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{% extends "layout.html" %}
{% block title %}ENGINEERING{% endblock %}
{% block lead %}OUR SUCCESS AND CHALLENGES WITH THE ENGINEERING CYCLE{% endblock %}
@@ -17,9 +17,11 @@
</div>
</div>
-->
<head>
</head>
<body>
<div class="row mt-4">
@@ -30,6 +32,8 @@
<ul class="nav flex-column"> <!--YOU CAN ADD MORE OVERVIEW LINKS HERE, SIMPLY DUPLICATE AN UNORDERED LIST-->
<li class="nav-item">
<a href="#Part-engineering">Part Engineering</a>
</li>
<li>
<a class="nav-link" href="#Vcell-Documentation">Vcell Engineering</a>
<ul class="nav flex-column ml-3">
<li class="nav-item">
@@ -53,12 +57,14 @@
<a class="nav-link" href="#Cycle-3">Cycle 3</a>
</li>
</ul>
</li>
<li>
<a href="#AI">AI initiatives</a>
</li>
</ul>
<ul class="nav flex-column">
<li>
</li>
</ul>
</div>
@@ -67,76 +73,143 @@
<div class="col-lg-9">
<div id="Part-engineering">
<h3>Design</h3>
<p><u>Goal:</u> Our project’s goal was to effectively detect PFAS (specifically, PFOA) by utilizing e. coli for detection.</p>
<p><u>Goal:</u> Our project’s goal was to effectively detect PFAS (specifically, PFOA) by utilizing e. coli for
detection.</p>
<p>With this, we design 3 main pathways:
</p>
<ol>
<li>prmA Promoter construct: We tested the pRMA last year with Rhodococcus jostii, however we weren’t able to get significant results. We retested this promoter this year with e. coli, placing it upstream of a superfolder GFP gene to see if e. coli would fluoresce when exposed to PFOA.</li>
<li>FAB-GFP Conjugate construct: Using the FAB-GFP conjugate molecule created by Dr. Bryan Berger, initially meant to react to fatty acids, we designed a system that would theoretically detect PFAS binding, initiating GFP fluorescence. We hypothesized that PFAS would bind similarly to fatty acids due to receptor similarities.
<li>prmA Promoter construct: We tested the pRMA last year with Rhodococcus jostii, however we weren’t able to
get significant results. We retested this promoter this year with e. coli, placing it upstream of a
superfolder GFP gene to see if e. coli would fluoresce when exposed to PFOA.</li>
<li>FAB-GFP Conjugate construct: Using the FAB-GFP conjugate molecule created by Dr. Bryan Berger, initially
meant to react to fatty acids, we designed a system that would theoretically detect PFAS binding, initiating
GFP fluorescence. We hypothesized that PFAS would bind similarly to fatty acids due to receptor
similarities.
</li>
<li>Synthetic Transcription Factor construct: Based on work done by Dr. Dossani in yeast, we designed a system of two plasmids: one containing a synthetic transcription factor that would respond to estradiol and another containing a hybrid promoter linked to GFP. Since there was already some research indicating that PFAS could interact with estradiol receptors, we wanted to see whether this system would function in e. coli as a method of PFAS detection.
<li>Synthetic Transcription Factor construct: Based on work done by Dr. Dossani in yeast, we designed a system
of two plasmids: one containing a synthetic transcription factor that would respond to estradiol and another
containing a hybrid promoter linked to GFP. Since there was already some research indicating that PFAS could
interact with estradiol receptors, we wanted to see whether this system would function in e. coli as a
method of PFAS detection.
</li>
</ol>
<h3>Build</h3>
<p>All part sequences are deposited into the registry and are publicly available. All genes were printed from Genscript and subcloned into pUC57-Kan or pUC57</p>
<p>All part sequences are deposited into the registry and are publicly available. All genes were printed from
Genscript and subcloned into pUC57-Kan or pUC57</p>
<ol>
<li>pRMA Promoter construct: The pRMA promoter construct was transformed into e. coli and plated on ampicillin-selective agar plates. After heat shock transformation, successful colonies were identified via colony screening, followed by plasmid extraction using a miniprep. Nanodrop analysis indicated DNA concentrations ranging from 200-300 ng/µL, confirming successful plasmid isolation for further testing in PFAS detection experiments.
<li>pRMA Promoter construct: The pRMA promoter construct was transformed into e. coli and plated on
ampicillin-selective agar plates. After heat shock transformation, successful colonies were identified via
colony screening, followed by plasmid extraction using a miniprep. Nanodrop analysis indicated DNA
concentrations ranging from 200-300 ng/µL, confirming successful plasmid isolation for further testing in
PFAS detection experiments.
</li>
<li>FAB-GFP Conjugate construct: The FAB-GFP conjugate plasmid was transformed into e. coli and plated on kanamycin-selective agar. White colonies were selected through blue-white screening, indicating successful transformation. Plasmid DNA was extracted using miniprep, with concentrations ranging from 170-350 ng/µL. A restriction digest using Eco31i enzyme confirmed the integrity of the construct, preparing it for PFAS-binding efficacy tests.
<li>FAB-GFP Conjugate construct: The FAB-GFP conjugate plasmid was transformed into e. coli and plated on
kanamycin-selective agar. White colonies were selected through blue-white screening, indicating successful
transformation. Plasmid DNA was extracted using miniprep, with concentrations ranging from 170-350 ng/µL. A
restriction digest using Eco31i enzyme confirmed the integrity of the construct, preparing it for
PFAS-binding efficacy tests.
</li>
<li>Synthetic Transcription Factor construct 1 (Kanamycin): The synthetic transcription factor (STF) construct was transformed into e. coli and screened via blue-white colony screening. White colonies resistant to both antibiotics were selected, and plasmid DNA concentrations ranged from 160-250 ng/µL after miniprep. A restriction digest confirmed the proper assembly of the STF construct, facilitating its use in estradiol receptor interaction experiments for PFAS detection.
<li>Synthetic Transcription Factor construct 1 (Kanamycin): The synthetic transcription factor (STF) construct
was transformed into e. coli and screened via blue-white colony screening. White colonies resistant to both
antibiotics were selected, and plasmid DNA concentrations ranged from 160-250 ng/µL after miniprep. A
restriction digest confirmed the proper assembly of the STF construct, facilitating its use in estradiol
receptor interaction experiments for PFAS detection.
</li>
<li>Synthetic Transcription Factor construct 2 (Ampicillin): The STF construct with ampicillin resistance was transformed into e. coli. Following colony screening, white colonies were cultured, and plasmid DNA was extracted using miniprep. Nanodrop analysis confirmed DNA concentrations, and a restriction digest verified the structural integrity of the construct, readying it for testing in PFAS detection through synthetic transcription factor mechanisms.
<li>Synthetic Transcription Factor construct 2 (Ampicillin): The STF construct with ampicillin resistance was
transformed into e. coli. Following colony screening, white colonies were cultured, and plasmid DNA was
extracted using miniprep. Nanodrop analysis confirmed DNA concentrations, and a restriction digest verified
the structural integrity of the construct, readying it for testing in PFAS detection through synthetic
transcription factor mechanisms.
</li>
</ol>
<h3>Test</h3>
<ol>
<li>pRMA Promoter construct:After successfully transforming the e. coli with the pRMA promoter construct, we tested its ability to induce GFP expression in response to PFAS exposure. A 96-well plate was prepared with E. coli cultures containing the pRMA promoter construct and exposed to varying concentrations of PFOA. Fluorescence readings were taken every 90 minutes using a plate reader over a 12-hour period.
<li>pRMA Promoter construct:After successfully transforming the e. coli with the pRMA promoter construct, we
tested its ability to induce GFP expression in response to PFAS exposure. A 96-well plate was prepared with
E. coli cultures containing the pRMA promoter construct and exposed to varying concentrations of PFOA.
Fluorescence readings were taken every 90 minutes using a plate reader over a 12-hour period.
</li>
<li>FAB-GFP Conjugate construct: To test the efficacy of the FAB-GFP construct in detecting PFAS, e. coli cultures containing the construct were exposed to increasing concentrations of PFOA in a 96-well plate format. Fluorescence was measured at 90-minute intervals for a 12-hour period.
<li>FAB-GFP Conjugate construct: To test the efficacy of the FAB-GFP construct in detecting PFAS, e. coli
cultures containing the construct were exposed to increasing concentrations of PFOA in a 96-well plate
format. Fluorescence was measured at 90-minute intervals for a 12-hour period.
</li>
<li>Synthetic Transcription Factor construct 1 (Kanamycin): The synthetic transcription factor (STF) construct with kanamycin resistance was tested for its response to estradiol receptor activation in the presence of PFAS. Cultures of e. coli containing the STF construct were grown in 96-well plates and exposed to various concentrations of PFOA (0 to 1000 ppm). Fluorescence measurements were recorded every 90 minutes using a plate reader.
<li>Synthetic Transcription Factor construct 1 (Kanamycin): The synthetic transcription factor (STF) construct
with kanamycin resistance was tested for its response to estradiol receptor activation in the presence of
PFAS. Cultures of e. coli containing the STF construct were grown in 96-well plates and exposed to various
concentrations of PFOA (0 to 1000 ppm). Fluorescence measurements were recorded every 90 minutes using a
plate reader.
</li>
<li>Synthetic Transcription Factor construct 2 (Ampicillin): The synthetic transcription factor (STF) construct with ampicillin resistance was tested for its response to estradiol receptor activation in the presence of PFAS. Cultures of e. coli containing the STF construct were grown in 96-well plates and exposed to various concentrations of PFOA (0 to 1000 ppm). Fluorescence measurements were recorded every 90 minutes using a plate reader.
<li>Synthetic Transcription Factor construct 2 (Ampicillin): The synthetic transcription factor (STF)
construct with ampicillin resistance was tested for its response to estradiol receptor activation in the
presence of PFAS. Cultures of e. coli containing the STF construct were grown in 96-well plates and exposed
to various concentrations of PFOA (0 to 1000 ppm). Fluorescence measurements were recorded every 90 minutes
using a plate reader.
</li>
</ol>
<p>After testing, there was no significant difference in fluorescence as the amount of PFOA added increased. This applies to every construct we tested (pRMA, FAB-GFP, STF 1/2). For a detailed look at results, please refer to our results page.
<p>After testing, there was no significant difference in fluorescence as the amount of PFOA added increased.
This applies to every construct we tested (pRMA, FAB-GFP, STF 1/2). For a detailed look at results, please
refer to our results page.
</p>
<h3>Learn</h3>
<p>Since there was no significant difference in fluorescence as the amount of PFOA added increased, more testing is needed. Additionally, we needed to validate that the proteins were correctly expressed.
</p>
<p>Since there was no significant difference in fluorescence as the amount of PFOA added increased, more testing
is needed. Additionally, we needed to validate that the proteins were correctly expressed.
</p>
</div>
<div id="Vcell-Documentation" class="mt-4">
<h2>VCell Engineering</h2>
<h3>Overall Purpose</h3>
<p>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.</p>
<p>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.</p>
<p> </p>
<hr>
<div id="Cycle-1">
<h2>Cycle 1</h2>
<p>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.</p>
<p> </p>
<p>Geometry:</p>
<p style="text-indent: 5%;"> Cell Size: 3.5 uM^3</p>
<p style="text-indent: 5%;"> Environment: 1000 uM^3</p>
<p> Cell size was bassed on <a href="https://bionumbers.hms.harvard.edu/bionumber.aspx?s=n&v=3&id=100003 ">Harvard Bionumbers</a></p>
<p>Environment size was determined by taking the reciprocal of the approximate max density of E. coli in batch culture, about 10^9 cells per milliliter of water (Tuttle et al., 2021), which is about 1000um^3.</p>
<p>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.</p>
<p> </p>
<p>Simulation Parameters: </p>
<p style="text-indent: 5%;"> Time: 10,000 seconds</p>
<p style="text-indent: 5%;"> PFAS uM : 0.01, 0.1, 0, 1, 5, 10, 50</p>
<p style="text-indent: 5%;"> Solver: Gibson</p>
<p> </p>
<p>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.</p>
<h2>Cycle 1</h2>
<p>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.</p>
<p> </p>
<p>Geometry:</p>
<p style="text-indent: 5%;"> Cell Size: 3.5 uM^3</p>
<p style="text-indent: 5%;"> Environment: 1000 uM^3</p>
<p> Cell size was bassed on <a
href="https://bionumbers.hms.harvard.edu/bionumber.aspx?s=n&v=3&id=100003 ">Harvard Bionumbers</a></p>
<p>Environment size was determined by taking the reciprocal of the approximate max density of E. coli in batch
culture, about 10^9 cells per milliliter of water (Tuttle et al., 2021), which is about 1000um^3.</p>
<p>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.</p>
<p> </p>
<p>Simulation Parameters: </p>
<p style="text-indent: 5%;"> Time: 10,000 seconds</p>
<p style="text-indent: 5%;"> PFAS uM : 0.01, 0.1, 0, 1, 5, 10, 50</p>
<p style="text-indent: 5%;"> Solver: Gibson</p>
<p> </p>
<p>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.</p>
</div>
<div id="prma-GFP">
<h2>Construct 1: prma_GFP</h2>
<p> </p>
<h3>Design</h3>
<p>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.</p>
<p>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.
</p>
<p> </p>
<p>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.</p>
<p>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.</p>
<p> </p>
<p>Model name: pRMA_GFP construct 1 v1.3</p>
<p>Owner: Pillow123</p>
@@ -144,134 +217,206 @@
<p> </p>
<h3>Build</h3>
<p>pRMA_GFP Construct #1:</p>
<img src="https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105032.png" alt="pRMA_GFP Biomodel Image">
<img src="https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105032.png"
alt="pRMA_GFP Biomodel Image">
<p> </p>
<p> </p>
<h3>Test & Results</h3>
<p>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.</p>
<img src="https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105104.png" alt="pRMA_GFP graph">
<p>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.</p>
<p>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.</p>
<img src="https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105104.png"
alt="pRMA_GFP graph">
<p>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.</p>
<p> </p>
<h10>Conclusion and Learnings</h10>
<p> </p>
<p>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.</p>
</div>
<div id="fab-GFP">
<h2>Construct 2: FAB_GFP</h2>
<p> </p>
<h3>Design</h3>
<p>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.</p>
<p> </p>
<p>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.</p>
<p> </p>
<p>Model name: FAB_GFP_Construct v1.1</p>
<p>Owner: Pillow123</p>
<p> </p>
<p> </p>
<h3>Build</h3>
<p>pRMA_GFP Construct #1:</p>
<img src="https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105125.png" alt="FAB_GFP biomodel">
<p> </p>
<p> </p>
<h3>Test & Results</h3>
<p>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.</p>
<img src="https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105140.png" alt="FAB_GFP data graph">
<p>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.</p>
<p> </p>
<p> </p>
<h3>Conclusion and Learnings</h3>
<p>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.</p>
</div>
<div id="synt-tran">
<h2>Construct 3: Synthetic Transcription Factor</h2>
<p>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.</p>
</div>
<div id="fab-GFP">
<h2>Construct 2: FAB_GFP</h2>
<p> </p>
<h3>Design</h3>
<p>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.</p>
<p> </p>
<p>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.</p>
<p> </p>
<p>Model name: FAB_GFP_Construct v1.1</p>
<p>Owner: Pillow123</p>
<p> </p>
<p> </p>
<h3>Build</h3>
<p>pRMA_GFP Construct #1:</p>
<img src="https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105125.png"
alt="FAB_GFP biomodel">
<p> </p>
<p> </p>
<h3>Test & Results</h3>
<p>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.</p>
<img src="https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105140.png"
alt="FAB_GFP data graph">
<p>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.</p>
<p> </p>
<p> </p>
<h3>Conclusion and Learnings</h3>
<p>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.</p>
</div>
<div id="synt-tran">
<h2>Construct 3: Synthetic Transcription Factor</h2>
<p> </p>
<h3>Design</h3>
<p>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</p>
<p> </p>
<p>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.</p>
<p> </p>
<p>Model name: Synthetic_Transcription_factorv1.1</p>
<p>Owner: Pillow123</p>
<p> </p>
<p> </p>
<h3>Build</h3>
<p>SyntTranscriptionFactor_GFP Construct #3:</p>
<img src="https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105200.png"
alt="Synt_Tran biomodel">
<p> </p>
<p> </p>
<h3>Test & Results</h3>
<p>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.</p>
<img src="https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105213.png"
alt="Synt_Tran data graph">
<p>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.</p>
<p> </p>
<p> </p>
<h3>Conclusion and Learnings</h3>
<p>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.</p>
</div>
<div id="pLex_GFP">
<h2>Construct 4: pLex_GFP</h2>
<p> </p>
<h2>Design</h2>
<p>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.</p>
<p> </p>
<p>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.</p>
<p> </p>
<p>Model name: pLex_GFP Construct</p>
<p>Owner: Pillow123</p>
<p> </p>
<p> </p>
<h3>Build</h3>
<p>pLex_GFP Construct #4:</p>
<img src="https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105225.png"
alt="pLex_GFP biomodel">
<h3>Test & Results</h3>
<p>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.</p>
<img src="https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105241.png"
alt="pLex_GFP data graph">
<p>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”.</p>
<p> </p>
<p> </p>
<h3>Conclusion and Learnings</h3>
<p>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.</p>
<div id="construct_2" class="mt-4">
<h2>VCell Modeling Cycle 2</h2>
<h2>Construct 3: SyntTranscriptionFactor_GFP</h2>
<p> </p>
<h3>Design</h3>
<p>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</p>
<p>Further research shows that a combination of constructs 3 & 4 enhances the production of GFP in the
presence of PFAS. Due to an error in the creation of constructs 3 and 4 which were supposed to work
together to produce the GFP we had to recreate construct 3 which is a combination of the 2 constructs.
When PFAS is introduced into the system and binds with the Synthetic Transcription factor it allows it to
bind to the pLex promoter which induces transcription. Using this information a new model was developed
using previous rate constants from the models and new simulations were run.
</p>
<p> </p>
<p>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.</p>
<p>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.</p>
<p> </p>
<p>Model name: Synthetic_Transcription_factorv1.1</p>
<p>Owner: Pillow123</p>
<p>Model name: Construct 3_Cycle 2</p>
<p>Owner: Aryanshah16</p>
<p> </p>
<p> </p>
<h3>Build</h3>
<p>SyntTranscriptionFactor_GFP Construct #3:</p>
<img src="https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105200.png" alt="Synt_Tran biomodel" >
<p>SyntTranscriptionFactor Construct:</p>
<img src="https://static.igem.wiki/teams/5114/aryan/kvekvk.png" alt="SyntTranscriptionFactor_GFP">
<p>The previous construct 3 and 4 were combined into one Biomodel.</p>
<p> </p>
<p> </p>
<h3>Test & Results</h3>
<p>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.</p>
<img src="https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105213.png" alt="Synt_Tran data graph">
<p>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.</p>
<p> </p>
<p>We ran stochastic simulations on the reaction diagram with varying micromolars of PFAS and ran to see its
effect on the GFP protein production. Different rate constants were used which caused variance in the
amount of GFP produced between the constructs.</p>
<img src="https://static.igem.wiki/teams/5114/aryan/vjvejievrji.png" alt="GFP_Graph">
<p>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.</p>
<p> </p>
<h3>Conclusion and Learnings</h3>
<p>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.</p>
</div>
<div id="pLex_GFP">
<h2>Construct 4: pLex_GFP</h2>
<p> </p>
<h2>Design</h2>
<p>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.</p>
<p> </p>
<p>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.</p>
<p> </p>
<p>Model name: pLex_GFP Construct</p>
<p>Owner: Pillow123</p>
<p> </p>
<p> </p>
<h3>Build</h3>
<p>pLex_GFP Construct #4:</p>
<img src="https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105225.png" alt="pLex_GFP biomodel">
<h3>Test & Results</h3>
<p>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.</p>
<img src="https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105241.png" alt="pLex_GFP data graph">
<p>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”.</p>
<p> </p>
<p> </p>
<h3>Conclusion and Learnings</h3>
<p>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.</p>
<div id="construct_2" class="mt-4">
<h2>VCell Modeling Cycle 2</h2>
<h2>Construct 3: SyntTranscriptionFactor_GFP</h2>
<p> </p>
<h3>Design</h3>
<p>Further research shows that a combination of constructs 3 & 4 enhances the production of GFP in the presence of PFAS. Due to an error in the creation of constructs 3 and 4 which were supposed to work together to produce the GFP we had to recreate construct 3 which is a combination of the 2 constructs. When PFAS is introduced into the system and binds with the Synthetic Transcription factor it allows it to bind to the pLex promoter which induces transcription. Using this information a new model was developed using previous rate constants from the models and new simulations were run.
</p>
<p> </p>
<p>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.</p>
<p> </p>
<p>Model name: Construct 3_Cycle 2</p>
<p>Owner: Aryanshah16</p>
<p> </p>
<p> </p>
<h3>Build</h3>
<p>SyntTranscriptionFactor Construct:</p>
<img src="https://static.igem.wiki/teams/5114/aryan/kvekvk.png" alt="SyntTranscriptionFactor_GFP">
<p>The previous construct 3 and 4 were combined into one Biomodel.</p>
<p> </p>
<p> </p>
<h3>Test & Results</h3>
<p>We ran stochastic simulations on the reaction diagram with varying micromolars of PFAS and ran to see its effect on the GFP protein production. Different rate constants were used which caused variance in the amount of GFP produced between the constructs.</p>
<img src="https://static.igem.wiki/teams/5114/aryan/vjvejievrji.png" alt="GFP_Graph">
<p>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.</p>
<p> </p>
<h3>Conclusion and Learnings</h3>
<p> </p>
<p>Going through the rate constant, where PFOA binds to the synthetic transcription factor, it directly correlates with the amount of GFP produced. Multiplying the rate constant by ½ would result in a 50% decrease in GFP production, etc. GFP was produced at similar amounts based on each volume of PFAS.</p>
</div>
<p>Going through the rate constant, where PFOA binds to the synthetic transcription factor, it directly
correlates with the amount of GFP produced. Multiplying the rate constant by ½ would result in a 50%
decrease in GFP production, etc. GFP was produced at similar amounts based on each volume of PFAS.</p>
</div>
</div>
<div id="Cycle-2">
<h2>VCell Modeling Cycle 2</h2>
<h2>Construct 3: SyntTranscriptionFactor_GFP</h2>
<h2>Construct 3: SyntTranscriptionFactor_GFP</h2>
<p> </p>
<h3>Design</h3>
<p>Further research shows that a combination of constructs 3 & 4 enhances the production of GFP in the presence of PFAS. Due to an error in the creation of constructs 3 and 4 which were supposed to work together to produce the GFP we had to recreate construct 3 which is a combination of the 2 constructs. When PFAS is introduced into the system and binds with the Synthetic Transcription factor it allows it to bind to the pLex promoter which induces transcription. Using this information a new model was developed using previous rate constants from the models and new simulations were run.
<p>Further research shows that a combination of constructs 3 & 4 enhances the production of GFP in the
presence of PFAS. Due to an error in the creation of constructs 3 and 4 which were supposed to work together
to produce the GFP we had to recreate construct 3 which is a combination of the 2 constructs. When PFAS is
introduced into the system and binds with the Synthetic Transcription factor it allows it to bind to the
pLex promoter which induces transcription. Using this information a new model was developed using previous
rate constants from the models and new simulations were run.
</p>
<p> </p>
<p>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.</p>
<p>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.</p>
<p> </p>
<p>Model name: Construct 3_Cycle 2</p>
<p>Owner: Aryanshah16</p>
@@ -284,71 +429,108 @@
<p> </p>
<p> </p>
<h3>Test & Results</h3>
<p>We ran stochastic simulations on the reaction diagram with varying micromolars of PFAS and ran to see its effect on the GFP protein production. Different rate constants were used which caused variance in the amount of GFP produced between the constructs.</p>
<p>We ran stochastic simulations on the reaction diagram with varying micromolars of PFAS and ran to see its
effect on the GFP protein production. Different rate constants were used which caused variance in the amount
of GFP produced between the constructs.</p>
<img src="https://static.igem.wiki/teams/5114/aryan/vjvejievrji.png" alt="GFP_Graph">
<p>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.</p>
<p>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.</p>
<p> </p>
<h3>Conclusion and Learnings</h3>
<h3>Conclusion and Learnings</h3>
<p> </p>
<p>Going through the rate constant, where PFOA binds to the synthetic transcription factor, it directly correlates with the amount of GFP produced. Multiplying the rate constant by ½ would result in a 50% decrease in GFP production, etc. GFP was produced at similar amounts based on each volume of PFAS.</p>
<p>Going through the rate constant, where PFOA binds to the synthetic transcription factor, it directly
correlates with the amount of GFP produced. Multiplying the rate constant by ½ would result in a 50%
decrease in GFP production, etc. GFP was produced at similar amounts based on each volume of PFAS.</p>
</div>
<div id="Cycle-3">
<h2>Cycle 3</h2>
<h3>Design</h3>
<p>In the previous iterations, there were rate constants that were fully estimated without sources. In this cycle, a new source was added to each rate constant that did not have one previously. All sources can be found within our publicly available VCell Biomodels.
PFAS testing ranges were recomputed. Regulations are on the parts per trillion (ppt) level, so we used 1 ppt as our minimum concentration. Assuming 1 ppt=1 ng/ml and the molar weight of PFOA, a common model PFAS, is 414 g/mol, that means 1ppt is approximately 2E-6 uM of PFOA.
Additionally, we realized stationary phase E. coli are smaller than 3.5 um^3 (Azam et al., 1999) and are closer to 1 um^3.
Since real life application of our biosensor would likely use cells that are already at steady state equilibrium, we ran simulations where protein and mRNA is at steady state equilibrium before PFAS is introduced. Steady state concentrations were determined using deterministic models that ran until concentrations ceased fluctuations.
For the synthetic transcription factor Biomodel, a new degradation pathway for synthetic transcription factor molecules bound to PFOA to be degraded was introduced. For the FAB-GFP model, PFAS is made to be regenerated when the PFAS.FAB-GFP protein is degraded.
<p>In the previous iterations, there were rate constants that were fully estimated without sources. In this
cycle, a new source was added to each rate constant that did not have one previously. All sources can be
found within our publicly available VCell Biomodels.
PFAS testing ranges were recomputed. Regulations are on the parts per trillion (ppt) level, so we used 1 ppt
as our minimum concentration. Assuming 1 ppt=1 ng/ml and the molar weight of PFOA, a common model PFAS, is
414 g/mol, that means 1ppt is approximately 2E-6 uM of PFOA.
Additionally, we realized stationary phase E. coli are smaller than 3.5 um^3 (Azam et al., 1999) and are
closer to 1 um^3.
Since real life application of our biosensor would likely use cells that are already at steady state
equilibrium, we ran simulations where protein and mRNA is at steady state equilibrium before PFAS is
introduced. Steady state concentrations were determined using deterministic models that ran until
concentrations ceased fluctuations.
For the synthetic transcription factor Biomodel, a new degradation pathway for synthetic transcription
factor molecules bound to PFOA to be degraded was introduced. For the FAB-GFP model, PFAS is made to be
regenerated when the PFAS.FAB-GFP protein is degraded.
</p>
<h3>Build</h3>
<p>Model name: Construct3_Cycle3_dgl
Model owner: dglVcell
</p>
<img src="https://static.igem.wiki/teams/5114/vcelleng/stf-pathway.png" alt="stf pathway" width="70%" height="70%">
<img src="https://static.igem.wiki/teams/5114/vcelleng/stf-pathway.png" alt="stf pathway" width="70%"
height="70%">
<p>Model name: FAB_GFP_Construct_v1.1_Douglas
Model owner: dglVcell
</p>
<img src="https://static.igem.wiki/teams/5114/vcelleng/fab-pathway.png" alt="fab pathway" width="70%" height="70%">
<img src="https://static.igem.wiki/teams/5114/vcelleng/fab-pathway.png" alt="fab pathway" width="70%"
height="70%">
<h3>Test</h3>
<p>Simulations were run for 100 minutes (6000 seconds). Every plot shown is stochastic using the Gibson solver.</p>
<img src="https://static.igem.wiki/teams/5114/vcelleng/vcellplots.jpg" alt="vcell plots" width="80%" height="80%">
<p>Simulations were run for 100 minutes (6000 seconds). Every plot shown is stochastic using the Gibson
solver.</p>
<img src="https://static.igem.wiki/teams/5114/vcelleng/vcellplots.jpg" alt="vcell plots" width="80%"
height="80%">
<h3>Learn</h3>
<p>The improved models showed very intriguing results.
<p>The improved models showed very intriguing results.
A PFAS-inducible transcription factor with a relatively strong binding affinity appears to produce the greatest amount of signal in response to a very small amount of PFAS. Even when there is only about a single PFAS molecule per cell (at 10^-6 uM), the synthetic transcription factor system can produce a large amount of GFP (about 5000 molecules per cell).
The FAB-GFP system appears to be limited by the amount of PFAS that can bind to the GFP. At extremely low concentrations of PFAS, like those set as the minimal safety limits, there may be only one bound FAB-GFP producing fluorescence in a cell.
For both constructs, it appears that the cell actually depletes the amount of PFAS that is available externally due to strong binding affinities. It is important to note that while the binding affinity of FAB-GFP has been experimentally characterized by the original creators, the binding affinity of the synthetic transcription factor was set to be the empirical binding affinity of human estrogen receptor alpha to PFOA, which may not accurately reflect the actual binding affinity of PFOA or other PFAS to the entire synthetic transcription factor.
</p>
A PFAS-inducible transcription factor with a relatively strong binding affinity appears to produce the
greatest amount of signal in response to a very small amount of PFAS. Even when there is only about a single
PFAS molecule per cell (at 10^-6 uM), the synthetic transcription factor system can produce a large amount
of GFP (about 5000 molecules per cell).
The FAB-GFP system appears to be limited by the amount of PFAS that can bind to the GFP. At extremely low
concentrations of PFAS, like those set as the minimal safety limits, there may be only one bound FAB-GFP
producing fluorescence in a cell.
For both constructs, it appears that the cell actually depletes the amount of PFAS that is available
externally due to strong binding affinities. It is important to note that while the binding affinity of
FAB-GFP has been experimentally characterized by the original creators, the binding affinity of the
synthetic transcription factor was set to be the empirical binding affinity of human estrogen receptor alpha
to PFOA, which may not accurately reflect the actual binding affinity of PFOA or other PFAS to the entire
synthetic transcription factor.
</p>
</div>
</div>
<div id="AI">
<h2>AI Initiatives</h2>
<p>At an earlier stage in the project, we attempted to use AI to design proteins that could be used to bind to PFAS, acting as a biosensor. While we were not able to incorporate a fully functional model into the final project, we have documented our efforts and progress so that the work could be expanded on.</p>
<object data="https://static.igem.wiki/teams/5114/vcelleng/igem-documentation-on-ai-initiatives-1.pdf" type="application/PDF" width="90%" height="600px"></object>
<p>At an earlier stage in the project, we attempted to use AI to design proteins that could be used to bind to
PFAS, acting as a biosensor. While we were not able to incorporate a fully functional model into the final
project, we have documented our efforts and progress so that the work could be expanded on.</p>
<object data="https://static.igem.wiki/teams/5114/vcelleng/igem-documentation-on-ai-initiatives-1.pdf"
type="application/PDF" width="90%" height="600px"></object>
</div>
<div id="References">
<h2>References</h2>
<ul>
<li>Blinov, M. L., J. C. Schaff, D. Vasilescu, Moraru, II, J. E. Bloom, and L. M. Loew. 2017.
Compartmental and Spatial Rule-Based Modeling with Virtual Cell. Biophysical journal
113:1365-1372. PMC5627391
</li>
<li>Cai, L., Friedman, N., & Xie, X. S. (2006). Stochastic protein expression in individual cells at the single molecule level. Nature, 440(7082), 358–362. https://doi.org/10.1038/nature04599
</li>
<li>Cowan, A. E., Moraru, II, J. C. Schaff, B. M. Slepchenko, and L. M. Loew. 2012. Spatial modeling of cell signaling networks. Methods Cell Biol 110:195-221. PMC3288182
</li>
<li>Schaff, J., C. C. Fink, B. Slepchenko, J. H. Carson, and L. M. Loew. 1997. A general computational framework for modeling cellular structure and function. Biophysical journal 73:1135-1146. PMC1181013
</li>
<li>Ali Azam T, Iwata A, Nishimura A, Ueda S, Ishihama A. Growth phase-dependent variation in protein composition of the Escherichia coli nucleoid. J Bacteriol. 1999 Oct;181(20):6361-70. doi: 10.1128/JB.181.20.6361-6370.1999. PMID: 10515926; PMCID: PMC103771.</li>
<li>Tuttle, A. R., Trahan, N. D., & Son, M. S. (2021). Growth and maintenance of escherichia coli laboratory strains. Current Protocols, 1(1). https://doi.org/10.1002/cpz1.20 </li>
</ul>
</div>
<div id="References">
<h2>References</h2>
<ul>
<li>Blinov, M. L., J. C. Schaff, D. Vasilescu, Moraru, II, J. E. Bloom, and L. M. Loew. 2017.
Compartmental and Spatial Rule-Based Modeling with Virtual Cell. Biophysical journal
113:1365-1372. PMC5627391
</li>
<li>Cai, L., Friedman, N., & Xie, X. S. (2006). Stochastic protein expression in individual cells at the
single molecule level. Nature, 440(7082), 358–362. https://doi.org/10.1038/nature04599
</li>
<li>Cowan, A. E., Moraru, II, J. C. Schaff, B. M. Slepchenko, and L. M. Loew. 2012. Spatial modeling of cell
signaling networks. Methods Cell Biol 110:195-221. PMC3288182
</li>
<li>Schaff, J., C. C. Fink, B. Slepchenko, J. H. Carson, and L. M. Loew. 1997. A general computational
framework for modeling cellular structure and function. Biophysical journal 73:1135-1146. PMC1181013
</li>
<li>Ali Azam T, Iwata A, Nishimura A, Ueda S, Ishihama A. Growth phase-dependent variation in protein
composition of the Escherichia coli nucleoid. J Bacteriol. 1999 Oct;181(20):6361-70. doi:
10.1128/JB.181.20.6361-6370.1999. PMID: 10515926; PMCID: PMC103771.</li>
<li>Tuttle, A. R., Trahan, N. D., & Son, M. S. (2021). Growth and maintenance of escherichia coli laboratory
strains. Current Protocols, 1(1). https://doi.org/10.1002/cpz1.20 </li>
</ul>
</div>
</div>
</div>
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