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. The new model not only reflects the addition of new biological components to our wet lab sensor, but also incorporates our most recent wet lab data. The new bacterial cell model was again 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 model was built from data found in literature and experimental data from the lab. Due to the fact that our model is based on experimental data, it is able to predict the outcome of experimental wet lab trials under a variety of different conditions. This not only helps 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 model can be used to identify any components which are interfering with our ability to obtain optimal data. The model was 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>
 
   <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. The new model not only reflects the addition of new biological components to our wet lab sensor, but also incorporates our most recent wet lab data. The new bacterial cell model was again 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 model was built from data found in literature and experimental data from the lab. Due to the fact that our model is based on experimental data, it is able to predict the outcome of experimental wet lab trials under a variety of different conditions. This not only helps 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 model can be used to identify any components which are interfering with our ability to obtain optimal data. The model was 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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Revision as of 15:41, 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 also updated the estrogen sensor model as well. The new model not only reflects the addition of new biological components to our wet lab sensor, but also incorporates our most recent wet lab data. The new bacterial cell model was again 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 model was built from data found in literature and experimental data from the lab. Due to the fact that our model is based on experimental data, it is able to predict the outcome of experimental wet lab trials under a variety of different conditions. This not only helps 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 model can be used to identify any components which are interfering with our ability to obtain optimal data. The model was run in Rule-Bender version 2.0.382, an interactive design environment which is dedicated to running, analyzing, visualizing, and debugging BioNetGen Language Models.



Bacteria Sensor Overview

The diagram above is system level description of our bacteria sensor. 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. It is important to note that each single arrow represents a possible reaction, and a double arrow indicates the reaction can proceed in both directions.




Rule-Based Model
Our 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 coelenterazine diffuses across the cell membrane and enters the cell.
  18. The rate at which coelenterazine 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.