{% extends "layout.html" %} {% block title %}ENGINEERING{% endblock %} {% block lead %}OUR SUCCESS AND CHALLENGES WITH THE ENGINEERING CYCLE{% endblock %} {% block page_content %}
A Virtual Cell (VCell) is a useful tool to simulate the reactions of complex reaction pathways to compute the predicted concentrations of each species at any given point in time. It is capable of deterministically simulating reactions with differential equations and mass-action kinetics or stochastically by probabilistically simulating individual chemical reactions based on collisions. Our aim with Vcell this year was to computationally predict which gene construct would be the most effective in producing GFP in the presence of PFAS. We only simulated a single cell for time and simplicity, however, future works could expand on our work by simulating multiple cells and testing new pathways for PFAS detection.
In Cycle 1, we first had to build the structure for the future cycles. First, we had to research the constructs we had to design to find out how to start constructing them. We decided to first look at the pRMA_GFP construct and end with the pLex_GFP construct. We first started with the design of the constructs. Each construct had the same geometry.
Geometry:
Cell Size: 3.5 uM^3
Environment: 1000 uM^3
Once a construct was designed we would input rate constants, and run stochastic simulations with the construct. In the simulations, we would use varying amounts of PFAS and see how much GFP was produced.
Simulation Parameters:
Time: 10,000 seconds
PFAS uM : 0.01, 0.1, 0, 1, 5, 10, 50
Solver: Gibson
Most rate constants were estimated and few were taken from the literature review. This is a problem we would like to address in future studies, ase construct would produce more SF_GFP. There is a direct relationship between PFAS concentrations and SF_GFP production. This construct helped us prepare for future models as they followed a similar structure. However, since most of the rates were estimated, we can not say for certain how accurate this model is. In future studies, we will look for more rate constants to have a higher accuracy in the output of this model.
We designed the reaction network building off last year reaction network PFAS Detector V2. We changed the reaction diagram so that when the pRMA_operon and the PFAS bonded and produced the complex the complex would produce the GFP. However, the GFP we used in our reaction diagram was a variant of GFP called SF_GFP(Super-folder GFP). Some reactions were taken from literature or pulled from the PFAS DetectorV2 Biomodel but others were estimated. This construct was the first one of the 4 constructs we diagramed and simulated on Vcell and this construct came as a learning guide for the creation of the future constructs.
Cell volume was chosen as 3.5 uM^3 while the Environment volume was set to 1000 uM^3 and time was measured in seconds.
Model name: pRMA_GFP construct 1 v1.3
Owner: Pillow123
pRMA_GFP Construct #1:
We ran the model with a variety of different starting conditions with stochastic solvers. We were able to visualize SuperFolder GFP or SF_GFP production in response to varying levels of PFAS.
The graph was made using the Google Sheets platform by extracting the data tables from VCell and pasting them onto sheets. We were then able to create a graph with multiple independent variables.
We can see from the data that having more micromolars of PFAS into the system the construct would produce more SF_GFP. There is a direct relationship between PFAS concentrations and SF_GFP production. This construct helped us prepare for future models as they followed a similar structure. However, since most of the rates were estimated, we can not say for certain how accurate this model is. In future studies, we will look for more rate constants to have a higher accuracy in the output of this model.
We designed the reaction network building on a paper we found on this topic. The FAB_GFP gene would be constantly produced by the pConst promoter and would bind with the PFAS to produce the complex. Similar to the construct before most rate constants were estimated however the binding affinity between the PFAS and the FAB_GFP protein was calculated and other rate constants were pulled from literature studies. This was the 2nd of the 4 constructs we had and it provided a base design for future constructs that we designed.
Cell volume was chosen as 3.5 uM^3 while the Environment volume was set to 1000 uM^3 and time was measured in seconds.
Model name: FAB_GFP_Construct v1.1
Owner: Pillow123
pRMA_GFP Construct #1:
The results for the PFAS_FAB_GFP were different than the pRMA_GFP construct but the style of the simulations was the same. All the same, constants were used (1000 seconds and the same solver). There were different rate constants overall showing differences in numbers but both of them provided convincing results.
The graph was also made using Google Sheets. The data was imported from Vcell as an HDF5 file and then put onto Google Sheets, where the graph was made. The graph used several ranges allowing for us to have several lines. The legend on the side helps improve the comprehensiveness of the graph and readability. Additionally with variety of colors can increase the readability of the graph as lines are now distinguished from one another.
We can see a direct relationship between the amount of micromolars of PFAS and the amount of PFAS_FAB_GFP being produced. The higher the micromolar count the higher count of PFAS_FAB_GFP. This is similar to the pRMA_GFP graph which shows a common relationship . This model helped become a model for future constructs and helped provide rate constants for future graphs.
This construct had a similar design to the FAB_GFP construct however the main difference is that when PFAS and the Synt Transcription Factor were binded they produced GFP. This model was where the FAB_GFP model helped a lot. Rate constants were estimated and similar to those of the FAB_GFP model but the binding affinity of PFAS and Synt_Tran factor was calculated. This was the 3rd of the 4 constructs with the last being pLex_GFP
Cell volume was chosen as 3.5 uM^3 while the Environment volume was set to 1000 uM^3 and time was measured in seconds.
Model name: Synthetic_Transcription_factorv1.1
Owner: Pillow123
SyntTranscriptionFactor_GFP Construct #3:
We ran stochastic simulations on the reaction diagram with varying micromolars of PFAS and ran to see its effect on the GFP protein. Different rate constants were used which caused variance in the amount of GFP produced between the constructs.
Unlike the other graphs, we can see in all the uM values of PFAS that there were near 0 for the first 100 seconds. Then they all separated around 1250 seconds.
We can see like all other graphs that this one had a direct relationship. The more PFAS in the system the more GFP was produced. There was no promoter leak so PFAS 0uM stayed at 0uM which makes logical sense. A future study on this construct would be to see if making it a dual construct (constructs 3 & 4) would optimize the detection of PFAS.
We designed the reaction network building off of last year’s reaction network PFAS Detector V2 similar to the pRMA_GFP construct. We simply replaced the pRMA_operon with the pLex_operon and changed the GFP variant. In this construct, unlike the 1st construct, the GFP variant will simply be GFP, unlike the SF_GFP variant used in the pRMA_GFP construct. Also like the pRMA_operon the pLex_operon has a leak which we estimated using scientific studies.The pLex_operon would bind with the PFAS to create the complex which would then produce the GFP_mRNA.
Cell volume was chosen as 3.5 uM^3 while the Environment volume was set to 1000 uM^3 and time was measured in seconds.
Model name: pLex_GFP Construct
Owner: Pillow123
pLex_GFP Construct #4:
We ran stochastic simulations on the reaction diagram with varying micromolars of PFAS and ran to see its effect on the GFP protein. Different rate constants were used which caused variance in the amount of GFP produced between the constructs.
We can see that each PFAS uM value has a varying amount at the 2000-second mark but all start to curve from there too. The 2000-second mark represents their “max”.
With this last construct, we can analyze all the graphs and come to a conclusion on which pathway was the most successful in detecting PFAS. However, additionally, cycles may be necessary as the rate constants were heavily estimated and few were based on scientific research. However, looking at the graph we can see that the 0.01uM graph produced the least while the 50 uM produced the most showing a direct relationship between the uM of PFAS and the GFP count. Additionally, due to a promoter leak, the 0uM produced more than the 0.1uM and 0.01uM. This was also the only graph that produced a true stochastic curve, while the others produced exponential graphs.