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

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   <h1><center><font size="20"> Purpose </font></center></h1>
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   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.
 
   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.
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  <h1><center><font size="26"> Bacteria Sensor Overview </font></center></h1>
 
   <center><image src="https://static.igem.org/mediawiki/2015/4/40/EstrogenLucModel.jpg"></center>
 
   <center><image src="https://static.igem.org/mediawiki/2015/4/40/EstrogenLucModel.jpg"></center>
 
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Revision as of 22:33, 13 September 2015

Under Construction.

Modeling.

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