/Team:Colombia/Modeling/Paramterers Colombia 2012 Team Wiki] | + | </p> |
| + | <p align="justify"> | ||
| + | <li><b> Screening: </b> If the parameters we are looking of are not found in literature we proceed to perform a screening of parameters as described in </html>[https://2012.igem.org/Team:Colombia/Modeling/Paramterers Colombia 2012 Team Wiki] | ||
| + | <html></p> | ||
</ol> | </ol> | ||
Below is shown how we found the parameters for both of the projects: | Below is shown how we found the parameters for both of the projects: | ||
| + | <div class="accordion" id="accordion2"> | ||
| + | <div class="accordion-group"> | ||
| + | <div class="accordion-heading"> | ||
| + | <a class="accordion-toggle" data-toggle="collapse" data-parent="#accordion2" href="#collapseOne"> | ||
| + | Glucocorticoid sensor | ||
| + | </a> | ||
| + | </div> | ||
| + | <div id="collapseOne" class="accordion-body collapse"> | ||
| + | <div class="accordion-inner"> | ||
| + | |||
<h3 id="gluco"> Glucocorticoid sensor </h3> | <h3 id="gluco"> Glucocorticoid sensor </h3> | ||
| Line 47: | Line 36: | ||
After looking for parameters in literature, the following values were found: | After looking for parameters in literature, the following values were found: | ||
| - | + | </html> | |
[[File:paramGC.png|center|750x800px]] | [[File:paramGC.png|center|750x800px]] | ||
| - | + | <html> | |
<ul> | <ul> | ||
| Line 63: | Line 52: | ||
</ul> | </ul> | ||
| - | + | <b>Sensitivity Analysis</b> | |
| + | |||
| + | <br> | ||
| + | <br> | ||
In order to see which of the parameters had a significant effect in the signal response, we performed a sensitivity analysis. For each parameter, we took the value found in the literature (or guessed value) and evaluated the model in a range that varied from one order of magnitude below to one order of magnitude above. Depending on how much the response varied to each parameter the screening was planned. | In order to see which of the parameters had a significant effect in the signal response, we performed a sensitivity analysis. For each parameter, we took the value found in the literature (or guessed value) and evaluated the model in a range that varied from one order of magnitude below to one order of magnitude above. Depending on how much the response varied to each parameter the screening was planned. | ||
| - | + | <br> | |
| + | <br> | ||
After perfoming the analysis we found some parameters that had little effect on the desired response of the system. For example, the destruction rate of glucocorticoids inside the cell and the reverse kinetic constant for the formation of the complex: | After perfoming the analysis we found some parameters that had little effect on the desired response of the system. For example, the destruction rate of glucocorticoids inside the cell and the reverse kinetic constant for the formation of the complex: | ||
| - | + | </html> | |
[[File:Nosens.png|left|950x700px]] | [[File:Nosens.png|left|950x700px]] | ||
| - | + | <html> | |
The parameters related to the Hill expression had a significant effect on the desired response. For this reason, and given the fact that two out of three parameters had an estimated order of magnitude, the screening of parameters was focused on this part of the model. | The parameters related to the Hill expression had a significant effect on the desired response. For this reason, and given the fact that two out of three parameters had an estimated order of magnitude, the screening of parameters was focused on this part of the model. | ||
| - | + | </html> | |
[[File:Hillsens.png|left|950x700px]] | [[File:Hillsens.png|left|950x700px]] | ||
| Line 80: | Line 73: | ||
You can see the complete sensitivity analysis [https://static.igem.org/mediawiki/2013/f/f8/Sensitivity.pdf here]. | You can see the complete sensitivity analysis [https://static.igem.org/mediawiki/2013/f/f8/Sensitivity.pdf here]. | ||
| + | <html> | ||
| + | <br> | ||
| + | <br> | ||
The parameters that need to be found with the model are those involved in the activation of the promoter by GAL4. Before this promoter exists an Upstream Activation Sequence (UAS) that enhances the production of M-CHERRY. This sequence can be as many times as wanted upstream de promoter, and depending on the number of times it exists the strength of the production is affected. Little is found in the literature about the parameters that concern to the activation by GAL4 and less is found in the difference of the parameters with the different arrangements of the UAS. | The parameters that need to be found with the model are those involved in the activation of the promoter by GAL4. Before this promoter exists an Upstream Activation Sequence (UAS) that enhances the production of M-CHERRY. This sequence can be as many times as wanted upstream de promoter, and depending on the number of times it exists the strength of the production is affected. Little is found in the literature about the parameters that concern to the activation by GAL4 and less is found in the difference of the parameters with the different arrangements of the UAS. | ||
| + | <br> | ||
| + | <br> | ||
| - | Without the parameter information of at least one arrangement of UAS the screening of parameters can not be performed | + | Without the parameter information of at least one arrangement of UAS the screening of parameters can not be performed. In order to solve this problem we designed an experiment, in which we tested the M-cherry's expression trough time with different UAS arrangements (1,3,6 and 9 UAS before de promoter). With the results of this procedure we aim to find an expression for the activation of the promoter pGAL1 with different UAS and its parameters. THIS EXPERIMENT CURRENTLY IS BEING PERFORMED AT OUR LABS! |
| - | + | ||
<br> | <br> | ||
<br> | <br> | ||
<br> | <br> | ||
| - | + | </div> | |
| - | <h3 id="nico"> Nickel removal | + | </div> |
| + | </div> | ||
| + | <div class="accordion-group"> | ||
| + | <div class="accordion-heading"> | ||
| + | <a class="accordion-toggle" data-toggle="collapse" data-parent="#accordion2" href="#collapseTwo"> | ||
| + | Nickel removal System </a> | ||
| + | </div> | ||
| + | <div id="collapseTwo" class="accordion-body collapse"> | ||
| + | <div class="accordion-inner"> | ||
| + | <h3 id="nico"> Nickel removal System </h3> | ||
After a research for parameters in the literature research was done the following values were found: | After a research for parameters in the literature research was done the following values were found: | ||
| - | + | </html> | |
[[File:paramNic.png|center|700x750px]] | [[File:paramNic.png|center|700x750px]] | ||
| - | + | <html> | |
<ul> | <ul> | ||
| Line 107: | Line 113: | ||
</ul> | </ul> | ||
| - | + | <b>Sensitivity Analysis</b> | |
| + | |||
| + | <br> | ||
| + | <br> | ||
A complete sensitivity analysis was performed in order to see which parameters have a significant importance in the model. Each parameter was evaluated in various orders of magnitude and the behaviour of the nickel intake was analysed. | A complete sensitivity analysis was performed in order to see which parameters have a significant importance in the model. Each parameter was evaluated in various orders of magnitude and the behaviour of the nickel intake was analysed. | ||
| + | <br> | ||
| + | <br> | ||
Most of the parameters had a significant effect. Only the rate constant for the formation of the tetramer and the repressor destruction rate were the parameters that compared with the others had little effect on the nickel intake. The first one needed to be found with the model, but after seeing that it has not a significant effect on the desired response an estimated order of magnitude is enough to run the deterministic model. | Most of the parameters had a significant effect. Only the rate constant for the formation of the tetramer and the repressor destruction rate were the parameters that compared with the others had little effect on the nickel intake. The first one needed to be found with the model, but after seeing that it has not a significant effect on the desired response an estimated order of magnitude is enough to run the deterministic model. | ||
| + | </html> | ||
[[File:repressordestructionses.png|left|950x800px]] | [[File:repressordestructionses.png|left|950x800px]] | ||
| - | + | <html> | |
| - | The repressor basal production rate is one of the parameters that needs to be found with the model. As can be seen below this parameter has a strong influence on the response of the system in a small range (0.8-1.3) . After 1.4 the response of the system is not influenced by the change of the basal production. The parameter can be | + | The repressor basal production rate is one of the parameters that needs to be found with the model. As can be seen below this parameter has a strong influence on the response of the system in a small range (0.8-1.3) . After 1.4 the response of the system is not influenced by the change of the basal production. The parameter can be chosen to be higher than 1.4 because it has no significant influence in the response and the intake of nickel is high enough. |
| - | + | </html> | |
[[File:basalprodrepanalysis.png|center|500x450px]] | [[File:basalprodrepanalysis.png|center|500x450px]] | ||
| - | + | <html> | |
As said before most of the parameters showed to have influence in the model. The parameters related to the Hill's equation: The Maximum expression rate and the cooperation constant had the highest effect on the model and its value needs to be screened. | As said before most of the parameters showed to have influence in the model. The parameters related to the Hill's equation: The Maximum expression rate and the cooperation constant had the highest effect on the model and its value needs to be screened. | ||
| - | + | </html> | |
[[File:EffectNickel.png|left|950x800px]] | [[File:EffectNickel.png|left|950x800px]] | ||
| + | Here is the [https://static.igem.org/mediawiki/2013/9/9a/NickelSensitivityAn.pdf complete document] with the results for all the parameters <html> </div> | ||
| - | + | ||
| - | < | + | </div> |
| + | </div> | ||
| + | </div> | ||
</div> | </div> | ||
</div> | </div> | ||
</html> | </html> | ||
Latest revision as of 21:27, 26 September 2013
Parameters
The second step in the deterministic model is to find the parameters that define the system. There are 3 possible ways to do this:
- Literature: Many of the genes that we use in iGEM have been characterised before, and the constants that model their behaviour have been found. Sometimes an exact value is not found, so we have to find at least the order of magnitude or a range where the parameter can be found
- Experiments: This is the most reliable way of knowing the parameters. In our case, we need them to perform the deterministic model first, in order to see if the system works as expected in the lab. If there is an estimated order of magnitude the model can be run while the exact value is found experimentally
- Screening: If the parameters we are looking of are not found in literature we proceed to perform a screening of parameters as described in Colombia 2012 Team Wiki
Glucocorticoid sensor
After looking for parameters in literature, the following values were found:
- The values for these parameters varied a lot depending on the glucocorticoid detected. As we plan to test the circuit with dexamethasone, all the parameters were adjusted for this hormone.
- The values in red are the parameters that could not be found. An estimated order of magnitude was assessed supported in the literature read.
- As the part of the chimera that is going to activate transcription is Gal4p, the values for the activation of the gene are assumed to be the same as those of the pure protein.
- Some of the destruction rates were not found so we assumed that the concentration of the protein is significantly reduced only when the cell divides.
In order to see which of the parameters had a significant effect in the signal response, we performed a sensitivity analysis. For each parameter, we took the value found in the literature (or guessed value) and evaluated the model in a range that varied from one order of magnitude below to one order of magnitude above. Depending on how much the response varied to each parameter the screening was planned.
After perfoming the analysis we found some parameters that had little effect on the desired response of the system. For example, the destruction rate of glucocorticoids inside the cell and the reverse kinetic constant for the formation of the complex:
The parameters related to the Hill expression had a significant effect on the desired response. For this reason, and given the fact that two out of three parameters had an estimated order of magnitude, the screening of parameters was focused on this part of the model.
You can see the complete sensitivity analysis here.
The parameters that need to be found with the model are those involved in the activation of the promoter by GAL4. Before this promoter exists an Upstream Activation Sequence (UAS) that enhances the production of M-CHERRY. This sequence can be as many times as wanted upstream de promoter, and depending on the number of times it exists the strength of the production is affected. Little is found in the literature about the parameters that concern to the activation by GAL4 and less is found in the difference of the parameters with the different arrangements of the UAS.
Without the parameter information of at least one arrangement of UAS the screening of parameters can not be performed. In order to solve this problem we designed an experiment, in which we tested the M-cherry's expression trough time with different UAS arrangements (1,3,6 and 9 UAS before de promoter). With the results of this procedure we aim to find an expression for the activation of the promoter pGAL1 with different UAS and its parameters. THIS EXPERIMENT CURRENTLY IS BEING PERFORMED AT OUR LABS!
Nickel removal System
After a research for parameters in the literature research was done the following values were found:
- The values in red are the parameters that could not be found. An estimated order of magnitude was assessed supported in the literature read.
- Some of the destruction rates are not found so it is assumed the concentration of the protein is significantly reduced only when the cell divides
A complete sensitivity analysis was performed in order to see which parameters have a significant importance in the model. Each parameter was evaluated in various orders of magnitude and the behaviour of the nickel intake was analysed.
Most of the parameters had a significant effect. Only the rate constant for the formation of the tetramer and the repressor destruction rate were the parameters that compared with the others had little effect on the nickel intake. The first one needed to be found with the model, but after seeing that it has not a significant effect on the desired response an estimated order of magnitude is enough to run the deterministic model.
The repressor basal production rate is one of the parameters that needs to be found with the model. As can be seen below this parameter has a strong influence on the response of the system in a small range (0.8-1.3) . After 1.4 the response of the system is not influenced by the change of the basal production. The parameter can be chosen to be higher than 1.4 because it has no significant influence in the response and the intake of nickel is high enough.
As said before most of the parameters showed to have influence in the model. The parameters related to the Hill's equation: The Maximum expression rate and the cooperation constant had the highest effect on the model and its value needs to be screened.
Here is the complete document with the results for all the parameters
