Difference between revisions of "Team:Carnegie Mellon/Modeling"

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  <div class = "description"> Along with improving the estrogen sensor part from our previous year’s project, this year we also updated the estrogen sensor model as well. We created two new models to represent the two different modifications we made to the estrogen sensor part. One model is based on using an RFP reporter, while the other model represents the incorporation of this year’s new part by using a gaussia reporter. The new models not only reflect the addition of new biological components to our wet lab sensor, but also incorporates our most recent wet lab data. The new biosensor models were written in the BioNetGen Language, a rule-based modeling language. Rule-based modeling is a type of modeling in which differential equations are generated from a description of how various biological components and systems interact with one another. Our models were built from data found in literature and experimental data from the lab. Due to the fact that our models are based on experimental data, it is able to predict the outcome of experimental wet lab trials under a variety of different conditions. Thus, our models not only help to guide wet lab experiments, but can also given us insight into some of the biological underpinnings of the experiment that we would not have necessarily considered without the model. Finally, the models can be used to identify any components which are interfering with our ability to obtain optimal data. The models were run in Rule-Bender version 2.0.382, an interactive design environment which is dedicated to running, analyzing, visualizing, and debugging BioNetGen Language Models. </div>
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<div class = "textbody"> Along with improving the estrogen sensor part from our previous year’s project, this year we alsoupdated the estrogen sensor modell. We created two new models to represent the two different modifications we made to the estrogen sensor part. One model is based on using an RFP reporter, while the other model represents the incorporation of this year’s new part by using a gaussia reporter. The new models not only reflect the addition of new biological components to our wet lab sensor, but also incorporates our most recent wet lab data. The new biosensor models were written in the BioNetGen Language, a rule-based modeling language. Rule-based modeling is a type of modeling in which differential equations are generated from a description of how various biological components and systems interact with one another. Our models were built from data found in literature and experimental data from the lab. Due to the fact that our models are based on experimental data, it is able to predict the outcome of experimental wet lab trials under a variety of different conditions. Thus, our models not only help to guide wet lab experiments, but can also given us insight into some of the biological underpinnings of the experiment that we would not have necessarily considered without the model. Finally, the models can be used to identify any components which are interfering with our ability to obtain optimal data. The models were run in Rule-Bender version 2.0.382, an interactive design environment which is dedicated to running, analyzing, visualizing, and debugging BioNetGen Language Models. </div>
 
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   <center><image src="https://static.igem.org/mediawiki/2015/9/96/RFPmodel.jpg" height="600" width="1000"></center>
 
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   Before we began writing code to generate our model, it was important to create a visualization of our model to serve as a template which we would base our code on. A legend for the components can be seen below:  
 
   Before we began writing code to generate our model, it was important to create a visualization of our model to serve as a template which we would base our code on. A legend for the components can be seen below:  
 
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     Our RFP reporter estrogen model captures a total of 16 different reactions:
 
     Our RFP reporter estrogen model captures a total of 16 different reactions:
 
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After the class definitions are provided, it is important to initialize each particular component to a predetermined value in order to begin the simulation. For components whose class definition includes multiple states, each particular instance must be initialized. Below is the initialization code used in the RFP reporter estrogen model:<br><br>
 
After the class definitions are provided, it is important to initialize each particular component to a predetermined value in order to begin the simulation. For components whose class definition includes multiple states, each particular instance must be initialized. Below is the initialization code used in the RFP reporter estrogen model:<br><br>
 
     <center><img src="https://static.igem.org/mediawiki/2015/f/fe/SeedRFP.png" height="600" width="1200"></center><br>
 
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Revision as of 20:37, 18 September 2015

Modeling.

Rule-based modeling for our estrogen sensor.

Purpose

Along with improving the estrogen sensor part from our previous year’s project, this year we alsoupdated the estrogen sensor modell. We created two new models to represent the two different modifications we made to the estrogen sensor part. One model is based on using an RFP reporter, while the other model represents the incorporation of this year’s new part by using a gaussia reporter. The new models not only reflect the addition of new biological components to our wet lab sensor, but also incorporates our most recent wet lab data. The new biosensor models were written in the BioNetGen Language, a rule-based modeling language. Rule-based modeling is a type of modeling in which differential equations are generated from a description of how various biological components and systems interact with one another. Our models were built from data found in literature and experimental data from the lab. Due to the fact that our models are based on experimental data, it is able to predict the outcome of experimental wet lab trials under a variety of different conditions. Thus, our models not only help to guide wet lab experiments, but can also given us insight into some of the biological underpinnings of the experiment that we would not have necessarily considered without the model. Finally, the models can be used to identify any components which are interfering with our ability to obtain optimal data. The models were run in Rule-Bender version 2.0.382, an interactive design environment which is dedicated to running, analyzing, visualizing, and debugging BioNetGen Language Models.



Biosensor Overview


The diagram above is a system level visualization of our RFP reporter estrogen biosensor.



The diagram above is a system level visualization of our gaussia reporter estrogen biosensor.


Before we began writing code to generate our model, it was important to create a visualization of our model to serve as a template which we would base our code on. A legend for the components can be seen below:




Rule-Based Model

Our RFP reporter estrogen model captures a total of 16 different reactions:
  1. The rate at which estrogen diffuses across the cell membrane and enters the cell.
  2. The rate at which estrogen diffuses across the cell membrane and exits the cell.
  3. The rate at which mRNA T7RNAP-LBD/YFP is transcribed from the sensor plasmid.
  4. The rate at which mRNA T7RNAP-LBD/YFP is degraded.
  5. The rate at which YFP is translated from mRNA T7RNAP-LBD/YFP.
  6. The rate at which YFP is degraded.
  7. The rate at which T7RNAP-LBD is translated from mRNA T7RNAP-LBD/YFP.
  8. The rate at which T7RNAP-LBD is degraded.
  9. The rate at which estrogen associates with the T7RNAP-LBD complex.
  10. The rate at which estrogen disassociates from the T7RNAP-LBD complex.
  11. The rate at which estrogen activated T7RNAP-LBD binds to reporter plasmid.
  12. The rate at which estrogen activated T7RNAP-LBD unbinds from reporter plasmid.
  13. The rate at which mRNA RFP is transcribed from the reporter plasmid.
  14. The rate at which mRNA RFP is degraded.
  15. The rate at which RFP is translated from mRNA gLuc.
  16. The rate at which RFP is degraded.

Our gaussia reporter estrogen model captures a total of 22 different reactions:
  1. The rate at which estrogen diffuses across the cell membrane and enters the cell.
  2. The rate at which estrogen diffuses across the cell membrane and exits the cell.
  3. The rate at which mRNA T7RNAP-LBD/YFP is transcribed from the sensor plasmid.
  4. The rate at which mRNA T7RNAP-LBD/YFP is degraded.
  5. The rate at which YFP is translated from mRNA T7RNAP-LBD/YFP.
  6. The rate at which YFP is degraded.
  7. The rate at which T7RNAP-LBD is translated from mRNA T7RNAP-LBD/YFP.
  8. The rate at which T7RNAP-LBD is degraded.
  9. The rate at which estrogen associates with the T7RNAP-LBD complex.
  10. The rate at which estrogen disassociates from the T7RNAP-LBD complex.
  11. The rate at which estrogen activated T7RNAP-LBD binds to reporter plasmid.
  12. The rate at which estrogen activated T7RNAP-LBD unbinds from reporter plasmid.
  13. The rate at which mRNA Gluc is transcribed from the reporter plasmid.
  14. The rate at which mRNA Gluc is degraded.
  15. The rate at which Gluc enzyme is translated from mRNA gLuc.
  16. The rate at which Gluc enzyme is degraded.
  17. The rate at which Gluc diffuses across the cell membrane and enters the cell.
  18. The rate at which Gluc diffuses across the cell membrane and exits the cell.
  19. The rate at which coelenterazine associates with the Gluc enzyme.
  20. The rate at which coelenterazine disassociates from the Gluc enzyme.
  21. The rate at which light and coelenteramide are produced from the coelenterazine-luciferase complex.
  22. The rate at which light dissipates from the cellular environment.

Before the written descriptions of the rules can be transformed into code, it is imperative that a class definition for each of the components be instantiated, as the model needs to know which cellular components must be included in the module, and which components are not being directly analyzed. A class definition essentially specifies the name of the component as it will appear in the code, all the possible binding sites of the component and to which molecules it can bind, and the possible states of the component (i.e., phosphorylation state, methylation state, etc.). Below is the class definition used for the RFP reporter estrogen model:


In the BioNetGen code estrogen will now be represented as E(). The full class definition of estrogen is E(S~U~B,L~I~O). The S~U~B, indicates that the estrogen can either be bound or unbound to the ligand binding domain of T7 RNA polymerase, and the L~I~O indicates that the estrogen can either be inside or outside of the cell.

After the class definitions are provided, it is important to initialize each particular component to a predetermined value in order to begin the simulation. For components whose class definition includes multiple states, each particular instance must be initialized. Below is the initialization code used in the RFP reporter estrogen model: