iGEM 2023 — Team manual-ky

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{% block lead %}Complete record of our lab.{% endblock %}
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## Our lab notebook

[Lab Notebook](https://static.igem.wiki/teams/5029/wiki/other-images/notebook.pdf)
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# Plasmid Vector Experiment

Our lab efforts were split into designing two plasmids: an expression vector and storage vectors. The expression vector was used to test our gene circuit in PFAS, while we used the storage vectors in order to transform bacteria as “warehouses” to allow for easy access to our gene inserts.

## Expression vectors

Note: Full protocols for each step can be found on Experiment and Notebook Page

### Engineering Cycle 1

#### Design

The design of the gene inserts and their addition into the plasmid was made by our team (REFERENCE DESIGN TAB OF WIKI). Preliminary protocols for procedures for our lab work were also created, including the ligation of the insert, PFAS testing, and Lactose testing (as a control). These protocols included basic lab techniques, such as Miniprep, PCR Purification columns, transformation, etc. In this cycle of the experiment, we were interested in making sure that the ligation procedure would result in our desired insert.

![](https://static.igem.wiki/teams/5029/wiki/other-images/assembly.png)

#### Build

In the lab, the dried DNA delivered by Twist was resuspended and an aliquot of resuspended parts A, B, C were taken for assembly, with the rest to be stored in plasmids (reference Storage Vectors). Part A was digested with SpeI, B with XbaI, and C with XbaI overnight. A PCR cleanup column procedure was performed on all 3 parts. A and B were then ligated with T4 DNA Ligase to form part AB. AB was then digested with SpeI before going through a PCR cleanup column. After cleanup, AB was ligated with C to form ABC and digested with EcoRI and PstI to prepare it for insertion into a vector backbone. However, during the final PCR cleanup procedure for part ABC, Wash Buffer was added instead of Binding Buffer on accident. This resulted in a low yield for the ABC insert, necessitating multiple cycles of recovery cleanup through gradual addition of Binding Buffer and ethanol evaporation.

#### testing the Gene Insert

Due to the mistake in the last step of gene insert preparation, testing of the insert with the Bioanalyzer was postponed. It was decided that the process of creating the insert should be restarted from scratch.

#### learning From Mistakes

In the next iteration of the engineering cycle, the goal was to recreate the composite part ABC without mistakes in the cleanup process. All other steps were correctly executed in the first cycle according to our planned procedure, so ideally the only alteration would be the avoidance of using the incorrect buffer during cleanup. We also decided to shorten digest times because ThermoFisher enzymes do not require overnight incubation and we had limited time left in the lab. Overnight incubation was only done in the first cycle because the entire assembly procedure could not be completed in one day, so digestion points were used as appropriate stopping points for lab work days.

### Cycle 2: Giving the Assembly Procedure Another Go

#### Design

The original steps set down in our lab notebook would be followed tightly with extra care required during cleanup stages. The goal of this cycle would remain unchanged: to assemble A, B, and C so that a gel analysis of our insert would have a band at ~3000 bp.

#### Building the Insert Again

Every step described above and in our Experiments page for the assembly of A, B, and C was carried out correctly, including the botched step from the previous cycle.

#### Testing the Insert

The gene insert was loaded into an Agilent 2100 Bioanalyzer to compare band sizes. A dark band was found at 1000 bp, consistent with the size of A or B individually. A lighter band was present at 2000 bp, consistent with the size of the composite AB part. However, no band was detected at 3000 bp, which would’ve been representative of all 3 parts correctly ligated together.

#### Learning

From the Bioanalyzer results, it was evident something had gone wrong in between assembly of part AB and the final assembly of ABC. Furthermore, the faintness of the 2000 bp band was also indicative of a problem with ligation, however, we did not have time to redo the entire procedure, so we planned to re-ligate AB and C together from surplus pre-digested aliquots of AB and C.

### Cycle 3: Designing Another Assembly

#### Design

Due to the low ligation efficiency demonstrated by the Bioanalyzer and the lack of the full ABC fragment, ligation time was increased beyond what was recommended by our Ligase supplier. Because we were also very short on time, we started from surplus AB and C left over from the previous cycle.

#### Rebuilding ABC from AB and C

We had some surplus materials labeled as AB and C, and in our experiment, we chose to use the remaining AB along with an appropriate proportion of C. Initially, when we combined components A and B, we used a specific quantity of each. We followed the same procedure when we introduced component C during the ligation process of ABC. Although the kit supplier recommended a 5-minute ligation period with the provided enzyme quantity, we decided to extend this time significantly. Consequently, we performed a 15-minute ligation step and a 45-minute digestion step.

#### Final test of ABC ligation

We loaded the ABC construct created in this cycle as well as the ABC construct created in Cycle 1 into the Bioanalyzer to compare band intensities and locations.

![](https://static.igem.wiki/teams/5029/wiki/other-images/screenshot-2023-10-12-at-8-05-10-am.png)
![](https://static.igem.wiki/teams/5029/wiki/other-images/screenshot-2023-10-12-at-8-05-21-am.png)

Bioanalyzer results for the ABC construct made in Cycle 3. A small band is present at 2000 bp but no discernable band is present at 3000.

![](https://static.igem.wiki/teams/5029/wiki/other-images/screenshot-2023-10-12-at-8-05-31-am.png)

Bioanalyzer results for the ABC construct created in Cycle 1. The Bioanalyzer showed that the A, B, and C parts used in Cycle 1 did not ligate at all. In the 3rd and 2nd engineering cycles, the construct did partially ligate to form AB but did not successfully incorporate part C. This means no detectable amounts of the full ABC construct was made in our lab. We decided not to pursue another cycle due to extreme time constraints.

#### Learning from the Data

Ligation remained a large problem with the procedure, as the parts evidently did not join together as expected. It is possible the large sizes of our inserts decreased the reaction efficiency during the ligation step, so in future assemblies with large parts a longer ligation time is strongly recommended. More testing will have to be conducted to determine exactly how much longer ligation time should take. If we had more time, we would’ve also miniprepped each insert in order to get a larger volume of each to work with, as that would’ve mitigated any DNA loss from each cleaning step. We were using nanogram quantities of DNA in these cycles because we didn’t have time to make more with mini prepping, so having more DNA would have increased our tolerance for any errors.

## Storage Vectors

### Engineering Cycle 1

#### Design

The design for the inserts in this portion were similar to the expression portion. Rather than ligating all three of the inserts together, the objective of this part of the project was to create storages of each part of the insert, which could then be extracted in the future for ligation and testing.

#### Build

The first part was to digest and prepare the plasmids for ligation with the inserts. Plasmids were chosen for restriction sites (EcoRI and PST) which resulted in 43 viable plasmids. Out of these 43, the first 4 were taken for experimentation (BBa\_I20270 - A11, BBa\_J364000 - C11, BBa\_J364001 - E11, BBa\_J364002 - G11). These plasmids were aliquoted from the distribution kit and transformed into bacteria, which were streaked for overnight growth. A colony was taken from each plate for the Miniprep procedure, and the resulting supernatant fluid was used as A11, C11, E11, and G11.

#### Test

The plasmids were nanodropped, and from concentration results, C11, E11, and G11 were used. Each of them was digested separately with EcoRI and PST overnight. They were then run through the bioanalyzer. After seeing good results, the plasmids were put through a gel purification column via gel electrophoresis. However, when the gel was examined for bands, none were present.

#### Learn

A few problems were observed in our build procedure, which we hoped to fix in the second iteration:
Following the miniprep protocol carefully. We accidentally skipped the Resuspension Buffer which may have impacted our results.
Taking out a singular colony before miniprep. Our plates contained a lot of lawns, which made it hard to pick out a singular colony, but this is something we needed to fix for the next iteration.
The nanodrop results were unreliable because water was used as the blank, not the elution buffer.

### Engineering Cycle 2

#### Build

Plasmid preparation was restarted by taking more of the already extracted A11, C11, E11, and G11. It was miniprepped while following the protocol carefully. The plasmids were nanodropped, and it was found that the A11, C11, and E11 were the purest. A PCR Purification column was used to purify the plasmid, and the plasmids were digested.

#### Test

The undigested and digested plasmids were run on a Bioanalyzer and sufficient concentrations were seen. However, when they were run side by side on a gel and the gel was analyzed for bands, sufficient bands weren’t seen for A11, C11, or E11, regardless of the results in the Bioanalyzer.

#### Learn

Although we weren’t sure why there were no bands present on the gel, we realized that the nanodrop results weren’t the only factor we should take into consideration when figuring out what plasmids to use. We also realized that very proper gel technique is necessary for the gel purification protocol, or the bands won’t show up.

### Engineering Cycle 3

#### Build

After seeing the lack of A11, C11, and E11 on the gel, G11 was quickly digested and run through a PCR Purification Column. After purification, G11 was nanodropped. After seeing the low concentration present, G11 was eluted 2 more times in order to extract any remnant plasmid in the column. The 3 elutions of G11 were run through a Bioanalyzer for verification. After verifying the presence of enough plasmid, the digested A, B, and C parts were each inserted into separate plasmids through a ligation protocol. After ligation, bacteria were transformed and streaked for storage.

#### Test

The ligated fragments were run through a bioanalyzer, which showed sufficient amounts of ligation and a high concentration of ligated plasmid. The following day, the plate growth was also verified (shown below).

![](https://static.igem.wiki/teams/5029/wiki/other-images/screenshot-2023-10-12-at-8-15-46-am.png)

#### Learn

Although no work with these storage bacteria has been done, we did modify our lab expectations for this competition season. We originally expected to do transformations and testing for both the PFAS sensitive and Lactose sensitive plasmids, but after the lab work done, we realized that only the transformation of the PFAS sensitive insert was plausible in that timeframe.

# OpenMM

## Cycle 1

### Design

In the initial cycle of our OpenMM project, our primary objective was to conceptualize a simulation that would allow us to explore the interactions between ahl (30C6HSL), LuxR, and PFOA. We began by investigating available modeling tools such as VCell, but quickly realized their limitations, particularly their reliance on mass action equations and molecular kinematics for predictions. Realizing the need for more comprehensive simulations, we sought advice from Dr. Monsen, who guided us toward a more sophisticated approach. Given our time constraints and limited lab access, molecular dynamics emerged as an attractive alternative. As we mapped out the simulation, our main focus was on understanding how the ligand moved within the molecular structure after docking. This required us to find a method for effectively docking the ligand and protein together while maintaining precise control over temperature and pressure. After thorough examination of the OpenMM documentation, we decided that an NPT simulation would best suit our needs, offering temperature and pressure control, and even the inclusion of a Monte Carlo barostat to capture vital data such as temperature and energy.

### Build

With the simulation design in place, the next step was to construct the script. Our first task was to dock the ligand and protein, which led us to discover the ROSIE server. This powerful tool generated over 200 iterations, and from this pool, we carefully selected the top 10 for inclusion in our simulation. Given our limited experience, we often defaulted to simplicity and used default settings, even opting to exclude water from the simulation.

![](https://static.igem.wiki/teams/5029/wiki/other-images/fibgubrv.png)

### Test

When we attempted to execute the script, we encountered a series of errors. These ranged from minor syntax issues and deprecated libraries to a more significant problem: OpenMM's inability to recognize our ligand and assign appropriate charges to it. To name some errors we received, our original idea to use Swissdock did not work because it did not accept our input files, resulting in us using ROSIE. Regarding our input files, OpenMM could not identify the name of the ligand residue, and so it was an unknown, causing errors. One of the most fatal errors we recieved was that when ever we tried to do something related to the bonds, it could not recognise the bond and or its charge.

### Learn

Through these initial challenges, we learned several critical lessons. We discovered that the forcefields we initially employed lacked essential data for our ligand. While forcefields for proteins are well-documented and encompass various residues, ligands typically lack such comprehensive information. Moreover, OpenMM struggled with charge assignment for the ligand molecule, resulting in additional errors and complications. We also discovered that things produced by online servers aren’t always 100% accurate and still needed to be edited by hand. We also realized in order for this to work, we will need to develop a forcefield for the specific ligand, which became a hassle in its own right.

## Cycle 2

### Design

In our second cycle of OpenMM exploration, we approached the project with a more informed strategy. Our focus shifted towards designing the necessary .xml files (forcefields) while ensuring accurate partial charge assignments for the ligand. We recognized that creating forcefields was a complex task, often requiring quantum computing tools. In addition, we explored the possibility of incorporating water into the simulation to enhance the accuracy of our results. To streamline our code and reduce complexity, we decided to implement a single system encompassing all four forcefields: the protein forcefield, water forcefield, ligand-protein forcefield, and ligand-specific forcefield. Instead of what was used before, a system with two subsystems, one for the ligand and one for the protein.

### Build

During the building phase, we relied heavily on the default models provided by OpenMM to minimize the risk of errors, as this was one of the few resources available to us. We also learned how to use antechamber to generate a file containing our charges, which could then be processed through an online server to create a forcefield file. However, fine-tuning was still necessary, particularly concerning the Coulomb scale for the non-bonded force.

### Test

With the modified simulation script in place, we ran the simulation once again. This time, however, we encountered new challenges, as the generated values did not align with our expectations. These discrepancies ranged from minor issues, such as temperature fluctuations, to more significant problems, including negative box volumes that raised questions about the theoretical space occupied by the simulation.

![](https://static.igem.wiki/teams/5029/wiki/other-images/screenshot-2023-10-07-at-11-00-58-am.png)

### Learn

In the second cycle, our journey with OpenMM led to a deeper understanding of forcefield design, partial charge assignment, and simulation optimization. We learned valuable lessons about fine-tuning parameters and the importance of meticulous adjustments to achieve meaningful and reliable results in molecular simulations. We also learned how simulations can be inaccurate based on how accurate the information being fed to them is, i.e. forcefields. And forcefields in their own right are hard to develop and even harder to get right, so considering the fact that some info in the forcefield could be incorrect, the data returned to us made sense to us, and though some outputs were completely out of the question such as a negative box volume, it was just a simple error! We incorrectly stored the data for that column, causing a slight confusion. We also learned that the ensemble average of a property (like temperature) calculated over multiple simulation trajectories should converge to the expected macroscopic value. In other words, if your simulated system was big enough to stick a real thermometer into it (like a protein in a buffer on your lab bench), the temperature is converged to a single, macroscopic value that is read out on your thermometer device. However, at the level of a simulation, a single trajectory is just one possible realization of the system's behavior over time. Fluctuations in any single trajectory are expected due to the inherent statistical nature of the system. Over multiple trajectories or a long enough time, the average of these fluctuations should converge to the expected value.

## Virtual Cell Model Development

[Open VCell model development](https://static.igem.wiki/teams/5029/wiki/pdf/virtual-cell-engineering-documentation.pdf)

# Wet Lab

### Reagents and Consumables:

Procedures were referenced from the official documents provided by Thermofisher or Qiagen for each kit.

* Thermofisher FastDigest Value Pack (K1991)
* ThermoFisher FastDigest BcuI (SpeI) (10U/uL) (ER1251)
* Thermofisher T4 DNA Ligase (1U/L) (15224025)
* Qiagen QIAquick PCR Purification Kit (Cat. No. 28104)
* DH5 alpha competent E. coli cells (EC0112)
* GeneJET Gel Extraction Kit (K0691)

### Instruments:

* Agilent 2100 Bioanalyzer
* ThermoFisher Nanodrop One
* Microcentrifuge

## 3 part assembly procedure (first iteration)

This procedure is an adapted form of iGEM’s 3A assembly method. Instead of assembling two gene parts together, this procedure aims to assemble 3 parts into a plasmid backbone. Reference Design section for a visual diagram of the ligation procedure.

### Digesting 3 inserts separately

* Label 3 1.5 mL microcentrifuge tubes A, B, and C.
* Into each tube, transfer 3uL of gene insert (part A into tube A, part B into tube B, part C into tube C) and 5 uL of distilled water.
* Into tube A, add 1uL of 10X Tango Buffer (comes with SpeI) and 1 uL of SpeI
* Into tubes B and C, add 1uL of FastDigest buffer and 1 uL of XbaI
* Vortex and microcentrifuge tubes A, B, and C to mix and collect the liquid in each
* Incubate in 37C water bath for 15 hours (overnight)

### Cleaning Up Each Sample with Qiagen PCR Purification Kit

Due to the heat resistant nature of SpeI, we could not heat inactivate the enzyme and had to use a PCR cleanup kit to remove the enzyme to ensure it did not interfere with downstream processes. Every cleanup step is performed with a Qiagen PCR Purification Kit. The procedure listed here is repeated for each tube. As a side note, our Speed-Vac machine was likely broken and couldn’t pull a vacuum or heat up to 60 C.

* Add 50 uL of Buffer PB to the digest mix (5:1 ratio)
* Transfer sample to QIAquick column that has already been assembled with a 2 mL collection tube
* Centrifuge at 13000 RPM for 60 seconds
* Discard flow-through and replace column in collection tube
* Add 750 uL of Buffer PE to the column and centrifuge again at 13000 RPM for 60 seconds
* Discard flow-through and replace column in collection tube
* Centrifuge again at 13000 RPM for 60 seconds
* Discard collection tube and place column in clean 1.5 mL microcentrifuge tube
* Add 50 uL Buffer EB to column
* Centrifuge for another 60 seconds at 13000 RPM and collect flowthrough
* Add another 30 uL of Buffer EB to column and centrifuge at 13000 RPM for 60 seconds using the same tube
* Place open microcentrifuge tube into Speed-Vac and spin at 60C for 5 minutes

### Ligating A and B Together

* Label a new 1.5 mL microcentrifuge tube “AB”
* Add 9 uL of the cleaned part A and 9 uL of the cleaned part B into that tube
* Add 1 uL of T4 DNA Ligase
* Add 4 uL of Ligase Buffer
* Incubate for 1 hour at room temperature
* To kill the ligase, incubate at 70 C for 5 minutes

### Digesting Part AB

* To the previous mixture, add 2 uL of 10X Tango Buffer and 1uL of SpeI
* Incubate overnight in 37 C water bath

### Cleaning Up Part AB

As a side note, our Speed-Vac machine was likely broken and couldn’t pull a vacuum or heat up to 60 C.

* Add 50 uL of Buffer PB to the digest mix (5:1 ratio)
* Transfer sample to QIAquick column that has already been assembled with a 2 mL collection tube
* Centrifuge at 13000 RPM for 60 seconds
* Discard flow-through and replace column in collection tube
* Add 750 uL of Buffer PE to the column and centrifuge again at 13000 RPM for 60 seconds
* Discard flow-through and replace column in collection tube
* Centrifuge again at 13000 RPM for 60 seconds
* Discard collection tube and place column in clean 1.5 mL microcentrifuge tube
* Add 50 uL Buffer EB to column
* Centrifuge for another 60 seconds at 13000 RPM and collect flowthrough
* Add another 30 uL of Buffer EB to column and centrifuge at 13000 RPM for 60 seconds using the same tube
* Place open microcentrifuge tube into Speed-Vac and spin at 60C for 5 minutes

### Ligating AB and C

* To 80 uL of cleaned AB from the last step, add 20 uL of part C
* Add 12 uL of Ligase Buffer
* Add 1 uL of T4 DNA Ligase
* Incubate for 1 hour at room temperature
* Kill ligase by incubating at 70 C for 5 minutes

### Digesting ABC with EcoRI and PstI

* To the ligation mixture above, add 15(?) uL of FastDigest Buffer
* Add 1 uL of EcoRI and 1uL of PstI
* Vortex and microcentrifuge to collect liquid
* Incubate in 37 C water bath overnight

### Cleaning Up Part ABC (botched)

As a side note, our Speed-Vac machine was likely broken and couldn’t pull a vacuum or heat up to 60 C.

* Load 100 uL of digestion mix into QIAquick column and place into collection tube
* Add 500 uL of Wash Buffer (should have been binding buffer)
* Realize mistake
* Centrifuge at 13000 RPM for 1 minute
* Speed Vac for 5 minutes
* Place flowthrough back into column and top off with Membrane Binding Buffer
* Repeat 4-6 4 times
* Place column into new 1.5 mL microcentrifuge tube
* Add 50 uL of Elution Buffer to column and centrifuge at 13000 RPM for 1 minute
* Add 30 uL of Elution Buffer to column and centrifuge at 13000 RPM for 1 minute
* Await Bioanalyzer testing of flowthrough, which should contain the composite part ABC

## 3 part assembly procedure (second iteration)

### Digesting 3 inserts separately

* Label 3 tubes: A, B, C
* For each tube add 3 uL of the insert, 1 uL of 10x buffer, 5 uL of distilled water, 1 uL enzyme

+ For tube A: Insert=Const-LuxR 2, enzyme is SpeI
+ For tube B: Insert=Prma-LuxI, enzyme is XbaI
+ For tube C: Insert=Plux-GFP1, enzyme is XbaI

* Vortex the mix, and then centrifuge to collect all contents to the bottom
* Put all 3 tubes in a water bath at 37C water bath for 15 minutes
* Purify all sample, to remove any enzymes

+ Label 3 column tubes A, B, C
+ Label 3 microcentrifuge tubes A, B, C
+ Put 50 uL of the buffer PB and mix in each tube
+ Place each 60 uL mix in their respective column tubes
+ Centrifuge the tubes for 1 minute at 13000 RPM
+ Empty flow through and place the column back into the same tubes
+ Add 750 uL of buffer PE into the column and centrifuge again
+ Discard flow through, place column in the same tube and centrifuge again
+ Place column into their respectively label new micro centrifuge tubes
+ Add 50 uL of buffer EB to each column and centrifuge
+ Now all samples have been purified

### Ligating Insert A and B

* Label a new tube AB
* Add 9 uL of purified A and 9 uL of purified B, 5 uL ligase buffer, 1 uL water, 1 uL of Ligase to the tube
* Vortex and Centrifuge
* Let the mix sit for 5 minutes

### Digesting AB mix

* In the AB tube (should contain 25 uL of content) add 3 uL of 10x TANGO buffer, 1 uL of SpeI and 1 uL of water
* Vortex, and centrifuge to bring contents down to the bottom
* Let tube sit in a 37C water bath for 15 minutes
* Purify the AB mix to remove any enzymes

+ Label 1 column tube AB
+ Label 1 microcentrifuge tubes AB, on the side write pure
+ Put 150 uL of the buffer PB and mix in the original AB tube
+ Place each 180 uL mix in its respective column tube
+ Centrifuge the tubes for 1 minute at 13000 RPM
+ Empty flow through and place the column back into the same tubes
+ Add 750 uL of buffer PE into the column and centrifuge again
+ Discard flow through, place column in the same tube and centrifuge again
+ Place column into their respectively label new micro centrifuge tubes
+ Add 50 uL of buffer EB to each column and centrifuge
+ Now all samples have been purified

### Ligating inserts AB and C

* Make a new tube labelled ABC
* Add 25 uL of purified AB mix, 9 uL of C, 9 uL of 5x ligase buffer, 1 uL of Ligase, and 1 uL of water
* Vortex and Centrifuge
* Let the mix sit at room temperature for 5 minutes

### Digesting ABC

* In your ABC tube (should have 45 uL of content)
* Add 6 uL of 10x Buffer, 7 uL of water, 1 uL of ECORI, and 1 uL of PSTI
* Vortex and centrifuge
* Let the mix sit in a 37C water bath for 15 minutes
* Purify your sample

+ Label 1 column tube ABC
+ Label 1 microcentrifuge tubes ABC, on the side write pure
+ Put 300 uL of the buffer PB and mix in the original ABC tube
+ Place each 360 uL mix in its respective column tube
+ Centrifuge the tubes for 1 minute at 13000 RPM
+ Empty flow through and place the column back into the same tubes
+ Add 750 uL of buffer PE into the column and centrifuge again
+ Discard flow through, place column in the same tube and centrifuge again
+ Place column into their respectively label new micro centrifuge tubes
+ Add 50 uL of buffer EB to each column and centrifuge
+ Now all samples have been purified

You should now have all 3 inserts assembled and purified along with some extra samples in each step of the way, in case you want to run a Bio analyzer, Nanodrop, etc.

## Redoing the process starting at Ligation of AB and C

### Ligating inserts AB and C again

* Make a new tube labelled ABC2
* Add 25 uL of purified AB mix, 9 uL of C, 9 uL of 5x ligase buffer, 1 uL of Ligase, and 1 uL of water
* Vortex and Centrifuge
* Let the mix sit at room temperature for 15 minutes

### Digesting ABC

* In your ABC tube (should have 45 uL of content)
* Add 6 uL of 10x Buffer, 7 uL of water, 1 uL of ECORI, and 1 uL of PSTI
* Vortex and centrifuge
* Let the mix sit in a 37C water bath for 45 minutes
* Purify your sample

+ Label 1 column tube ABC
+ Label 1 microcentrifuge tubes ABC, on the side write pure
+ Put 300 uL of the buffer PB and mix in the original ABC tube
+ Place each 360 uL mix in its respective column tube
+ Centrifuge the tubes for 1 minute at 13000 RPM
+ Empty flow through and place the column back into the same tubes
+ Add 750 uL of buffer PE into the column and centrifuge again
+ Discard flow through, place column in the same tube and centrifuge again
+ Place column into their respectively label new micro centrifuge tubes
+ Add 50 uL of buffer EB to each column and centrifuge
+ Now all samples have been purified

You should now have all 3 inserts assembled again and purified along with almost no extra surplus.

## Storage Vectors - Plasmid Digestion Try #1

### Creation of Competent Cells

* Obtain Escherichia coli cells
* Heat shock for 30 seconds in 42° C H2O
* Incubated on ice for 2 minutes
* Store the cells at room temperature while preparing the Super Optimal Broth
* Add 250 uL of SOB (Super Optimal Broth) Medium to the cells
* Put cells on a shaking incubator for 60 minutes at 225 rpm (revolutions per minute) at 37° C.

### Transforming Competent Cells with Plasmids

* Thaw the competent cells on wet ice. Add the microcentrifuge tubes that will be used to the wet ice as well.
* Gently mix the cells and then add 50 μL aliquots to the chilled microcentrifuge tubes
* Add 1-5 μL of the plasmid DNA into the tube and mix well by flicking the tube
* Incubate the cells on ice for 30 minutes
* Heat shock the cells for exactly 30 seconds in a 42° C water bath
* Incubate the cells on ice for 2 minutes
* Add 250 μL of SOC medium and place the tube in a shaking incubator
* Shake the tubes at 225 rpm for 1 hour at 37° C
* Spread cells from each tube to an agar plate and incubate at 37° C for 18 hours (overnight)

+ To prepare the agar plate

- Add 500 uL of chloramphenicol to 500 mL of agar
- Pour the mix was onto a 20 uL plate.

Our agar plates had to much growth. We realized that our ration of 1:1 was too high, so we decided that we would dilute it the next time we made agar plates.

### Miniprep of Colonies

Note: All centrifuges were done at 14000 rpm and 27° C

* Centrifuge cells for 5 minutes to pellet cells

+ 5 minutes was too long to centrifuge, only did 3 minutes next time

* Add 250 μL of Resuspension Solution and ensure no cell clumps are present

+ We skipped this step on accident

* Transfer all liquid to a microcentrifuge tube
* Add 250 μL of Lysis Solution to tube and invert 4-6 times
* Add 350 μL of Neutralization Solution and invert the tube 4-6 times immediately
* Centrifuge for 5 minutes to pellet cell debris and chromosomal DNA
* Transfer supernatant fluid to the GeneJET Spin Column
* Centrifuge for 1 minute and discard flow-through
* Add 500 μL of the Wash Solution, centrifuge for 30-60 seconds, and discard flow-through
* Centrifuge for an additional 60 seconds and discard the flow-through
* Transfer the GeneJET column to a 1.5 mL microcentrifuge tube
* Add 50 μL of the Elution Buffer and incubate at room temperature for 2 minutes
* Centrifuge for 2 minutes and discard the column. Ensure that the flowthrough is stored safelyh

After nanodropping the plasmids from the miniprep, we decided to only use C11, E11, and G11 since they had the highest concentration and purity (high 260/280 ratio).

### Digestion of Plasmids + Inserts

* Label 7 1.5 mL microcentrifuge tubes C11, E11, G11, A, B, and C.
* Into each tube for the plasmids, add 25 uL of the plasmid, 3 uL of 10X buffer, 1 uL of EcoRI, and 1 uL of PST
* Into each tube for the inserts, add 9 uL of the insert, 7 uL of H2O, 2 uL of 10X Buffer, 1 uL EcoRI, and 1 uL of PST
* Put plasmids and inserts into a 37° C water bath for 15 hours (overnight)

### BioAnalyzer

* Put agarose gel into microchip and incubate for 1 minute to let it set
* Add undigested and digested plasmids in an alternating pattern into the chip
* Run the chip through the BioAnalyzer. Ensure the BioAnalyzer is set to high sensitivity

The results from the BioAnalyzer were inconclusive for the plasmids, so we decided to move on to the gel electrophoresis and purification. The BioAnalyzer is essentially a smaller gel electrophoresis, which means that running a gel can confirm the results and allow us to move with our experiment.

The results from the BioAnalyzer for the inserts showed that parts A and B had digested correctly. However, part C wasn’t digested correctly from the size that the band correlated with. We redigested part C using the same digestion procedure shown above.

### Gel Electrophoresis

* Measure out 1 gram of agarose gel powder.
* Poured into a beaker and measure up to 100 mL with TAE (Tris-acetate EDTA 50X) buffer.
* Prepare the gel cast with tape around the corners.
* Pour up to 25 mL of TAE/agarose mix into the empty gel cast.

+ Ensured that the teeth aren’t covered completely

* Put DNA samples onto the gel.

No bands were seen under UV light, so we decided to redo the entire procedure and fix some of the mistakes that were made.

## Storage Vectors - Plasmid Digestion Try #2

### Miniprep of Colonies

Note: All centrifuges were done at 14000 rpm and 27° C

* Centrifuge cells for 5 minutes to pellet cells
* Add 250 μL of Resuspension Solution and ensure no cell clumps are present
* Transfer all liquid to a microcentrifuge tube
* Add 250 μL of Lysis Solution to tube and invert 4-6 times
* Add 350 μL of Neutralization Solution and invert the tube 4-6 times immediately
* Centrifuge for 5 minutes to pellet cell debris and chromosomal DNA
* Transfer supernatant fluid to the GeneJET Spin Column
* Centrifuge for 1 minute and discard flow-through
* Add 500 μL of the Wash Solution, centrifuge for 30-60 seconds, and discard flow-through
* Centrifuge for an additional 60 seconds and discard the flow-through
* Transfer the GeneJET column to a 1.5 mL microcentrifuge tube
* Add 50 μL of the Elution Buffer and incubate at room temperature for 2 minutes
* Centrifuge for 2 minutes and discard the column. Ensure that the flowthrough is stored safelyh

After nanodropping the plasmids from the miniprep, we decided to only use A11, C11, and E11 since, although G11 had high concentration, they had the highest purity (high 260/280 ratio).

### PCR Purification Column

Note: All centrifuges were done at 13000 rpm and 27° C

* Add 5 volumes of PB buffer for 1 volume of plasmid
* Place liquid into a QIAquick column
* Centrifuge for 30-60 seconds and discard the flow-through
* Add 750 μL of Buffer PE and centrifuge for 30-60 seconds and discard the flow-through
* Centrifuge one more for 1 minute and discard the flow-through
* Place the QIAquick columns into a 1.5 mL microcentrifuge tube
* Add 30 μL of Elution Buffer (for concentrated DNA) and centrifuge for 1 minute and store the flow-through safely

The plasmids were run through a BioAnalyzer again (Check above for the protocol) and sufficient concentrations and sizes were seen for all of the plasmids that were digested.

### Gel Electrophoresis

* Measure out 1 gram of agarose gel powder.
* Poured into a beaker and measure up to 100 mL with TAE (Tris-acetate EDTA 50X) buffer.
* Prepare the gel cast with tape around the corners.
* Pour up to 25 mL of TAE/agarose mix into the empty gel cast.

+ Ensured that the teeth aren’t covered completely

* Put DNA samples onto the gel.

Minimal bands were seen in the gel for A11, C11, and E11, so it was determined that the digestion was unsuccessful once again. However, the results were confusing because the BioAnalyzer showed sufficient amounts of all three of the digests. We decided to run the undigested G11 on the gel and its band showed up. For this reason, we decided to quickly digest G11 and use it for the experiment.

## Storage Vectors - G11

### PCR Purification Column

Note: All centrifuges were done at 13000 rpm and 27° C

* Add 5 volumes of PB buffer for 1 volume of plasmid
* Place liquid into a QIAquick column
* Centrifuge for 30-60 seconds and discard the flow-through
* Add 750 μL of Buffer PE and centrifuge for 30-60 seconds and discard the flow-through
* Centrifuge one more for 1 minute and discard the flow-through
* Place the QIAquick columns into a 1.5 mL microcentrifuge tube
* Add 30 μL of Elution Buffer (for concentrated DNA) and centrifuge for 1 minute and store the flow-through safely

The resulting flow-through was nanodropped, but the results showed low concentrations of plasmid. The G11 was eluted 2 more times in order to maximize the amount of plasmid taken from the column.

### Gel Extraction

* Measure out 1 gram of agarose gel powder.
* Poured into a beaker and measure up to 100 mL with TAE (Tris-acetate EDTA 50X) buffer.
* Prepare the gel cast with tape around the corners.
* Pour up to 25 mL of TAE/agarose mix into the empty gel cast.

+ Ensured that the teeth aren’t covered completely

* Put DNA samples onto the gel and let the gel run for 1-1.5 hours.
* Excise the gel slice with the band (make sure it is as close as possible) and add it to a 1.5 mL tube
* Add 1:1 volume of Binding Buffer (volume of buffer: gel slice weight)
* Incubate at 50 to 60° C for 10 minutes or until the gel slice is dissolved
* Vortex the gel mixture briefly and then transfer 800μL of the mixture to the GeneJET column
* Centrifuge for 1 minute and discard the flow-through
* Add 100 μL of Binding Buffer and centrifuge for 1 minute. Discard the flow-through
* Add 700 μL of Wash Buffer and centrifuge for 1 minute. Discard the flow through
* Centrifuge for an additional 1 minute and discard the flow through.
* Transfer the column to a 1.5 mL microcentrifuge tube and add 50 μL of Elution Buffer
* Centrifuge for 1 minute and store the flow-through safely

### Ligation of Inserts

Note: All numerical values of insert amount, plasmid amount, and buffer amount are based on our calculations

* Calculate the ratio of insert:plasmid amount to reach a 1:3 ratio (dependent on concentration)
* In tube 1, add 5 μL of the cleaned part A to 1 μL of G11
* In tube 2, add 2 μL of the cleaned part B to 1 μL of G11
* In tube 3, add 8 μL of the cleaned part C to 1 μL of G11
* Add 1 uL of T4 DNA Ligase to each tube
* Add 3 μL of 10X Ligase Buffer to Tube 1 and 6 μL to Tube 2
* Incubate for 1 hour at room temperature
* Incubate at 70° C for 5 minutes to kill the ligase

### transformation Vector

* Thaw the competent cells on wet ice. Add the microcentrifuge tubes that will be used to the wet ice as well.
* Gently mix the cells and then add 50 μL aliquots to the chilled microcentrifuge tubes
* Add 1-5 μL of the plasmid DNA into the tube and mix well by flicking the tube
* Incubate the cells on ice for 30 minutes
* Heat shock te cells for exactly 30 seconds in a 42° C water bath
* Incubate the cells on ice for 2 minutes
* Add 250 μL of SOC medium and place the tube in a shaking incubator
* Shake the tubes at 225 rpm for 1 hour at 37° C
* Spread cells from each tube to an agar plate and incubate at 37° C for 18 hours (overnight)

+ To prepare the agar plate

- Add 500 uL of chloramphenicol to 500 mL of agar
- Pour the mix was onto a 20 uL plate.

# Dry Lab

## OpenMM

### Setting Up OpenMM

To start with OpenMM, set up procedures on the official website need to be followed. A simplified version for MacBooks will be presented here. Anaconda and a new environment to install OpenMM will be needed. To install run this snippet of code

```
    conda install -c conda-forge openmm
```

this should install openmm, but you might have to use pip to install other necessary libraries for your script. Also to use any of the same files that we have used visit this link.

https://gitlab.igem.org/2023/software-tools/manual-ky

### Docking

A file that contains your ligand and protein is needed, and, ideally, it should already be situated in a binding pocket to reduce the amount of time needed to observe. We used ROSIE a ligand docking software to dock the ligand to the protein. 200 different iterations were used and when the 2 get docked, conformational change is also recorded and saved, meaning the structure is in the correct shape.

https://rosie.graylab.jhu.edu/ligand\_docking

The needed files can be generated; Using the pdb created by Uniprot for LuxR, this smile string for PFOA "C(=O)(C(C(C(C(C(C(C(F)(F)F)(F)F)(F)F)(F)F)(F)F)(F)F)(F)F)O", and this smile string for AHL "CCCC(=O)CC(=O)NC1CCOC1=O"

### Forcefields

Now, to govern how the ligand and protein interact, you will need forcefields, which are essentially just files of data, containing everything about the molecule from elements to charges. Now for the protein, LuxR we can use the general amber forcefield, amber 14 all, and the general forceifeld for water is, amber 14 - tip3pfb, for the ligand and protein system we will use the amber 14 general forcefield, GAFF, and finally we will need one specific to the ligand, or in this case one for pfoa and one for ahl. To generate these files you will need the sdf file for the ligand, the same one used in the ROSIE docking. You will need to install homebrew before proceding. Once homebrew is installed, open terminal and run this

```
    brew install gcc
```

This will install all the dependencies for ambertools.

Now we can use the antechamber tool that comes with ambertools, this is a very useful quantum computing tool. Run this command into terminal to determine the charges for your ligand. Run this in your terminal (same environment that all libraries were installed in anaconda), and you can use The OpenBabel to convert your sdf to mol2 so that you can store charges

```
    antechamber -i /path/to/PFOA.mol2 -fi mol2 -o /new/path/pfoa-ligand-charged.mol2 -fo mol2 -c bcc -nc -1
```

Now that you have a mol2 with your charges, you can run it through this converter to get a pdb file. Then using this cloud server, you can obtain a forcefield file (.xml) for your ligand, you will need to do this twice, to get one for ahl, and one for PFOA.

https://www.cheminfo.org/Chemistry/Cheminformatics/FormatConverter/index.html and https://datascience.unm.edu/tomcat/biocomp/convert

### Developing the script

Now it is time to create the script, we can run this through snippets so each part of the code is understandable, this is the final code after many many iterations. The provided code appears to be using the OpenMM library to perform molecular dynamics simulations.

#### Importing Modules

```
    from openmm.app import *
  from openmm import *
  from openmm.unit import *
  from sys import stdout
  from mdtraj.reporters import *
```

This section imports the necessary modules and classes from OpenMM and other libraries. OpenMM is used for molecular dynamics simulations, and `mdtraj` is used for reporting and analyzing simulation data.

* `openmm.app` contains classes for working with molecular systems, like `PDBFile`, `ForceField`, and `Modeller`
* `openmm` contains core OpenMM classes, such as `System`, `Integrator`, and `Simulation`
* `openmm.unit` contains units used for specifying quantities (e.g., nanometers, kelvin, picoseconds)
* `sys` is used for standard I/O operations, specifically to print output to the console.
* `mdtraj.reporters` contains classes for reporting simulation data

#### Loading Molecular Structures

```
    pdb = PDBFile("pfoa_luxr/pfoa_dock_luxr_1.pdb")
```

In this section, a PDB file representing a molecular structure is loaded using the `PDBFile` class. This file contains the initial coordinates and topology information for the system.

#### Creating a Force Field

```
    forcefield = ForceField('amber14-all.xml', 'amber14/tip3pfb.xml', 'pfoa_luxr/gaff.xml', 'pfoa_luxr/PFOA-specific.xml')
```

A force field is created by specifying XML files that define the parameters for the molecules in the system. This includes information about atoms, bonds, angles, dihedrals, and nonbonded interactions.

#### Modifying the Molecular Structure

```
    modeller = Modeller(pdb.topology, pdb.positions)
    modeller.deleteWater()
    residues = modeller.addHydrogens(forcefield)
    modeller.addSolvent(forcefield, padding=1.0*nanometer)
```

This section imports the necessary modules and classes from OpenMM and other libraries. OpenMM is used for molecular dynamics simulations, and `mdtraj` is used for reporting and analyzing simulation data.

* `Modeller` is used to create a modified molecular structure based on the loaded PDB file.
* `deleteWater()` removes water molecules from the system.
* `openmm.unit` contains units used for specifying quantities (e.g., nanometers, kelvin, picoseconds)
* addHydrogens(forcefield)` adds hydrogen atoms to the structure as per the force field..
* `mdtraj.reporters` contains classes for reporting simulation data

#### Creating the Molecular System

```
    system = forcefield.createSystem(modeller.topology, nonbondedMethod=PME, nonbondedCutoff=1.0*nanometer, constraints=HBonds)
```

Here, the molecular system is created based on the modified topology. The force field parameters are used to define the system's potential energy functions. The `nonbondedMethod` is set to PME, and the `nonbondedCutoff` is set to 1.0 nanometers. Constraints on hydrogen bonds are also specified.

#### Integrator Configuration

```
    integrator = LangevinMiddleIntegrator(300*kelvin, 1/picosecond, 0.002*picoseconds)
```

An integrator is configured for the simulation. In this case, a Langevin integrator is used with a temperature of 300 K, a collision rate of 1/picosecond, and a time step of 0.002 picoseconds.

#### Setting up a Simulation

```
    simulation = Simulation(modeller.topology, system, integrator)
    simulation.context.setPositions(modeller.positions)
```

An integrator is configured for the simulation. In this case, a Langevin integrator is used with a temperature of 300 K, a collision rate of 1/picosecond, and a time step of 0.002 picoseconds.

#### Energy Minimization

```
    simulation.minimizeEnergy()
```

The energy of the system is minimized to relax the initial structure.

#### Simulation Reporters

```
    simulation.reporters.append(PDBReporter('output.pdb', 20000))
    simulation.reporters.append(StateDataReporter(stdout, 100, step=True,
    potentialEnergy=True, temperature=True, volume=True, kineticEnergy=True, totalEnergy=True))
    simulation.reporters.append(StateDataReporter("md_log.txt", 100, step=True,
    potentialEnergy=True, temperature=True, volume=True, kineticEnergy=True, totalEnergy=True))
    simulation.reporters.append(DCDReporter('trajectory.dcd', 100))
```

In this section, several reporters are added to report simulation data at regular intervals. These reporters include PDBReporter for trajectory snapshots, StateDataReporter for energy and temperature data, and DCDReporter for trajectory storage.

#### Running NVT Simulation

```
    simulation.step(10000)
```

A molecular dynamics simulation in the NVT ensemble is run for 10,000 steps.

#### Adding Barostat and Running NPT Simulation

```
    system.addForce(MonteCarloBarostat(1*bar, 300*kelvin))
    simulation.context.reinitialize(preserveState=True)
    simulation.step(10000)
    simulation.reporters[-1].close()
```

A Monte Carlo barostat is added to the system to maintain constant pressure (NPT ensemble), and another NPT simulation is run for 10,000 steps. The last reporter is closed to finish data collection.

#### Data Analysis and Plotting

```
    import numpy as np
    import matplotlib.pyplot as plt
    data = np.loadtxt("md_log.txt", delimiter=',')
```

Data collected from the simulation is loaded from the "md\_log.txt" file into a NumPy array for analysis and plotting.

#### Data Analysis and Plotting

```
    step = data[:,0]
    potential_energy = data[:,1]
    temperature = data[:,2]
    volume = data[:,3]
    kinetic_energy = data[:,4]
    total_energy = data[:,5]

    plt.plot(step, potential_energy, label="Potential Energy")
    plt.plot(step, kinetic_energy, label="Kinetic Energy")
    plt.plot(step, total_energy, label="Total Energy")
    plt.xlabel("Step")
    plt.ylabel("Energy (kJ/mol)")
    plt.legend()
    plt.show()

    plt.plot(step, temperature)
    plt.xlabel("Step")
    plt.ylabel("Temperature (K)")
    plt.show()
    plt.plot(step, volume)
    plt.xlabel("Step")
    plt.ylabel("Volume (nm^3)")
    plt.show()
```

This section uses Matplotlib to create and display plots of potential energy, kinetic energy, total energy, temperature, and volume as a function of simulation step. The data for these properties is obtained from the loaded "md\_log.txt" file. The code is primarily concerned with setting up and running a molecular dynamics simulation and visualizing the results through plots.

#### Final Script

The final script can be downloaded with the gitlab link from earlier.

## Virtual Cell Modeling

Our models are freely available to anyone that has a VCell account. All constants, simulation setups, reaction diagrams, and results can all be accessed for free by opening our models in VCell. Annotations for each reaction include the sources used to find the reaction constants. The models are hosted on VCell servers and are shared publicly in the “Uncurated” folder.

![vcell](https://static.igem.wiki/teams/5029/wiki/other-images/vcell.png)

The best way to replicate our experiments with VCell is to find our models (names listed in Engineering) and play around with them!

## Wet Lab

### Expression Gene construction

We were unable to fully ligate all 3 parts of our gene construct together. Our BioAnalyzer data did show minimal concentrations of ligated ABC insert, but this concentration wasn’t high enough for us to be able to use in the rest of the experiment (transformation+testing with PFAS). In the future, we will redesign our ligation procedure via more thorough research for the creation of the protocols and looking at other IGem teams to see attempts at a 3 part ligation. In addition, we will ensure that proper amounts of time are allocated for the digestion and ligation steps of our protocols, since this year, we had limited lab time and were therefore rushed in our experimentation. In addition, we will make sure to follow the steps of each protocol carefully, since that cost us a lot of time this year.

![](https://static.igem.wiki/teams/5029/wiki/other-images/screenshot-2023-10-12-at-8-47-50-am.png)
![](https://static.igem.wiki/teams/5029/wiki/other-images/screenshot-2023-10-12-at-8-49-34-am.png)
![](https://static.igem.wiki/teams/5029/wiki/other-images/screenshot-2023-10-12-at-8-50-03-am.png)

### Storage Vector Construction

The construction of the storage vectors was a success. We were able to put our parts (A, B, and C) into different plasmids and transform these plasmids into bacteria. With these bacteria present for the harvesting of the parts of the insert, we will be able to have quick access to the inserts in the future without having to order them from Twist when we run out. However, there were many mishaps that occurred throughout our protocols and experimentation process. From these mishaps, we now know to follow each step of the protocols carefully. We also have increased knowledge of the different protocols needed for the digestion and ligation of inserts and plasmids, which will help us be independent and efficient in the future. In the future, we will use these storage vectors and miniprep + digest them for extraction of the inserts, which will allow us to create our full insert and experiment with PFAS more thoroughly.

![](https://static.igem.wiki/teams/5029/wiki/other-images/screenshot-2023-10-12-at-8-15-46-am.png)

## Literature Search and Reverse Protein Screening

Throughout our project, our team carried out literature research in order to derive a clear plan for our project. Thanks to the USAFA iGEM Team (1), we learned that the prmA promotor region can be induced with the presence of PFAS. However, there was no clear explanation on why this occured. Thus, our team actively read through more studies and established a potential conclusion to prmA promotor’s upregulatation by PFAS chemicals. We believe that PFAS presence causes a reduction in catalase efficacy (2) leading to an H2O2 accumulation. Subsequently, the H202 accumulation causes Fis transcriptional regulator or cAMP Receptor Protein to upregulate prmA promoter region. (3) (4) (5)

Also, thanks to the Stockholm 2020 iGEM team that attempted to detect PFAS using mRFP fluorescence, we were able to build our own genetic circuit based off of known inducers and promoters from their team’s research. (6)

* https://academic.oup.com/milmed/article/186/Supplement\_1/801/6119513
* https://pubs.acs.org/doi/pdf/10.1021/es5060034
* https://onlinelibrary.wiley.com/doi/pdf/10.1046/j.1365-2958.2000.01805.x
* https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4868304/
* https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0051179
* https://2020.igem.org/Team:Stockholm

### Reverse Protein Screening

In our research, we used reverse screening to achieve four goals: (1) Find proteins that interact with PFAS (PFOA and PFOS specifically), (2) look for similarities between location/function/sequence, (3) Tie into diseases linked to PFAS See this paper for one clear mechanism for liver and kidney damage (https://www.sciencedirect.com/science/article/pii/S0160412023002246), and lastly (4) look for bacterial protein hits, especially those that can cause stress because our prmA promoter seems to be general stress activated. If we could figure out what proteins PFAS interacts with to cause a stress response then we would uncover the mechanism of prmA promoter activation in response to PFAS, something not done before. It could improve future modeling and potentially open the door to engineering more sensitive methods of detection. After our research, We found targets that bound or were related to PFOA or PFAS which in the long run were very beneficial in working on this project as we could understand what types of proteins are related and we could use them to help build our gene circuit. These databases provided lists of proteins that are related and some of them like androgen receptors were very useful and we related them to our project. Some databases that provided promising information for this research are listed below.

* superPRED: Found what PFOA binds too and the probability of binding
* Pharmmapper: Tested the ability of PFOA to bind to a large database of proteins
* Targetnet: Predicted the binding of different targets for PFOA and PFOS
* STITCH: Helped us find nodes between PFOA and PFOS and Rhodococcus Jostii
* SwissTargetPrediction: Helped solidify our findings from pharmmapper

We were also able to tie these targets to diseases or viruses and what part of the body they originate. The database searching also revealed the functions of these targets and how they could maybe be related to PFAS and PFOA as well as the probability of interaction between the proteins. This helped form the basis for the molecular modeling we would do in the future with platforms like V-Cell and OpenMM

## Molecular Dynamics Modeling

We were finally able to run the OpenMM script, and the data that was outputed was quite reasonable. This proved useful as the lab part did not go according to plan, we were still able to test a part of our construct in some way. There were some slight discrepancies in the data, such as seemingly random temperature fluctuations, and beyond that almost no change was observed before and after the simulation. This might be caused by a faulty forcefield, in which we could spend more time researching and developing a proper one instead of blindly using an online server to generate these files. This may also be a true result, but we won’t know until more iterations of the docked complex have been tested, and the data examined. Over all, there are chances that the data might be off from faulty forcefields and other data file, in which more care will be taken, but the data does match our expectations, which means this data is likely true. If the results are true, we may not need to use OpenMM for observing conformation change after docking, but it can still be used to determine other changes and record useful values.

![](https://static.igem.wiki/teams/5029/wiki/other-images/screenshot-2023-10-06-at-1-51-22-pm.png)

## Virtual Cell Modelling

Using a stochastic solver provided by VCell and constants provided by Weber and Becut, we found that in a single cell, PFAS levels as low as 0.033 uM could trigger bursts of GFP production. These bursts are consistent with the well known phenomenon that transcription and translation happen in bursts and not as a continuous curve.

![](https://static.igem.wiki/teams/5029/wiki/other-images/screenshot-2023-10-12-at-9-05-24-am.png)

Despite the relatively low amount of GFP, this is occurring in only 1 cell. In real scenarios, this burst may occur in millions of cells at once that are all exposed to PFAS, potentially providing a detectable signal to a fluorimeter. More simulation will have to be conducted on multi cell systems and potentially how AHL can diffuse through membranes and affect multiple cells.

In experiments where the binding affinity between LuxR and AHL is extremely high, bursts of GFP are also seen in different concentrations of PFAS.

![](https://static.igem.wiki/teams/5029/wiki/other-images/screenshot-2023-10-12-at-9-06-10-am.png)

In both of the above simulation scenarios, the stochastic solver predicted higher GFP concentrations than the deterministic solver did (see Engineering for more graphs for comparison). In certain instances, the deterministic solver actually predicted less than 1 molecule of GFP would be produced at all. This all highlights the importance of stochastic solvers in microscopic environments such as intracellular reaction pathways.

### Summary and Future Works

In future works, further tinkering with the estimated reaction rate constants for PFAS’s effects on transcription could provide more accurate simulations. Inclusion of multiple cells and the interplay between the signaling molecules released between each individual would also be more representative of real field conditions. However, we hope our current results can serve as a positive proof of concept in silico model for our gene circuit and also illustrate the importance of stochastic simulations in synthetic biology.

With more time, we could also re-attempt lab work and apply the lessons we learned this year to ensure correct assembly of our gene parts. We can then compare the actual sensitivity and the pattern of GFP production with our simulated Virtual Cell models.