Team:Carnegie Mellon/Device
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. 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
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
The general outline for constructing a rule-based model is shown 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:
The rate at which estrogen diffuses through membrane was found indirectly via the synthesis of literature values from a variety of sources. One of the most difficult tasks of modeling involves parsing through multiple pieces of literature in order to derive a particular rate constant. A common problem in constructing biological models is that individuals with strong computational backgrounds are not always well versed in sorting through wet lab literature for relevant information. Luckily iGEM stresses the intersectionality of synthetic biology, and our diverse team was able to overcome this issue as the modelers were able to tell those with strong wet lab backgrounds exactly what they needed from the literature, and in turn the wet lab students were able to efficiently parse through the literature to find the relevant data. It is shown below exactly how the cell's permeability to estrogen was derived:
A similar process was used to computer many of the other rate constants. However, certain rate constants had to be lifted from our team’s wet lab trials. For example, the diffusion of estrogen is not only dependent on the cell membrane’s permeability to estrogen, but also upon the estrogen concentration gradient. As the initial concentration of estrogen outside the cell was set in the wet lab, it was imperative for the team to run wet lab trials with various concentrations of estrogen in order to confirm the model’s correctness.
The final step of constructing a rule-based model is to compile all of the aforementioned information into single lines of executable code, which correspond to a reaction rule. This can be seen below for the RFP reporter estrogen model:
Each of the above lines will be turned into a differential equation or sets of differential equations, and run for a specified amount of cycles.
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:
The rate at which estrogen diffuses through membrane was found indirectly via the synthesis of literature values from a variety of sources. One of the most difficult tasks of modeling involves parsing through multiple pieces of literature in order to derive a particular rate constant. A common problem in constructing biological models is that individuals with strong computational backgrounds are not always well versed in sorting through wet lab literature for relevant information. Luckily iGEM stresses the intersectionality of synthetic biology, and our diverse team was able to overcome this issue as the modelers were able to tell those with strong wet lab backgrounds exactly what they needed from the literature, and in turn the wet lab students were able to efficiently parse through the literature to find the relevant data. It is shown below exactly how the cell's permeability to estrogen was derived:
A similar process was used to computer many of the other rate constants. However, certain rate constants had to be lifted from our team’s wet lab trials. For example, the diffusion of estrogen is not only dependent on the cell membrane’s permeability to estrogen, but also upon the estrogen concentration gradient. As the initial concentration of estrogen outside the cell was set in the wet lab, it was imperative for the team to run wet lab trials with various concentrations of estrogen in order to confirm the model’s correctness.
The final step of constructing a rule-based model is to compile all of the aforementioned information into single lines of executable code, which correspond to a reaction rule. This can be seen below for the RFP reporter estrogen model:
Each of the above lines will be turned into a differential equation or sets of differential equations, and run for a specified amount of cycles.
Results
Unlike the intein-based estrogen sensor from this year, the ligand binding domain based estrogen sensor is able to produce a detectable RFP signal as shown in the graph below:
The units for the x-axis are time (minutes), and the units of the y-axis are concentration (nM). Unlike last year’s sensor, the amount of RFP produced within a few hours is significantly greater than the minimum threshold detection of 100 µm. Thus the model is able to be successfully validated via our results from the wet lab.
The units for the x-axis are time (minutes), and the units of the y-axis are concentration (nM). Unlike last year’s sensor, the amount of RFP produced within a few hours is significantly greater than the minimum threshold detection of 100 µm. Thus the model is able to be successfully validated via our results from the wet lab.
Code Files
Working BioNetGen files of the two estrogen models can be found in the following zip file.
References
