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

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.

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:

Our RFP reporter estrogen model captures a total of 16 different reactions:


Our gaussia reporter estrogen model captures a total of 22 different reactions:


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:



The final step before assembling the reaction rules, is to determine the rate constants of each reaction. Certain rate constants can be obtained directly from literature, whereas others must be approximated as a function of literature values and our experimental data. The code for the rate constants in the RFP reporter estrogen model is shown below:



The rate constants for the diffusion of estrogen into and out of the cell are provided by the equations below: