iGEM 2024 — Team gcm-ky

##### Overview

* [General Guidelines](#guide)
* [Selective Agar Prep](#select)
* [Blue-White Screening](#screen)
* [Plate-Prep](#plate)
* [Single Transformation](#single-0)
  + [Construct 2](#single-1)
  + [Construct 1](#single-2)
* [Double Transformation](#double22)
* [Repouring of Construct 3 Plates](#redo1)
* [Selection](#selection)
  + [Construct 2](#selcon2)
  + [Construct 3](#selcon3)
  + [Construct 1](#selcon1)
* [Miniprep](#mini)
  + [Construct 2](#mini2)
  + [Construct 1](#shitter1)
  + [Construct 3](#shitter2)
* [Digest](#digester)
  + [Construct 2](#digc2)
  + [Construct 1](#shitter11)
  + [Construct 3](#shitter21)
* [Nanodrop Results](#nano1)
  + [Construct 2](#resc2n)
  + [Construct 1](#resc1n)
  + [Construct 3](#resc3n)
* [Calculations for 1μg (Digest)](#digestCalc)
  + [Construct 2](#resc2n2)
  + [Construct 1](#shitter12)
  + [Construct 3](#shitter22)
* [Miniprep of the repoured construct 3 plates](#redo321)
* [Nandrop of Construct 3 Transcription Factor](#sigma1)
* [First Gel](#geluno)
* [First Gel Layout](#gelunol)
* [Redoing all digests](#digiredo321)
  + [Nanodrop Results](#wathh)
  + [Digest](#notsigma)
* [Second Gel](#gel2)
* [OD600 Reading Trial](#od600)
* [Gel Early Reading](#early)
* [Fluorescence Reader](#fluoro)
  + [Plating Colonies](#plating)
  + [Setup](#set)
  + [Testing](#testing)
* [Storage of Plated Colonies](#finallybruh)

# General Guidelines

**Always** clean all work areas (fume hood, bench, sinks, balance area, and other work surfaces)
thoroughly before you start working, and after you complete your work in the
laboratory.
**Never** eat or drink in the laboratory and wash your hands before leaving the
laboratory.

View a PDF version of our Lab-notebook

[Lab Notebook](https://static.igem.wiki/teams/5114/vishwaa/lab-notebook-2.pdf)

---

## **Selective Agar Prep (6/8/2024) Performed by Sheila T.**

**Always** clean all work areas (fume hood, bench, sinks, balance area, and other work surfaces)
thoroughly before you start working, and after you complete your work in the
laboratory.
**Never** eat or drink in the laboratory and wash your hands before leaving the
laboratory.

---

* Proper disinfection of fume hood with 70% ethanol
* 1 L Agar prepared (32 g/L)

  + Autoclaved at 121 Celsius for 15 min
* Poured Agar into 100mL bottles in a fume hood
* Added 1mL Ampicillin stock per 100 mL agar

  + Verified that agar was warm, not hot; otherwise, the antibiotic will be deactivated
  + Ampicillin stock concentration was 10 mg/mL
* Added 1mL Kanamycin stock per 100 mL agar

  + Verified that agar was warm, not hot; otherwise, the antibiotic will be deactivated
  + Kanamycin stock was 10mg/mL
* Added 500 µl of Kanamycin and 500 µl of Ampicillin per 100 mL of Agar

  + Verified that agar was warm, not hot; otherwise, the antibiotic will be deactivated
  + Both Ampicillin and Kanamycin stocks were 10 mg/mL
* Poured 11 plates of Kanamycin, 10 plates of Ampicillin, & 3 plates of both. Each plate contained 25
  mL of agar.

## **Blue-White Screening - 7/3/2024 Performed by Vishwaa K.**

---

* Disinfected fume hood with 70% ethanol
* 100mg X-Gal was eluted in 5mL DMSO in the fume hood
* Filtered using a 0.2-micron filter

  + Removed and discarded needle from the syringe
  + Screwed on filter on the syringe
  + Poured X-Gal in DMSO in the syringe
  + Pushed plunger down and collected the X-Gal in a 10 mL tube
  + Discarded needle and syringe in the sharps container and the filter in the regulated waste container
* Aliquoted 1 mL of X-Gal in DMSO into five 1.5 mL tubes

  + Wrapped each of the five tubes in foil and stored at -20 Celsius

## **Plate Prep - 7/8/2024 Performed by Vishwaa K. and Daniel J.**

---

* For the mixed plates: (5 Plates poured and stored at 4 degrees Celsius)

  + 75 mL Agar
  + 375 µL Kanamycin
  + 750 µL Ampicillin
* For Kanamycin Only (6 plates stored at 4 Celsius)

  + 90 mL Agar
  + 450 µL Kanamycin
* All work was done in a sterile fume hood disinfected with 70% ethanol.

## **Single Transformation - 7/10/2024**

---

### **Construct 2: Performed by Vishwaa K., Edward K., Leon G.**

* Competent cells were thawed for 25 minutes on ice.
* Mixed 0.5 µL plasmid with 1.5 µL water in 1.5 mL DNA tube.
* Vortex the diluted plasmid DNA for 2 seconds.

  + Note: Edward Centrifuged for too long (20 seconds).
* Added 50 µL E. coli to DNA tube.

  + Note: Leon left some of the mix of the 1.5 mL tube in the pipette tip.
* Flicked the 1.5 mL DNA tube to mix the DNA and competent cells gently.
* Placed the tube on ice for 25 min.
* Resealed the plasmid storage tubes and returned them to the freezer.
* Heat shocked the transformation tube at 42°C for 30 seconds.
* Placed heat-shocked 1.5 mL DNA tube on ice for 2 minutes.
* Added 500 µL of SOC media to the transformation reaction.
* Placed the transformation tube on a shaker at 200 RPM and 37°C for 60 min.
* Pipetted 100 µL X-Gal and 40 µL IPTG on six kanamycin plates: 3 high-concentration (100 μg/mL) plates
  and two low-concentration (50 μg/mL) plates.
* Spread the X-GAL and IPTG evenly on each plate and let the plates dry.
* Plates were closed with lids until the bacteria were done shaking.
* Added bacteria to plates; plates had the following µL of bacteria, respectively:

  + High-concentration plates: 150 µL, 100 µL, 50 µL.
  + Low-concentration plates: 30 µL, 10 µL.
* Incubated overnight at 37°C.

### **Construct 1: Performed by Shlok J., Akhila N., James M.**

* Thawed *E. coli* bacteria on ice for approximately 25 minutes.
* Thawed plasmid DNA (containing prma promoter) and vortexed briefly.
* Added 1.5 µL of water into a separate microcentrifuge tube.
* Pipetted 0.5 µL of DNA (4 ng/mL) into the water tube to prepare the dilution mix.
* Vortexed the DNA dilution mix again.
* Placed the DNA solution back on ice.
* Added 50 µL of E. coli into the microcentrifuge tube and returned it to ice.
* Incubated the E. coli/DNA mixture on ice for 25 min.
* After the incubation, the ice bucket was taken to the water bath, and the E. coli/DNA tube was
  heat-shocked for 30 sec at 42°C.
* Returned the E. coli/DNA tube to ice for 2 min.
* Added 500 µL of SOC media to the bacteria tubes and incubated them in a shaking incubator.
* Incubated the tubes for 60 min at 37°C and 225 rpm.
* Waited 2 mins to put on shaking incubator with Construct 2.
* Pipetted 10 µL and 20 µL *E. coli*/DNA mixture onto 10cm LB selective agar plates.
* Gently spread bacteria throughout agar plate.
* Incubate plates at 37°C overnight (putting plates upside down).

## **Double Transformation of Construct 3: Performed by Aryan S, Gowshik R, Daniel J**

---

* Labeled 2, 1.5 mL tubes
* Competent cells were thawed on ice for 25 minutes
* Added 1.5 µL water into a 1.5 mL centrifuge tube
* Vortexed the tube containing the plasmid stock for approximately 2 seconds
* Added 0.5 µL plasmid DNA to the tube with water
* Added 50 µL *E. coli* to the DNA tube
* Flicked the 1.5 mL tube to mix the DNA and competent cells gently
* Placed 1.5 mL tubes on ice for 25 minutes
* Heat shocked each transformation 1.5 mL tube at 42°C for 30 seconds
* Placed heat-shocked 1.5 mL tubes on ice for 2 min
* Added 500 µL of SOC media to each transformation tube
* Placed each 1.5 mL transformation tube on a shaker at 200 RPM and 37 degrees Celsius for 60 min
* Pipetted 100 µL X-Gal and 40 µL IPTG on six kanamycin plates: 3 high-concentration

  + Note: Plates were wrongly plated, and Kanamycin and ampicillin were mixed up, resulting in abnormal
    results. So we repoured the plates.
* (100 μg/mL) plates and two low-concentration [50 μg/mL] plates.
* Spread the X-GAL and IPTG mix evenly on each plate
* After the plates dried, they were closed with lids until the bacteria were done shaking
* Added bacteria to plates; plates had this many µL of bacteria, respectively:

  + High-concentration plates: 150 µL, 100 µL, 50 µL
  + Low-concentration plates: 30 µL, 10 µL
* Incubated Overnight at 37 °C

## **Repouring Construct 3 plates (due to previous failure) - Performed by Vishwaa K.**

---

* Three types of selective plates were used, 100 µL of Kanamycin, 100 µL of Ampicillin, 100 µL of both

  + Negative controls showed no growth, so we had to redo
* Added 100 µL of X-Gal and 40 µL of IPTG on each plate
* Spread the X-GAL and IPTG evenly on each plate
* After the plates dried, they were closed with lids until the bacteria were done shaking
* Added 20 µL of bacteria to plates
* Incubated Overnight at 37°C

  + Note: Blue color means:

    - Empty vector (company didn’t correctly put construct in place of Lac Z)
    - Next time, we should purify and sequence (order by May 25 next year)

## Selection

---

### **Selection for Construct 2 - 7/11/2024 Performed by Vishwaa K.**

* Opened plates in the fume hood @ 8:30 AM the next day
* Found good growth in Construct 2 plates
* Placed in a 4°C fridge so that blue colonies can emerge

  + Note: The plate with 10 µL of bacteria worked best
* Circled white colonies on all plates with Sharpie on the back of the plate
* Poured 50 mL of LB broth into a tube
* Added 500 µL of Kanamycin to the tube & mixed by inverting
* Aliquoted 5 mL of the solution in each of 10 cell culture tubes under the fume hood
* Used Sterile Pipette tips to select a colony

  + Swiped colony with the sterile end of the pipette tip
  + Placed full pipette tip inside cell culture tube
  + After inserting the pipette tip, the cell culture tubes were closed halfway to allow the cells to
    breathe.
* Placed the tubes in the incubator at 225 rpm, 37 °C overnight
* Plates were sealed with parafilm and stored at 4 °C

### **Selection for Construct 3 7-12-24 - Aryan S, Gowshik R, Daniel J**

﻿Circled white colonies on all plates with Sharpie on the back of the plate

* Poured 50mL of LB broth in a tube
* Added 500 µL of Kanamycin to the tube & mixed by inverting
* Aliquoted 5mL of the solution in each of 10 cell culture tubes under the fume hood
* Used Sterile Pipette tips to select a colony

  + Swiped colony with the sterile end of the pipette tip
  + Placed full pipette tip inside cell culture tube
  + After inserting the pipette tip, the cell culture tubes were closed halfway to allow the cells to
    breathe
* Placed the tubes in the incubator at 225 rpm, 37 °C overnight
* Returned plates to 4 °C fridge after properly sealed with parafilm

\* Old negative controls thrown out

* Added 25 mL LB Broth in broth-Kanamycin solution tube
* Added 250 µL Ampicillin and 250 µL Kanamycin into the tube containing 25 mL LB broth

  + Mixed by inverting
* Aliquoted 5 mL of the solution to the tubes

* Circled positive colonies for the redo plates - **7/12/24**

### Construct 1

Due to other members not doing their job, proper notes of what happened were not kept. But the procedures
(in wetlab experiments page) contain the steps followed.

## Miniprep

---

### **Miniprep of Construct 2 - Performed by Vishwaa K., Leon G., and Edward K. - 7/12/2024:**

* Removed cell culture tubes at 9:50 AM from the incubator

  + Note: Leon dropped our cell culture tubes, but none spilled
* Used a miniprep kit (opened but unused)

* Aliquoted 1.5mL of each colony into a respectively labeled microcentrifuge tube

  + Meaning they were labeled Colony 1 - Colony 5
* Centrifuged at 8000 rpm and room temperature for 2 mins
* Labeled five more 1.5mL microcentrifuge tubes (Colony 1-Colony 5) for storage
* Removed supernatant of microcentrifuge tube
* Added 250 µL of Resuspension solution to each microcentrifuge tube
* Flicked tubes gently until cell clumps completely dissolved
* Added 250 µL of Lysis Solution to each tube and inverted 6 times to mix evenly
* Added 350 µL of the Neutralization Solution and mixed by inversion until cloudy
* Centrifuged the tubes at 12000 rpm and room temperature for 5 min
* Took 650 µL of each microcentrifuge tube’s supernatant and placed it into their respectively labeled
  spin column
* Centrifuged the spin columns at 12000 rpm and room temperature for 1 min
* **Repeated twice**
  + Discarded flow through of each spin column
  + Added 500 µL wash solution to each spin column
  + Centrifuged the spin columns at 12000 rpm for 1 min
* Discarded the flow-through of each spin column
* Centrifuged all the spin columns at 12000 RPM for 1 min
* Transferred the filter part of each spin column to their respectively labeled microcentrifuge tube
* Added 50 µL of elution buffer to the center of each spin column filter
* Incubated at room temperature for 2 minutes
* Centrifuged at 12000 RPM for 2 min
* Discarded filter columns and placed the microcentrifuge tubes with the flow through on ice
* Nanodropped samples for each of the five colonies

### **Construct 1: Miniprep**

Due to other members not doing their job, proper notes of what happened were not kept. But the procedures
(in wetlab experiments page) contain the steps followed.

### **Construct 3: Miniprep**

Due to other members not doing their job, proper notes of what happened were not kept. But the procedures
(in wetlab experiments page) contain the steps followed.

## Digest

---

### **Digest of Construct 2 - Performed by Vishwaa K., Leon G., and Edward K. - 7/12/2024:**

* Pipetted a master mix of 5 µL Eco31i, 25 µL G-buffer, and 120 µL water in a 1.5 mL microcentrifuge
  tubes

  + Gently Vortexed and Mixed
* Created microcentrifuge tubes with a mix of plasmid DNA and water, equaling 20 µL

  + See after nanodrop results for exact measurements
* Added 30 µL of master mix to each of the 5 microcentrifuge tubes
* Incubated the five tubes at 37 °C for 1.5 hours
* Moved the five microcentrifuge tubes of plasmid DNA to 4 °C for overnight storage and stored purified
  DNA at -20 °C

**Repoured plates for construct 3**

* Removed colonies from incubating overnight and placed at 4 °C after incubating overnight

### **Construct 1: Digest**

Due to other members not doing their job, proper notes of what happened were not kept. But the procedures
(in wetlab experiments page) contain the steps followed.

### **Construct 3: Digest**

Due to other members not doing their job, proper notes of what happened were not kept. But the procedures
(in wetlab experiments page) contain the steps followed.

## Nanodrop results

---

### **Construct 2: Nanodrop Results**

|  | ng/µL | A260/A280 | A260/A230 |
| --- | --- | --- | --- |
| **Colony 1** | 347.9 | 1.90 | 2.23 |
| **Colony 2** | 168.8 | 1.88 | 2.09 |
| **Colony 3** | 302.7 | 1.89 | 1.95 |
| **Colony 4** | 320.1 | 1.90 | 2.21 |
| **Colony 5** | 327.4 | 1.88 | 2.00 |

### **Construct 1: Nanodrop Results**

Due to other members not doing their job, proper notes of what happened were not kept. But the procedures
(in wetlab experiments page) contain the steps followed.

|  | ng/µL | A260/A280 | A260/A230 |
| --- | --- | --- | --- |
| **Colony 1** | 78 | 1.86 | 1.83 |
| **Colony 2** | 260 | 1.89 | 2.17 |
| **Colony 3** | 213 | 1.88 | 2.15 |
| **Colony 4** | 213 | 1.88 | 2.17 |
| **Colony 5** | 296 | 1.90 | 2.22 |

### **Construct 3: Nanodrop Results**

Due to other members not doing their job, proper notes of what happened were not kept. But the procedures
(in wetlab experiments page) contain the steps followed.

## Digest Calculation

---

### **Calculations for 1μg (Digest) for Construct 2**

|  | DNA | H2O |
| --- | --- | --- |
| **Colony 1** | **2.87µL** | **17.13** |
| **Colony 2** | **5.92µL** | **14.08** |
| **Colony 3** | **3.30µL** | **16.7** |
| **Colony 4** | **3.12µL** | **16.88** |
| **Colony 5** | **3.05µL** | **16.95** |

### **Calculate for 1μg (Digest) for Construct 1**

|  | DNA | H2O |
| --- | --- | --- |
| **Colony 1** | **12.821µL** | **7.179** |
| **Colony 2** | **3.846µL** | **16.154** |
| **Colony 3** | **4.695µL** | **15.305** |
| **Colony 4** | **4.695µL** | **15.305** |
| **Colony 5** | **3.378µL** | **16.622** |

### **Construct 3: Digest Calculation**

Due to other members not doing their job, proper notes of what happened were not kept. But the procedures
(in wetlab experiments page) contain the steps followed.

## **7/15/2024 Miniprep of the repoured construct 3 plates (Vishwaa K.)**

---

Performed miniprep on the TF (media culture)

* Labeled 2, 1.5mL microcentrifuge tubes (Transcription factor 1 and 2)
* And labeled 2 more 1.5mL microcentrifuge tubes for storage (Transcription factor 1 and 2)
* Aliquoted 1.5mL of each bacterial colony into a respectively labeled microcentrifuge tube

  + Meaning they were labeled Colony 1 - Colony 5
* Centrifuged at 8000 rpm and room temperature for 2 mins
* Removed supernatant of microcentrifuge tube by pouring it into the waste
* Added 250 µL of Resuspension solution to each microcentrifuge tube
* Flicked tubes gently until cell clumps completely dissolved
* Added 250 µL of Lysis Solution to each tube and inverted 6 times to mix evenly

  + Note: Shlok added too much Lysis solution to Colony 1 of the Transcription factor
* Added 350 µL of the Neutralization Solution and mixed by inversion until cloudy
* Centrifuged the tubes at 12000 rpm and room temperature for 5 min
* Took 650 µL of each microcentrifuge tube’s supernatant and placed it into their respectively labeled spin
  column

  + In Colony 2, the precipitant had not fully settled, so we did a quick spin and then took 800 µL of
    the supernatant because it had a little bit of the precipitant
* Centrifuged the spin columns at 12000 rpm and room temperature for 1 min
* **Repeated twice**

  + Discarded flow through of each spin column
  + Added 500µL wash solution to each spin column
  + Centrifuged the spin columns at 12000 rpm for 1 min
* Discarded the flow-through of each spin column
* Centrifuged all the spin columns at 12000 RPM for 1 min
* Transferred the filter part of each spin column to their respectively labeled microcentrifuge tube for
  storage
* Added 50 µL of elution buffer to the center of each spin column filter
* Incubated at room temperature for 2 minutes
* Centrifuged at 12000 RPM for 2 min
* Discarded filter columns and placed the microcentrifuge tubes with the flow through on ice
* Nanodropped purified plasmid DNA

## **Nandrop of Construct 3 Transcription Factor**

---

| Construct 3 transcription factor: | ng/uL | A260/A280 | A260/A230 |
| --- | --- | --- | --- |
| **Col 1** | 168.3 | 1.89 | 2.15 |
| **Col 2** | 141.2 | 1.86 | 1.82 |

\* used 1.5 uL for Nanodrop

## **Digestion Calculation:**

---

1000/162.3 = 5.94 C1 + 14.00 H2O = 20 uL
1000/141.2 = 7.08 C2 + 12.92 H2O = 20 uL

* Now we have 2, 20 uL tubes

* Created digest master mix of 2 uL ECO3I, 10 uL buffer g, and 48 uL of H2O
* Added 30 uL of master mix to each 20 uL tube
* Incubated the 2 tubes (50 uL) at 37°C for 1 hr
* Sealed all cell culture tubes with parafilm and stored at 4°C (4 tubes)

## **Gel**

---

* Preparation of 1% agarose gel
* 1 g agar and 100 mL of 1x TAE (Tris-acetate-EDTA)

  + 1x TAE created by adding 6 mL of TAE and 600 mL H2O
* Boiled 1% agar solution in the microwave for ~1 min

  + Should be fully dissolved (clear)
  + Placed stir bar and stirred until the solution was warm to touch
  + Added 6 uL safe dye to 100 mL gel solution

    - Used stir bar and magnetic stir plate to mix
  + Poured gel solution into the chamber
  + Placed the combs into the gel chamber (used the thicker side of the combs) and let the gel sit to
    harden

* Labeled 500µL tubes 10-19 (10 tubes)
* Labeled mini preps-DNA 10-19 as well
* Took 25 µL of miniprepped DNA and placed it into respectively labeled 500 µL tube

  + 12 didn't have the full 25 µL in the tube (construct 3, colony 3)

    - About 23 µL only
* Added 2.5 µL of Loading Dye(10x) to each 500 µL tube to bring to 1x
* Vortexed and centrifuged mini tubes to mix
* Pipetted all of the digestion reaction and Dye to respective wells on the agarose gel
* Rinsed pipette tips in buffer (Loading) between pipetting

  + Some wells were cloudy (Buffer has glycerol)
* Run the gel at 110v for gel 1h
* For ladder

  + 1.5 µL loading dye and 1 µL of 1 kb Invitrogen Track IT

**Note: Discovered one well had two samples**

* Relabelled all purified plasmid DNA

  + Vishwaa, Edward, Leon
  + Because the naming conventions used prior were confusing

## **Gel layout (1-10 horizontally and 11-19 in the second row)**

---

| 1 | C2(c1) | 6 | C3 (Gene)(c1) | 11 | C3 (B) (c3) | 16 | C3 (B) (c2) |
| --- | --- | --- | --- | --- | --- | --- | --- |
| **2** | C2(c1) | **7** | C3 (Gene)(c2) | **12** | C3 (B) (c4) | **17** | C1 (c1) |
| **3** | C2(c2) | **8** | C3 (TF)(c3) | **13** | C3 (B) (c3) | **18** | C1 (c3) |
| **4** | C2(c3) | **9** | C3 (TF)(c4) | **14** | C1 (c5) | **19** | C1 (c4) |
| **5** | C2(c4) | **10** | C1 (c2) | **15** | C3 (B) (c1) |  |  |

## 19 Digests; Redoing of all digests - 7/16/2024 - Performed by Vishwaa and Kalina

---

### **Nanodrop Results**

| Con 3 | 1 µL | ng/µL | A260/A280 | A260/A230 |
| --- | --- | --- | --- | --- |
|  | C1 (Gene) | 166.0 | 1.88 | 2.11 |
|  | C2 (Gene) | 147.5 | 1.84 | 1.93 |
|  | C1 (TF) | 205.3 | 1.86 | 1.72 |
|  | C2 (TF) | 415.5 | 1.84 | 2.18 |

| Con 3 | 1 µL | ng/µL | A260/A280 | A260/A230 |
| --- | --- | --- | --- | --- |
|  | C1 | 113.9 | 1.84 | 2.0 |
|  | C2 | 114.8 | 1.89 | 2.26 |
|  | C3 | 19.7 | 1.74 | 1.01 |
|  | C4 | 241.5 | 1.88 | 2.23 |
|  | C5 | 158.0 | 1.82 | 2.21 |

| Con 1 | 1 µL | ng/µL | A260/A280 | A260/A230 |
| --- | --- | --- | --- | --- |
|  | C1 | 70.1 | 1.86 | 2.09 |
|  | C2 | 205.3 | 1.88 | 2.22 |
|  | C3 | 234.1 | 1.89 | 2.22 |
|  | C4 | 198.8 | 1.89 | 2.25 |
|  | C5 | 285.2 | 1.89 | 2.22 |

### **Restriction Digest**

* Labeled 18 tubes for digest

  + Red: Construct 1
  + Green: Construct
  + Black: TF
  + Orange: Gene
  + Blue: Construct 3

(All microliters of sample + dH2O)

|  | Construct 1 | Construct 2 | Construct 3 | TF | Gene |
| --- | --- | --- | --- | --- | --- |
| C1 | 13 + 7 | 3 + 17 | 10 + 10 | 6 + 14 | 5 + 15 |
| C2 | 5 + 15 | 6 + 14 | 10 + 10 | 7 + 13 | 3 + 17 |
| C3 | 5 + 15 | 3.5 + 16.5 |  |  |  |
| C4 | 5 + 15 | 3.5 + 16.5 | 5 + 15 |  |  |
| C5 | 5 + 15 | 3 + 17 | 7 + 13 |  |  |

* Made digest mix

  + 90 µL of buffer G
  + 18 µL of enzyme EcoRI
  + 432 µL of H2O
  + Vortexed and spun down
  + Added 30 µL of digest mix to each tube of 20 µL (plasmid DNA and water)
  + Vortexed, spun down, and incubated at 37.0 degrees Celsius for 1.5 hours

## **Second Gel Electrophoresis**

---

* Agarose gel for checking the digested colonies:

  + 1 g agar + (100 mL dH2O + 2 mL TAE (50x))
  + Microwave to boil for 1 minute
  + Cooled by using a stir bar + hot plate (no heat) until it was warm to the touch
  + Added 6 mL safe red dye and stirred
* Poured 1% agar on the chamber
* Created 19 mini tubes with digested DNA:

  + Put 5 microliters 10x blue juice loading dye in each mini tube
  + Used 5 microliters for the ladder
* Pipetted all contents of mini tubes (digests) into gel wells
* Run the gel for 1 hour at 60 volts
* Switched to 75 volts after one hour to speed up

Ladder Layout:

![](https://static.igem.wiki/teams/5114/vishwaa/ladlayout.png)

* Making LB Broth:

  + 10g LB broth + 500 mL dH2O
  + Autoclaved broth, a bottle of 75mL of 50% Glycerol, and empty flasks

    - Opened the lid slightly, wrapped the lid in foil, and put white tape onto the lid to make sure
      the autoclave worked
    - Poured some water in the bin (up to 1 cm off the bottom)
    - Autoclaved for 66 minutes - P4 Setting (30 minutes wait for liquid cycle)

## **OD 600 reading trial - performed by Vishwaa Kannan**

---

* Created 15 microliter aliquot of colony 2 construct 2 for OD 600 reacting
* Created blank for OD of 1mL 1x LB Broth + 1 microliter Kanamycin 100x

**Reading**

| A600 | Correction | λ | A(x) |
| --- | --- | --- | --- |
| 3.11 | 0 | 500 | 3.9 |
| 3.13 | 0 | 500 | 3.97 |
| 3.13 | 0 | 500 | 3.96 |

* Meaning ~ 8x109 cells
* Diluted aliquot to ½:

| A600 | Correction | λ | A(x) |
| --- | --- | --- | --- |
| 1.44 | 0 | 500 | 1.84 |
| 1.64 | 0 | 500 | 2.07 |

## **Gel Results from Early Predictions**

---

* Took a look after 1 hour
* Constructs 1 and 2 good
* Construct 3 is ok; bands should be separated more
* TF looks ok
* Gene is missing?

  + Possibly human error
* Placed back in chamber for 1 hour at 75 volts

## **Fluorescence Reader**

---

### **Plating colonies**

* Construct 1 - prmA

  + Colony 4 Kan
  + Colony 5 Kan
* Construct 2 - FAB

  + Colony 2 Kan
  + Colony 3 Kan
* Construct 3 - Estradiol

  + Colony 5 (More TF) Both
  + Colony 1 (More gene) Both
* Construct 3 - TF

  + Colony 1 Kan
  + Colony 2 Kan
* Construct 3 - Gene (No insert, probably)

  + Colony 1 Amp
  + Colony 2 Amp
* Labeled 10 culture tubes for the plate
* Four were smaller because we ran out of large cell culture tubes (2 colonies per construct + 4 negative
  controls)
* Made stock for Kan, Amp, and Both (All mixed by inverting)

  + Kan

    - 35 µL LB Broth + 350 µL Kan (100x)
  + Amp

    - 15 µL LB Broth + 150 µL Amp (100x)
  + Both

    - 15 µL LB Broth + 150 µL Amp (100x) + 150 µL Kan (100x)
* Pipetted 5 mL or 3 mL to each tube respective of size and antibiotic
* Pipetted 5 µL or 3 µL of original media to new media respective of size and antibiotic
* Opened lid slightly
* Incubated overnight at 37 degrees Celsius and at 210 RPM

### **Adding PFOA and H2O2**

* Labeled 3 tubes for selective LB Broth

  + 30 mL Kan (29.7 mL broth + 300 µL Kan (100X))
  + 10 mL Amp (9.9 mL broth + 100 µL Amp (100X))
  + 15 mL both (14.7 mL broth + 150 µL Kan (100X) + 150 µL Amp (100X))
* PFOA Dilutions

  + 1 mL stock A\_p: 875 µL LB Broth + 125 µL of 20 mM PFOA = 2500 µM PFOA
  + 1 mL stock B\_p: 800 µL LB Broth + 200 µL of stock A\_p = 500 µM PFOA
  + 1 mL stock C\_p: 900 µL LB Broth + 100 µL of stock B\_p = 50 µM PFOA
* H2O2 Dilutions

  + 10 mL stock A\_h: 9997.45 mL LB Broth + 2.55 µL 30% H2O2 = 2500 µM H2O2
  + 1 mL stock B\_h: 800 µL LB Broth + 200 µL of stock A\_h = 500 µM H2O2
  + 1 mL stock C\_h: 900 µL LB Broth + 100 µL of stock B\_h = 50 µM H2O2
* Overnight culture + nanodrop the following day:

**Note: Letters are out of order due to a labeling mistake, but now they are correct.**

| ND # | Name | Letter | A260 | Correction | λ | A(x) |
| --- | --- | --- | --- | --- | --- | --- |
| 1 | Construct 1 C4 | A | 2.11 | 0 | 500 | 2.6 |
| 2 | Construct 1 C5 | B | 3.10 | 0 | 500 | 3.79 |
| 3 | Construct 2 C2 | C | 2.58 | 0 | 500 | 3.22 |
| 4 | Construct 2 C3 | D | 1.24 | 0 | 500 | 1.58 |
| 7 | Construct 3 C1 | G | 1.37 | 0 | 500 | 1.73 |
| 8 | Construct 3 C5 | H | 2.06 | 0 | 500 | 2.57 |
| 5 | Construct 3 TF C1 | E | 1.62 | 0 | 500 | 2.06 |
| 6 | Construct 3 TF C2 | F | 0.81 | 0 | 500 | 1.05 |
| 10 | Construct 3 Gene C1 | I | 0.95 | 0 | 500 | 1.26 |
| 11 | Construct 3 Gene C2 | J | 2.1 | 0 | 500 | 2.72 |

### **Testing fluorescence**

* Aliquots:

| Local Broth | Microliters of sample | Microliters of selective broth |
| --- | --- | --- |
| A x2 | 986 x2 = 1972 | 2074 x2 = 4148 |
| B x2 | 663 x2 = 1326 | 2397 x2 = 4794 |
| C | 799 | 2261 |
| D | 1666 | 1394 |
| G | 1513 | 1547 |
| H | 1003 | 2057 |
| E | 1275 | 1785 |
| F | 2533 | 527 |
| I | 2159 | 901 |
| J | 984 | 2024 |

* Added PFOA to all wells
* Read plate for OD + fluorescence (results in google sheets)
* **T2 Plate 2 reading ‘Too saturated’**

## **Long-term storage of colonies - performed by Vishwaa Kannan**

---

* Storage solutions of colonies used on plates

  + 500 µL 50% glycerol
  + 500 µL new colony (used old ones for remaining volumes or TF Colony 1, Colony 2)
  + Mixed carefully by turning the tubes upside down to homogenize
  + Place at -80 degrees Celsius

##### Overview

* [Part Engineering](#Part-engineering)
  + [References](#kms)
* [Vcell Engineering](#Vcell-Documentation)
  + [Cycle 1](#Cycle-1)
    - [Construct 1: prma\_GFP](#prma-GFP)- [Construct 2: FAB\_GFP](#fab-GFP)
      - [Construct 3: Synthetic Transcription Factor](#synt-tran)
      - [Construct 4: pLex\_GFP](#pLex_GFP)
  + [Cycle 2](#Cycle-2)
    [Cycle 3](#Cycle-3)
* [AI initiatives](#AI)
  [Umbrella Sampling](#Umbrella-Sampling)

### Design

Goal: Our project’s goal was to effectively detect PFAS (specifically, PFOA) by utilizing E. coli for
detection.

With this, we design 3 main pathways:

1. prmA Promoter construct: We tested the prmA promoter 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.
2. 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.
3. 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.

BsaI and SapI restriction sites were added to both sides of gene inserts before printing from Genscript to enable restriction digestion to check for the presence of the inserts in the plasmids.

### Build

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.

1. 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.
![construct 1 bws](https://static.igem.wiki/teams/5114/vcelleng/construct1-bws.jpg)2. 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.
![bws c2](https://static.igem.wiki/teams/5114/fabgfp-registry-files/blue-white-screening-c2.jpg)3. 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.
4. 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.
![c3 bws](https://static.igem.wiki/teams/5114/vcelleng/c3-bws.png)

Restriction digest (Eco31i/BsaI) and gel electrophoresis were performed on all plasmids to ensure our inserts were present

![final gel](https://static.igem.wiki/teams/5114/vcelleng/final-gel.png)

All inserts appear to be present.

### Test

1. 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.
2. 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.
3. 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.
4. 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.

PREFACE: We planned to collect data up to the 48-hour timestamp in 1.5-hour increments. However, the fluorometer we were using had technical issues, resulting in data points only up till the 3-hour timestamp.

#### prmA-GFP

Our first construct used the prmA promoter characterized by United States Air Force Academy (2019) and Stockholm (2020), as well as by previous literature.
The results of the fluorescence of our construct are shown below:

![](https://static.igem.wiki/teams/5114/result-graphs/flc1.png)

Graph 1

Graph 1 displays the fluorescence values over time of a colony from plate 1 AFTER subtracting the LB broth and PFOA solution’s fluorescence values at the corresponding times. From the graph, it’s evident that there was an increase in fluorescence as PFOA was added, as can be seen by the difference between the blue line, which represents 0 micromolar concentrations of PFOA, and the other lines, which represent higher concentrations. This difference indicates that PFOA may have led to an uptick in GFP production. However, when looking at the values of the fluorescence intensities themselves, which are all negative, show that the LB Broth with PFOA solution had a higher fluorescence than the fluorescence of the cells on their own. Although we aren’t sure of the reason behind this, it may be because of the addition of the cells, which reduced the natural fluorescence of the LB broth, or because of errors in the measurement of the fluorescence from the fluorometer. More testing is needed to determine if the constructs work.

![](https://static.igem.wiki/teams/5114/result-graphs/fnc1.png)

Graph 2

The data in Graph 2 implies that all cells produce basal fluorescence over time based on the increasing fluorescence reading across all cells. The fluorescence may be affected by PFAS since the fluorescence at any given time point appears to be ordered by PFAS concentration, however, more testing is needed to determine if the ordering is statistically significant and not an artifact of any inaccuracies in the fluorimeter’s readings.

#### prmA tested with H2O2

![](https://static.igem.wiki/teams/5114/result-graphs/flc1h2o2.png)

Graph 3

Graph 3 displays the fluorescence values over time of a colony that was taken from plate 1, containing E. coli transformed with the prmA gene construct, after subtracting the LB broth fluorescence values at the corresponding times. From this, we can see there was an uptick in GFP production with higher concentrations of H2O2 concentrations; the blue line, which represents a 0 micromolar concentration of hydrogen peroxide, has a lower fluorescence value than the other lines. However, once again we see, when looking at the values of the fluorescence intensities themselves, which are all negative, show that the LB Broth with PFOA solution had a higher fluorescence than the fluorescence of the cells on their own.

![](https://static.igem.wiki/teams/5114/result-graphs/fnc1h2o2.png)

Graph 4

The data in Graph 4 implies that all cells produce basal fluorescence over time based on the increasing fluorescence reading across all cells. The small spacing between curves makes it very difficult to establish any causality between fluorescence and H2O2, so more testing is needed to determine if the ordering is statistically significant and not an artifact of any inaccuracies in the fluorimeter’s readings.

#### FAB-GFP

The FAB-GFP mechanism was taken from previous literature (https://www.nature.com/articles/s41598-023-41953-1). According to their results, they found that the FAB-GFP complex was capable of fluorescing in E. coli after exposure to concentrations of PFAS

![](https://static.igem.wiki/teams/5114/result-graphs/flc2.png)

Graph 5

Graph 5 displays the fluorescence values over time of a colony taken from plate 1, containing E. coli taken from the FAB-GFP construct after subtracting the LB broth fluorescence values at the corresponding times. From this, we can see there was an increase in fluorescence when PFOA was added. This is evident by comparing the heights at different points of the 0 uM PFOA concentration line to the others. However, this trend doesn’t compare with the other values. The 50 μM concentration line begins by being lower than the 5 μM concentration. Despite this, from the 1.5-hour mark onwards, it remains higher than both the 5 μM and the 250 μM concentration levels. Additionally, at 0 hours, the 250 μM was lower than the 5 μM, not following the traditional trend of a direct relationship between PFOA concentration and fluorescence intensity. Since we know that the concentration of PFAS doesn’t directly correlate to the fluorescence intensity (exemplified by this graph’s data), we can still confirm the fact that PFOA concentration increases the fluorescence intensity compared to no PFOA at all.

![](https://static.igem.wiki/teams/5114/result-graphs/fnc2.png)

Graph 6

The data in Graph 6 implies that all cells produce basal fluorescence over time based on the increasing fluorescence reading across all cells. The fluorescence may be affected by PFAS since the fluorescence at any given time point appears to be ordered by PFAS concentration, however, more testing is needed to determine if the ordering is statistically significant and not an artifact of any inaccuracies in the fluorimeter’s readings.

#### Estrogen Receptor Synthetic Transcription Factor

The estrogen receptor synthetic transcription factor (STF) and its promoter was taken from previous literature (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5873372/ ). These researchers primarily developed the STF to detect various concentrations of estradiol in yeast cells. They also developed a hybrid promoter dedicated to the binding sites of the STF to upregulate the transcription of GFP. Since PFAS is known to be an agonist for estrogen receptors, it was hypothesized that the estrogen synthetic transcription factor may also bind to PFAS chemicals, causing a conformational change that may also upregulate transcription in the hybrid promoter Plex.

![](https://static.igem.wiki/teams/5114/result-graphs/flc3.png)

Graph 7

Graph 7 displays the fluorescence values over time of a colony that was taken from plate 1, containing E. coli taken from the Estrogen Receptor STF construct after subtracting the LB broth fluorescence values at the corresponding times. Since this graph doesn’t follow the previous and confirmed trend of fluorescence intensity increasing when PFAS (PFOA) is added, it can be hypothesized that when applied in real-time, this construct is less likely to be able to provide accurate results via fluorescence.

![](https://static.igem.wiki/teams/5114/result-graphs/fnc3.png)

Graph 8

The data in Graph 8 implies that all cells produce a basal fluorescence over time based on the increasing fluorescence reading across all cells. The amount of fluorescence at any time point appears to be inversely related to PFAS concentration, however more testing is needed to determine if the ordering is statistically significant and not an artifact of any inaccuracies in the fluorimeter’s readings.

Attached is the raw data from lab testing

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). For a detailed look at results, please
refer to our results page.

### Learn

Overall, all three of our constructs had inconclusive results. A common reason for this inconclusiveness may be because of the technical issues our team faced in the lab with the fluorometer. However, more issues and areas of further experimentation are listed below.

#### prmA construct

After testing E. coli transformed with the prmA-GFP construct, the results were inconclusive. According to USAFA 2019, there has been a precedent that the PrmA promoter is sensitive to PFAS concentrations. However, those findings were tested in Rhodococcus jostii, suggesting that the prmA promoter may not be induced in the presence of PFAS due to the potential lack of transcriptional elements native to Rhodococcus and absent in E. coli. Based on an extensive literature review, we hypothesize that the prmA promoter requires R. jostii specific Fis family transcriptional regulators and cAMP-receptor proteins to function properly. We confirmed these results by conducting a BLAST of R. jostii cAMP-receptor proteins and Fis family transcriptional regulators against the E. coli genome, which did not return any sequences with >50% identity. E. coli likely lacks the transcriptional machinery for the prmA promoter to function as it does in R. jostii. It was also hypothesized that the prmA promoter region was induced by an abundance of H2O2 caused by oxidative stress from the presence of PFAS. So, when our team also tested prmA-GFP in the presence of H2O2, the results were inconclusive as well. Based on our team's results, it may be that the H2O2 abundance is not the correct mechanism that causes the prmA promoter to upregulate transcription, or there may have been human error when testing concentrations of H2O2, suggesting that further research must be completed to identify the mechanism of prmA’s sensitivity to PFAS.

#### FAB-GFP

When tested at our lab to confirm, the results came out inconclusive. One major reason may be that our genetic construct had a few missense mutations, which may be the leading cause of the lack of promising results. Based on the molecular dynamics simulations, there are many possibilities for the improvement of binding affinities through mutations, which may translate to better results in a practical setting. Additionally, according to Virtual Cell results (detailed below), the limiting factor in the fluorescence of FAB-GFP is the availability of PFAS in the environment (using association constants given by the original author). Thus, this opens two avenues to explore: decreasing binding affinities even further, because of the simplicity of Virtual Cell and its models, and innovating a way to increase the fold change in fluorescence of the bound FAB-GFP compared to the unbound (essentially making the FAB-GFP fluoresce more when PFAS or a fatty acid is bound to it), which requires increased structural understanding of the protein.

#### Estrogen receptor STF construct

There may be several reasons why our fluorescence results came out inconclusive. One major reason may be that the PFAS chemicals may not bind correctly to the STF, which prevents its activation of expression on the hybrid promoter. Another reason may be that the conformational change does not correctly occur to activate expression on the hybrid promoter. In addition, there may be native key transcription factors that are necessary to induce the hybrid promoter that is only found in yeast cells and absent in E. coli, suggesting that there is significantly less transcription in our E. coli. The last contributing factor to our inconclusive results may be that the VP16 activator domain may not have functioned as desired in our E. coli. VP16 is native to herpes simplex virus proteins which mainly target eukaryotic cells. The RNA polymerase in eukaryotic cells is fundamentally different compared to prokaryotic cells, which means that the VP16 may not have successfully recruited the polymerases to the DNA as desired.

These reasons call for the need for future research on and testing to confirm the viability of the use of the estrogen receptor STF as a potential mechanism for biosensing of PFAS in E. coli.

* ↑ Mann, M. M., & Berger, B. W. (2023, September 13). A genetically-encoded biosensor for direct
  detection of perfluorooctanoic acid. Nature News. https://www.nature.com/articles/s41598-023-41953-1
* ↑ Smathers, R. L., & Petersen, D. R. (2011, March 1). The human fatty acid-binding protein family:
  Evolutionary divergences and functions - human genomics. BioMed Central.
  https://humgenomics.biomedcentral.com/articles/10.1186/1479-7364-5-3-170
* ↑ 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
* ↑ 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.
* Dossani, Z. Y., Reider Apel, A., Szmidt-Middleton, H., Hillson, N. J., Deutsch, S., Keasling, J. D., &
  Mukhopadhyay, A. (2018, March). A combinatorial approach to synthetic transcription factor-promoter
  combinations for yeast strain engineering. Yeast (Chichester, England).
  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5873372/
* ACS Publications: Chemistry journals, books, and references published ... (n.d.).
  http://pubs.acs.org/doi/full/10.1021/ja026939x?mobileUi=0

---

## VCell Engineering

### Overall Purpose

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.

## Cycle 1

In Cycle 1, we first had to build the structure for the future cycles. First, we had to research the constructs we had to design to find out how to start constructing them. We decided to first look at the prmA\_GFP construct and end with the pLex\_GFP construct. We first started with the design of the constructs. Each construct had the same geometry.

Geometry:

Cell Size: 3.5 uM^3

Environment: 1000 uM^3

Cell size was based on [Harvard Bionumbers](https://bionumbers.hms.harvard.edu/bionumber.aspx?s=n&v=3&id=100003 )

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.

Once a construct was designed, we would input rate constants and run stochastic simulations with the construct. In the simulations, we would use varying amounts of PFAS and see how much GFP was produced.

Simulation Parameters:

Time: 10,000 seconds

PFAS uM : 0.01, 0.1, 0, 1, 5, 10, 50

Solver: Gibson

Most rate constants were estimated, and few were taken from the literature review. This is a problem we would like to address in future studies, as this 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 cannot 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.

## Construct 1: prma\_GFP

### Design

We designed the reaction network based on last year's 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 (Superfolder 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 future constructs.

Cell volume was chosen as 3.5 uM^3, while the environment volume was set to 1000 uM^3, and time was measured in seconds.

Model name: pRMA\_GFP construct 1 v1.3

Owner: Pillow123

### Build

prmA\_GFP Construct #1:

![pRMA_GFP Biomodel Image](https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105032.png)

### Test & Results

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.

![pRMA_GFP graph](https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105104.png)

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.

###### Conclusion and Learnings

We can see from the data that, having a higher molarity of PFAS in 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 cannot 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.

## Construct 2: FAB\_GFP

### Design

We designed the reaction network building on a paper we found on this topic. The FAB\_GFP gene would be constantly produced by the pConst promoter and would bind with the PFAS to produce the complex. Similar to the construct before, most rate constants were estimated; however, the binding affinity between the PFAS and the FAB\_GFP protein was calculated, and other rate constants were pulled from literature studies. This was the 2nd of the 4 constructs we had, and it provided a base design for future constructs that we designed.

Cell volume was chosen as 3.5 uM^3, while the environment volume was set to 1000 uM^3, and time was measured in seconds.

Model name: FAB\_GFP\_Construct v1.1

Owner: Pillow123

### Build

prmA\_GFP Construct #1:

![FAB_GFP biomodel](https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105125.png)

### Test & Results

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.

![FAB_GFP data graph](https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105140.png)

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, a variety of colors can increase the readability of the graph as lines are now distinguished from one another.

### Conclusion and Learnings

We can see a direct relationship between the molarity of PFAS and the amount of PFAS\_FAB\_GFP being present in the cell. The higher the micromolar count, the higher the 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.

## Construct 3: Synthetic Transcription Factor

### Design

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 bound, 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 third of the 4 constructs, with the last being pLex\_GFP.

Cell volume was chosen as 3.5 uM^3, while the environment volume was set to 1000 uM^3, and time was measured in seconds.

Model name: Synthetic\_Transcription\_factorv1.1

Owner: Pillow123

### Build

SyntTranscriptionFactor\_GFP Construct #3:

![Synt_Tran biomodel](https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105200.png)

### Test & Results

We ran stochastic simulations on the reaction diagram with varying molarities 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.

![Synt_Tran data graph](https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105213.png)

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.

### Conclusion and Learnings

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. Since there was no promoter leak, so PFAS 0uM stayed at 0uM, which makes logical sense. A future study on this construct would be to simulate the effects of promoter leakage on GFP production at different PFAS concentrations.

## Construct 4: pLex\_GFP

## Design

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 first construct, the GFP variant will simply be GFP, unlike the SF\_GFP variant used in the prmA\_GFP construct. Also like the prmA\_operon, the pLex\_operon has a leak, which we estimated using scientific studies. The pLex\_operon would bind with the PFAS to create the complex, which would then produce the GFP\_mRNA.

Cell volume was chosen as 3.5 uM^3 while the Environment volume was set to 1000 uM^3 and time was measured
in seconds.

Model name: pLex\_GFP Construct

Owner: Pillow123

### Build

pLex\_GFP Construct #4:

![pLex_GFP biomodel](https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105225.png)

### Test & Results

We ran stochastic simulations on the reaction diagram with varying molarities 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.

![pLex_GFP data graph](https://static.igem.wiki/teams/5114/images-arjun/screenshot-2024-09-15-105241.png)

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.”.

### Conclusion and Learnings

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 0.01 uM 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.

## VCell Modeling Cycle 2

## Construct 3: SyntTranscriptionFactor\_GFP

### Design

Further research shows that a combination of constructs 3 and 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.

Cell volume was chosen as 3.5 uM^3 while the Environment volume was set to 1000 uM^3 and time was measured
in seconds.

Model name: Construct 3\_Cycle 2

Owner: Aryanshah16

### Build

SyntTranscriptionFactor Construct:

![SyntTranscriptionFactor_GFP](https://static.igem.wiki/teams/5114/aryan/kvekvk.png)

The previous construct 3 and 4 were combined into one Biomodel.

### Test & Results

We ran stochastic simulations on the reaction diagram with varying molarities 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.

![GFP_Graph](https://static.igem.wiki/teams/5114/aryan/vjvejievrji.png)

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.

### Conclusion and Learnings

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 in similar amounts based on each volume of PFAS.

## Cycle 3

### Design

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 1 ppt 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 are 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.

### Build

Model name: Construct3\_Cycle3\_dgl
Model owner: dglVcell

![stf pathway](https://static.igem.wiki/teams/5114/vcelleng/stf-pathway.png)

Model name: FAB\_GFP\_Construct\_v1.1\_Douglas
Model owner: dglVcell

![fab pathway](https://static.igem.wiki/teams/5114/vcelleng/fab-pathway.png)

### Test

Simulations were run for 100 minutes (6000 seconds). Every plot shown is stochastic using the Gibson
solver.

![vcell plots](https://static.igem.wiki/teams/5114/vcelleng/vcellplots.jpg)

The top 2 graphs are for the synthetic transcription factor construct. The bottom two graphs are for the FAB-GFP construct.

### Learn

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.

---

## AI Initiatives

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.

---

## Umbrella Sampling

### Overall Purpose

The Umbrella Sampling technique used in molecular dynamics is especially useful in this project as it manually enhances the sampling of complex energy landscapes of protein data banks, involving free energy calculations and calculations of other thermodynamic variables. It can overcome large energy barries and simulate rare events that other rudimentary simulations would not be able to achieve.

---

### Design

The program was designed as a pipeline between various molecular dynamics tools. However, the most important part of the pipeline is the Umbrella Sampling process indicated by the caption on the arrow between the windows rectangle and metafile/cv files rectangle.

![](https://static.igem.wiki/teams/5114/umbrellasampling/mermaid-diagram-2024-09-28-193939.png)

### Build

Below is the pseudocode outlining the program above including the SMD pulling loop, Umbrella Sampling, wham analysis, and binding coefficient algorithm. This paraphrases the code itno simple words, and the full code in python is in the software gitlab repository.

```
                BEGIN

                # Setup Directories
                DEFINE master_path as current directory
                DEFINE test_path as subdirectory "tests"
                DEFINE forcefield_path, pdb_path
                IF directories do not exist
                    CREATE necessary directories (e.g., pdb, windows, cv, hist)

                # Test Folder Naming
                SET test_name = nextFolder()
                DEFINE new_path as directory for current test
                IF new_path does not exist
                    CREATE directory structure for new test (cv, windows, hist)

                PRINT "New folder created"

                # Initialize System
                LOAD PDB file from pdb_path
                CREATE Modeller to check and add missing hydrogens
                APPLY forcefield to the system

                # Define System and Simulation
                SET system with forcefield, constraints, and parameters
                INITIALIZE Langevin integrator for temperature and timestep
                SET positions and velocities for the system
                MINIMIZE system energy

                # Parameters for Steered Molecular Dynamics (SMD)
                DEFINE starting and ending values for collective variable (CV)
                DEFINE pulling force constant, velocity, and number of windows
                DEFINE steps and recording intervals for simulation

                # Add pulling force to system
                CREATE pullingForce as harmonic force on CV
                ADD pullingForce to the system
                INITIALIZE simulation with pulling force

                # Perform Steered Molecular Dynamics (SMD) Loop
                FOR each window
                    PERFORM simulation steps
                    UPDATE pulling force parameter r0
                    RECORD collective variable (CV) values
                    IF CV reaches target value for the current window
                        SAVE system coordinates
                    END IF
                    UPDATE progress bar
                END FOR

                # Save Windows and CV Data
                FOR each saved window
                    SAVE window coordinates as PDB file
                END FOR

                # Run Simulations on Each Window
                DEFINE function run_window(window_index)
                    LOAD coordinates from corresponding window
                    SET system positions to window coordinates
                    RESET temperature and run simulation for steps
                    RECORD collective variable (CV) values for each window
                    SAVE CV values to file
                END FUNCTION

                CALL run_window() for each window

                # Generate Histogram and Save
                FOR each window
                    LOAD CV data
                    GENERATE histogram of CV values
                    SAVE histogram plot
                    CREATE metafile entry for WHAM analysis
                END FOR

                # WHAM Analysis
                PREPARE WHAM input arguments
                RUN WHAM on metafile to compute PMF
                SAVE WHAM output and log

                # Post-WHAM Processing
                LOAD PMF data from WHAM output
                PARSE coordinates, free energies, and probabilities
                PLOT free energy and probability graphs
                SAVE graph as PNG file

                # Compute Equilibrium Constant (Kd)
                COMPUTE delta_G_bound and delta_G_unbound from PMF
                CALCULATE standard free energy difference (ΔG)
                COMPUTE dissociation constant (Kd) from ΔG
                OUTPUT Kd value

                END
```

### Test & Results

This program was tested with the PDB that had protonated PFOA in FAB-GFP. Although deprotonated PFOA could be simulated in antechamber, we were unable to generated a forcefield file for the program to use to simulate deprotonated PFOA. Thus we had to use protonated pfoa where the forcefield was able to generate with a program called LigParGen, named and stored as:

```
umbrellasampling/tests/forcefields/pfoa_protonated.xml
```

**1.** The protonated PFOA in the PDB was made by manually adding a hydrogen atom to PFOA through ChimeraX and edited into the FAB-GFP again.
**2.** To prevent NaN coordinates, the simulation's energy was minimzied by moving the protiens and PFOA around. The conformal differences of:
**UNL 1** Refers to the protonated PFOA, and the conformational changes of the protein fab-gfp in window 0 that follow from the minimized energy process (via openMM) is in red. The added hydrogen according to ChimeraX is in solid green.
**UNL 377** Refers to the deprotonated PFOA, and the conformational changes of the protein fab-gfp that follow from the minimized energy process (via antechamber) is in green with reduced opacity.

![](https://static.igem.wiki/teams/5114/umbrellasampling/t4-t0-fabgfp-test/overallcomp.png)
![](https://static.igem.wiki/teams/5114/umbrellasampling/t4-t0-fabgfp-test/ligandcomp2.png)

The setup of the test includes the following paraemters:

| Li | Lf | index1 | index2 | num\_win | fc\_pull | v\_pulling | total\_steps | increment\_steps | wTotal | wDelta |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 3.0 nm | 3.2 nm | 14 (Hydrogen UNL 1) | 5439 (Hydrogen SER 349) | 100 | 1000.0 kj/mol/nm^2 | 0.02 nm/pc | 30,000 | 10 | 100,000 | 10 |

The window 0 pdb file looks as follows with the two atoms that were pulled indicated:

![](https://static.igem.wiki/teams/5114/umbrellasampling/t4-t0-fabgfp-test/image6.png)

The results of the PMF graph and histogram graph are as shown below.

![](https://static.igem.wiki/teams/5114/umbrellasampling/t4-t0-fabgfp-test/pmf.png)
![](https://static.igem.wiki/teams/5114/umbrellasampling/t4-t0-fabgfp-test/histogram.png)

The Kd and delta G calculated from the PMF graph is:

| Dissociation Constant | ΔG° |
| --- | --- |
| 2.26 M | 0.48224 kcal/mol |

### Learn

Although we were unable to simulate deprotonated PFOA with the program, the major takeaways with the test was the stark differences in the behaviors of protonated and deprotonated PFOA.
**1.** When the energy was minimized, the PFOA was put outside the binding pocket of FAB-GFP, thus the very high dissociation constant, or low binding strength, was explained by this result.
**2.** The deprotonated PFOA had a much lower dissociation constant, thus much higher bonding strength, explained by being inside the binding pocket of FAB-GFP.

## References

* 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
* 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
* 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
* 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
* 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.
* 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

##### Overview

* [Notice](#Overview)
* [Schedule](#Schedule)
* [Single Transformation Protocol](#Single)
* [Double Transformation Protocol](#Double)
* [Blue-White Screening Protocol](#Blue-White)
* [Selection Protocol](#Selection)
* [Miniprep Protocol](#Miniprep)
* [Storage Protocol](#Storage)
* [Exposure Protocol](#Exposure)

# Notice

Download the PDF version of our lab procedures, written by directors Vishwaa and Daniel, here:

[Lab Procedures](https://static.igem.wiki/teams/5114/documentation-files/2024-lab-procedures-1.pdf)

---

## 7/9/2024 Lab Schedule By: Vishwaa Kannan

| Day | Schedule |
| --- | --- |
| Wednesday | Transformation Blue White Screening |
| Thursday | Blue White Screening Select colony to grow in media (white colonies) |
| Friday | Mini-prep (19 tubes)Set up digestion for gel (if time) (maybe overnight) |
| Monday | Digest Run Gel Copies for Storage |
| Tuesday | PFAS Exposure |
| Wednesday-Friday | Time to redo parts, fix mistakes, etc. |

---

## 6-17-2024 **Single Transformation** by Daniel Jiang

---

**Bacteria Strain:**

1. **E. coli DH5α™ Competent Cells**

**Protocol:**

1. Thaw bacteria on ice ~ **20-30 mins**
2. **Gently** mix cells
3. Aliquot **50 μl** of E. coli in microcentrifuge tube **\*\*\* label carefully**
4. Add 1.5 μl of H20 into a different microcentrifuge tube
5. **Pipette** 0.5 μl of DNA (4ng/4ul) into H2O tube dilution

   Mix.
6. **Pipette** the 2.0ul diluted DNA into the 50ul E. coli tube
7. Incubate the e-coli/DNA mixture on ice **~20-30 mins.**
8. **Heat shock** each e-coli/DNA tube by placing bottom 50% - 66% of the tube in water bath
   **30 sec @ 42°C**
9. **Place** e-coli/DNA tubes on ice **~2 min.**
10. **Add** 250-1,000 μl **Antibiotic-ABSENT** LB or SOC media to bacteria tubes
    and grow in shaking incubator (**document which media was used)**

    **60 min @ 37°C 225 rpm**
11. Pipette 10 and 20 μl e-coli/DNA mixture onto a 10 cm LB **selective** agar plate.

    1. **\*\*Blue White Screening + selective agar in one plate**
12. Gently spread bacteria throughout agar plate
13. Incubate plates **@ 37°C O/N**

**Properly dispose and clean up**

---

## 6-17-2024 **Double Transformation** by Vishwaa Kannan

**Note**

* **3** instances in parallel for this protocol
* Concentration of the DNA Plasmids **5 ng/ul**
* **Both** construct 1 and 2

**Bacteria Strain:**

1. **E. coli DH5α™ Competent Cells**

**Protocol:**

1. Thaw bacteria on ice ~ **5 minutes**
2. Aliquot 50 μl of E. coli in microcentrifuge tube **\*\*\* label carefully**
3. Add 1 μl of H20 into a different microcentrifuge tube
4. Pipette 0.5 μl DNA 1 (4mg/4ul) into H2O tube dilution + 0.5 ul DNA 2 (4mg/4ul)
5. Pipette the 2.0ul diluted DNA into the 50ul E. coli tube
6. Incubate the e-coli/DNA mixture on ice **~20-30 mins.**
7. Heat shock each e-coli/DNA tube by placing bottom 50% - 66% of the tube in water bath

   **30 sec @ 42°C**
8. Place e-coli/DNA tubes on ice **~2 min.**
9. Add 250-1,000 μl Antibiotic-ABSENT LB or SOC media to bacteria tubes and grow in shaking incubator

   **60 min @ 37°C 225 rpm\*\*\* Document what media you end up using**
10. Pipette 10 and 20 μl e-coli/DNA mixture onto a 10 cm LB SELECTIVE agar plate.

    **\*\*\*\*Blue White Screening + selective agar in one plate**
11. Gently spread bacteria throughout agar plate
12. Incubate plates @ 37°C O/N

**Properly dispose and clean up**

---

## 7-9-2024 **Blue White Screening Protocol** by Vishwaa Kannan

**Bacteria Strain:**

1. **E. coli DH5α™ Competent Cells / DNA +**

**Reagents:**

1. **X-Gal**
2. **Dimethyl sulfoxide (DMSO)**
3. **dH2O**
4. **Isopropyl β-D-1-thiogalactopyranoside, IPTG (100 mM)**

**Protocol:**

1. Prepare 20 mg/ml of X-Gal in DMSO

   * 5 ml of DMSO
   * 100 μg of X-Gal
   * \*\***Store at -20° C, wrap in foil**
2. 1 ml aliquots should be made of X-Gal in DMSO **(completed up to here)**

   ```
                 **Start plate preparation while bacteria is in the shaker**
   ```
3. Add ~6.5 µl of 20 mg/ml X-Gal solution per 1 ml of media on the surface of the selective agar plate

   * 100 µl per plate
4. Add ~2.5 µl of 20 mg/ml X-Gal solution per 1 ml of media on the surface of the selective agar plate

   * 40 µl per plate
5. Smear with spreader on top of the selective agar plate
6. Air dry plate in fume hood

**Properly dispose and clean up**

---

## 7-9-2024 **Selection protocol** by Vishwaa Kannan

**Bacteria Strain:**

1. **E. coli DH5α™ Competent Cells / DNA +**

**Materials:**

1. **LB Broth**
2. **Antibiotics (Kanamycin, Ampicillin, Both)**
3. **15 ml Tubes**
4. **Toothpicks**

**Protocol:**

1. Prepare 5 ml in each 15 ml tube

   1. **Make 5 tubes per transformation**
   2. **Make 2 tubes per negative control**
2. Pick a white colony for each tube using toothpicks and place it into a 15 ml tube
3. Keep at **37° C @225 RPM Overnight**

**Properly dispose and clean up**

---

## 7-9-2024 **Mini-prep Protocol** by Vishwaa Kannan

**Bacteria Strain:**

1. **E. coli DH5α™ Competent Cells / DNA +**

**Materials:**

1. **GeneJET mini-prep kit K0502**

**Protocol:**

1. Add the provided RNase A solution to the Resuspension Solution and mix, can be used for 6 months when
   stored at 4°C
2. Add ethanol (96-100%) to the Wash Solution prior to first use, total volume of **55 ml**

   1. 20 ml wash solution
   2. 35 ml Ethanol
3. If salt precipitation occurs on Lysis Solution and the Neutralization Solution

   1. Redissolve any precipitate by warming the solution at 37°C
   2. Cool to 25°C before use \*\***Don’t shake too vigorously**
4. Centrifuge selected colony (which was in media overnight) at **8000 rpm** (6800 × g) in a
   microcentrifuge for 2 min at room temperature

   1. Decant the supernatant and remove all remaining medium
5. Resuspend the pelleted cells in 250 µL of the Resuspension Solution

   1. Transfer the cell suspension to a microcentrifuge tube
   2. Bacteria should be resuspended completely by vortexing or pipetting up and down until no cell clumps
      remain
6. Add 250 µL of the Lysis Solution

   1. Mix thoroughly by inverting the tube 4-6 times until the solution becomes viscous and slightly clear
7. Add 350 µL of the Neutralization Solution

   1. Mix immediately and thoroughly by inverting the tube 4-6 times
   2. Note: It is important to mix thoroughly and gently after the addition of the Neutralization Solution
      to avoid localized precipitation of bacterial cell debris. The neutralized bacterial lysate should
      become cloudy.
8. Centrifuge for 5 min **@>12000 × g, 10 000-14 000 rpm (table-top centrifuge should
   suffice)** to pellet cell debris and chromosomal DNA
9. Transfer the supernatant to the supplied GeneJET spin column by decanting or pipetting **Avoid
   disturbing or transferring the white precipitate**
10. Centrifuge for 1 min **@>12000 × g, 10 000-14 000 rpm (table-top centrifuge should
    suffice)**

    1. discard the flow-through
    2. place the column back into the same collection tube
11. Add 500 µL of the Wash Solution to the GeneJET spin column

    1. Centrifuge for 30-60 seconds **@>12000 × g, 10 000-14 000 rpm (table-top centrifuge should
       suffice)**
    2. discard the flow-through
    3. Place the column back into the same collection tube
    4. **\*\*REPEAT (STEP 11)**
12. Discard the flow-through

    1. centrifuge for an additional 1 min **@>12000 × g, 10 000-14 000 rpm (table-top centrifuge
       should suffice)** to remove residual Wash Solution
    2. \*\***ESSENTIAL to remove ethanol**
13. Transfer the GeneJET spin column into a fresh 1.5 mL microcentrifuge tube

    1. Add 50 µL of the Elution Buffer to the center of GeneJET spin column membrane to elute the plasmid
       DNA

       1. \*\***Take care not to contact the membrane with the pipette tip**
    2. Incubate for 2 min at room temperature and centrifuge for 2 min **@>12000 × g, 10 000-14 000
       rpm**
    3. Note. An additional elution step (optional) with Elution Buffer or water will recover residual DNA
       from the membrane and increase the overall yield by 10-20%. For elution of plasmids or cosmids >20
       kb, prewarm Elution Buffer to 70°C before applying to silica membrane.
14. Discard the column and store the purified plasmid DNA at **-20°C**

**Properly dispose and clean up**

---

## 7-9-2024 **Storage Protocol** by Vishwaa Kannan

**Bacteria Strain:**

1. **E. coli DH5α™ Competent Cells / DNA +**

**Protocol:**

1. Add 400 μL of the overnight culture to 400 μL of 50% glycerol in a 1 mL screw-top tube/cryovial and
   gently mix

   1. Make the 50% glycerol solution by diluting 100% glycerol in dH20
   2. *Snap top tubes are not recommended as they can open unexpectedly at -80°C*
2. Freeze the glycerol stock tube at -80°C

   1. Subsequent freeze and thaw cycles reduce shelf life

---

## 6-17-2024 **PFAS Exposure** by Daniel Jiang

**IMPORTANT: Visit this google sheet for complete exposure protocol and PFAS plate set up:**

[Google Sheets](https://docs.google.com/spreadsheets/d/1B4hUZd8jT0iZLK4qdNwc6RJfuZfvXbdZWtUS1HCPTlw/edit?usp=sharing)

**Bacteria Strain:**

1. **E. coli DH5α™ Competent Cells / DNA +**

**Reagents:**

1. **10uM PFOA Stock Solution**

**Protocol:**

1. | Dilute PFOA Stock Solutions | Amount |
   | --- | --- |
   | 0 uM PFAS (Only liquid broth) | 5 ml |
   | 20 ng/ml | 5 ml |
   | 40 ng/ml | 5 ml |
   | 60 ng/ml | 5 ml |
2. Collect all E. coli/DNA+ cells in liquid broth 5ml
3. Centrifuge E. coli/DNA+ vials 5 min @ 1000rpm
4. Suction out all liquid broth

   Isolating bacteria cells only
5. Pipette PFOA solutions into E. coli/DNA+ vial
6. Mix thoroughly by pipetting up and down

   **\*\*\*** Try not to spill
7. Incubate vial 30min @ 37°C

**Properly dispose and clean up**

---

##### Overview

* [Molecular Dynamics Results](#results-md1)
  + [Mutation Table](#mutty1)
* [Wet Lab Results](#wet)
  + [prmA-GFP](#prma)
  + [FAB-GFP](#fab1)
  + [Estrogen-Receptor](#estro)
  + [Conclusion](#conclusion)
    - [prmA Construct](#prmaC)
    - [FAB-GFP construct](#fabC)
    - [Estrogen-Receptor](#estroC)
* [Kinetic modeling with Virtual Cell](#vcell1)
  + [Construct 2](#second)
  + [Construct 3](#third)

## Results

---

The initial docking of hlFAB with PFOA was successful, providing a solid foundation for further analysis.
Using Autodock Vina, the docking procedure predicted an initial binding free energy (ΔG) of -11.3016
kcal/mol for the wild-type hlFAB bound to PFOA. This value reflects the strength of the interaction between
the protein and the ligand, where a more negative ΔG indicates a stronger, more favorable binding.

To better understand the significance of this result, we calculated the dissociation constant (Kd) from the
ΔG using the following equation:

```
ΔG = RT * ln(Kd)
```

Where:

1. R, ideal gas constant, (1.987 \* 10^-3 cal/mol·K)
2. Temperature (300K)
3. ΔG is the binding free energy (-11.3016 Kcal/mol)

By rearranging the equation to solve for ΔG:

```
e(ΔG / RT) = Kd
```

Substituting in the values, we get:

5.836 nano Molar

This calculation gives a dissociation constant (Kd) of approximately 5.836 nM, which indicates strong
binding. Since Kd means the concentration required of the ligand to bind to half of all the receptors, a low
Kd, especially in the nanomolar range, signifies that the ligand (PFOA) binds tightly to the protein
(hlFAB), making it an effective candidate for detection in our experimental work.

### Mutation Results Table

After running the first MMPBSA, we generated a graph from the python script, in order to see the charge
decomposition:

![](https://static.igem.wiki/teams/5114/image-5.png)

We used this graph to determine likely residues to mutate.

To track the impact of various mutations on the binding affinity, we created a table comparing the ΔG
values and calculated Kd for each mutation. These results show how each mutation influences the
interaction between hlFAB and PFOA, and whether the changes enhance or weaken binding strength.

| Mutation | ΔG (kcal/mol) | Kd (M) | Effect on Binding Strength | Why we chose this mutation |
| --- | --- | --- | --- | --- |
| Wild type + PFOA | -11.3016 | 5.8359 x 10^-9 | N/A | Baseline |
| Wild type + Palmitic Acid | -2.4944 | 0.0152 | N/A | To compare PFOA to it’s natural ligand. |
| ILE 52 to ARG + PFOA | -10.0965 | 4.4065 x 10^-8 | Slightly worse | Due to this residue having a very small contribution (thin slice of the pie graph) to the bind, changing this residue to a positively charged resiude (ARG) would increase the binding strength to PFOA, since PFOA has a net charge of -1. |
| ILE 308 to ARG + PFOA | -13.1878 | 2.4654 x 10^-10 | Great improvement. | Due to this residue having a very small contribution (thin slice of the pie graph) to the bind, changing this residue to a positively charged resiude (ARG) would increase the binding strength to PFOA, since PFOA has a net charge of -1. |
| SER 349 to THR + PFOA | -3.1394 | 0.0051 | Severly worse | In the binding pocket, There is a Serine and a Threonine that interact with the oxygens on PFOA, and they form hydrogen bonds between FAB and PFOA. So the idea was to make the two residues that interact with PFOA the same. ![](https://static.igem.wiki/teams/5114/image-6.png) |
| PHE 50 to ARG + PFOA | -6.9105 | 9.2318 x 10^-6 | Severly worse | Due to this residue having a very small contribution (thin slice of the pie graph) to the bind, changing this residue to a positively charged resiude (ARG) would increase the binding strength to PFOA, since PFOA has a net charge of -1. |
| THR 351 to SER + PFOA | -12.2177 | 1.2550 x 10^-9 | Slightly worse | In the binding pocket, There is a Serine and a Threonine that interact with the oxygens on PFOA, and they form hydrogen bonds between FAB and PFOA. So the idea was to make the two residues that interact with PFOA the same. ![](https://static.igem.wiki/teams/5114/image-6.png) |

This table organizes the results of each mutation, highlighting how the mutations affect the protein's
ability to bind with PFOA. For example, the ILE 308 mutation shows an improvement in binding, while the
SER 349 mutation significantly weakens the interaction. These findings provide clear direction on which
mutations could improve the lower detection limit of hlFAB, and helps identify key residues such as SER
349 and THR 351.

## Wet Lab Results

---

Our iGEM Team’s research goal was to design an efficient mechanism to detect PFAS chemicals in the bacterial species DH5-Alpha E. coli. To do so, our team developed three potential pathway mechanisms: prmA Promoter, FAB-GFP, and Estrogen Synthetic Transcription Factor. After testing all three pathways, our results were inconclusive, suggesting that more testing and future research are needed.

PREFACE: We planned to collect data up to the 48-hour timestamp in 1.5-hour increments. However, the fluorometer we were using had technical issues, resulting in data points only up till the 3-hour timestamp.

---

### prmA-GFP

Our first construct used the prmA promoter characterized by United States Air Force Academy (2019) and Stockholm (2020), as well as by previous literature.

The results of the fluorescence of our construct are shown below:

![](https://static.igem.wiki/teams/5114/result-graphs/flc1.png)

Graph 1

Graph 1 displays the fluorescence values over time of a colony from plate 1 AFTER subtracting the LB broth and PFOA solution’s fluorescence values at the corresponding times. From the graph, it’s evident that there was an increase in fluorescence as PFOA was added, as can be seen by the difference between the blue line, which represents 0 micromolar concentrations of PFOA, and the other lines, which represent higher concentrations. This difference indicates that PFOA may have led to an uptick in GFP production. However, when looking at the values of the fluorescence intensities themselves, which are all negative, show that the LB Broth with PFOA solution had a higher fluorescence than the fluorescence of the cells on their own. Although we aren’t sure of the reason behind this, it may be because of the addition of the cells, which reduced the natural fluorescence of the LB broth, or because of errors in the measurement of the fluorescence from the fluorometer. More testing is needed to determine if the constructs work.

![](https://static.igem.wiki/teams/5114/result-graphs/fnc1.png)

Graph 2

The data in Graph 2 implies that all cells produce basal fluorescence over time based on the increasing fluorescence reading across all cells. The fluorescence may be affected by PFAS since the fluorescence at any given time point appears to be ordered by PFAS concentration, however, more testing is needed to determine if the ordering is statistically significant and not an artifact of any inaccuracies in the fluorimeter’s readings.

In addition to testing the prmA promoter with PFOA, we attempted to test it with H2O2 concentrations to determine if our proposed mechanism of action is plausible.

#### Construct 1 Tested with H2O2 Concentrations:

![](https://static.igem.wiki/teams/5114/result-graphs/flc1h2o2.png)

Graph 3

Graph 3 displays the fluorescence values over time of a colony that was taken from plate 1, containing E. coli transformed with the prmA gene construct, after subtracting the LB broth fluorescence values at the corresponding times. From this, we can see there was an uptick in GFP production with higher concentrations of H2O2 concentrations; the blue line, which represents a 0 micromolar concentration of hydrogen peroxide, has a lower fluorescence value than the other lines. However, once again we see, when looking at the values of the fluorescence intensities themselves, which are all negative, show that the LB Broth with PFOA solution had a higher fluorescence than the fluorescence of the cells on their own.

![](https://static.igem.wiki/teams/5114/result-graphs/fnc1h2o2.png)

Graph 4

The data in Graph 4 implies that all cells produce basal fluorescence over time based on the increasing fluorescence reading across all cells. The small spacing between curves makes it very difficult to establish any causality between fluorescence and H2O2, so more testing is needed to determine if the ordering is statistically significant and not an artifact of any inaccuracies in the fluorimeter’s readings.

### FAB-GFP:

The FAB-GFP mechanism was taken from previous literature (https://www.nature.com/articles/s41598-023-41953-1). According to their results, they found that the FAB-GFP complex was capable of fluorescing in E. coli after exposure to concentrations of PFAS.

The results of the fluorescence for our construct are shown below:

![](https://static.igem.wiki/teams/5114/result-graphs/flc2.png)

Graph 5

Graph 5 displays the fluorescence values over time of a colony taken from plate 1, containing E. coli taken from the FAB-GFP construct after subtracting the LB broth fluorescence values at the corresponding times. From this, we can see there was an increase in fluorescence when PFOA was added. This is evident by comparing the heights at different points of the 0 uM PFOA concentration line to the others. However, this trend doesn’t compare with the other values. The 50 μM concentration line begins by being lower than the 5 μM concentration. Despite this, from the 1.5-hour mark onwards, it remains higher than both the 5 μM and the 250 μM concentration levels. Additionally, at 0 hours, the 250 μM was lower than the 5 μM, not following the traditional trend of a direct relationship between PFOA concentration and fluorescence intensity. Since we know that the concentration of PFAS doesn’t directly correlate to the fluorescence intensity (exemplified by this graph’s data), we can still confirm the fact that PFOA concentration increases the fluorescence intensity compared to no PFOA at all.

![](https://static.igem.wiki/teams/5114/result-graphs/fnc2.png)

Graph 6

The data in Graph 6 implies that all cells produce basal fluorescence over time based on the increasing fluorescence reading across all cells. The fluorescence may be affected by PFAS since the fluorescence at any given time point appears to be ordered by PFAS concentration, however, more testing is needed to determine if the ordering is statistically significant and not an artifact of any inaccuracies in the fluorimeter’s readings.

### Estrogen Receptor Synthetic Transcription Factor

The estrogen receptor synthetic transcription factor (STF) and its promoter was taken from previous literature (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5873372/ ). These researchers primarily developed the STF to detect various concentrations of estradiol in yeast cells. They also developed a hybrid promoter dedicated to the binding sites of the STF to upregulate the transcription of GFP. Since PFAS is known to be an agonist for estrogen receptors, it was hypothesized that the estrogen synthetic transcription factor may also bind to PFAS chemicals, causing a conformational change that may also upregulate transcription in the hybrid promoter Plex.

The results of the fluorescence of our construct are shown below:

![](https://static.igem.wiki/teams/5114/result-graphs/flc3.png)

Graph 7

Graph 7 displays the fluorescence values over time of a colony that was taken from plate 1, containing E. coli taken from the Estrogen Receptor STF construct after subtracting the LB broth fluorescence values at the corresponding times. Since this graph doesn’t follow the previous and confirmed trend of fluorescence intensity increasing when PFAS (PFOA) is added, it can be hypothesized that when applied in real-time, this construct is less likely to be able to provide accurate results via fluorescence.

![](https://static.igem.wiki/teams/5114/result-graphs/fnc3.png)

Graph 8

The data in Graph 8 implies that all cells produce a basal fluorescence over time based on the increasing fluorescence reading across all cells. The amount of fluorescence at any time point appears to be inversely related to PFAS concentration, however more testing is needed to determine if the ordering is statistically significant and not an artifact of any inaccuracies in the fluorimeter’s readings.

### Conclusion

Overall, all three of our constructs had inconclusive results. A common reason for this inconclusiveness may be because of the technical issues our team faced in the lab with the fluorometer. However, more issues and areas of further experimentation are listed below.

#### prmA Construct

After testing E. coli transformed with the prmA-GFP construct, the results were inconclusive. According to USAFA 2019, there has been a precedent that the PrmA promoter is sensitive to PFAS concentrations. However, those findings were tested in Rhodococcus jostii, suggesting that the prmA promoter may not be induced in the presence of PFAS due to the potential lack of transcriptional elements native to Rhodococcus and absent in E. coli. Based on an extensive literature review, we hypothesize that the prmA promoter requires R. jostii specific Fis family transcriptional regulators and cAMP-receptor proteins to function properly. We confirmed these results by conducting a BLAST of R. jostii cAMP-receptor proteins and Fis family transcriptional regulators against the E. coli genome, which did not return any sequences with >50% identity. E. coli likely lacks the transcriptional machinery for the prmA promoter to function as it does in R. jostii. It was also hypothesized that the prmA promoter region was induced by an abundance of H2O2 caused by oxidative stress from the presence of PFAS. So, when our team also tested prmA-GFP in the presence of H2O2, the results were inconclusive as well. Based on our team's results, it may be that the H2O2 abundance is not the correct mechanism that causes the prmA promoter to upregulate transcription, or there may have been human error when testing concentrations of H2O2, suggesting that further research must be completed to identify the mechanism of prmA’s sensitivity to PFAS.

#### FAB-GFP construct

When tested at our lab to confirm, the results came out inconclusive. One major reason may be that our genetic construct had a few missense mutations, which may be the leading cause of the lack of promising results. Based on the molecular dynamics simulations, there are many possibilities for the improvement of binding affinities through mutations, which may translate to better results in a practical setting. Additionally, according to Virtual Cell results, the limiting factor in the fluorescence of FAB-GFP is the availability of PFAS in the environment (using association constants given by the original author). Thus, this opens two avenues to explore: decreasing binding affinities even further, because of the simplicity of Virtual Cell and its models, and innovating a way to increase the fold change in fluorescence of the bound FAB-GFP compared to the unbound (essentially making the FAB-GFP fluoresce more when PFAS or a fatty acid is bound to it), which requires increased structural understanding of the protein.

#### Estrogen receptor STF construct

There may be several reasons why our fluorescence results came out inconclusive. One major reason may be that the PFAS chemicals may not bind correctly to the STF, which prevents its activation of expression on the hybrid promoter. Another reason may be that the conformational change does not correctly occur to activate expression on the hybrid promoter. In addition, there may be native key transcription factors that are necessary to induce the hybrid promoter that is only found in yeast cells and absent in E. coli, suggesting that there is significantly less transcription in our E. coli. The last contributing factor to our inconclusive results may be that the VP16 activator domain may not have functioned as desired in our E. coli. VP16 is native to herpes simplex virus proteins which mainly target eukaryotic cells. The RNA polymerase in eukaryotic cells is fundamentally different compared to prokaryotic cells, which means that the VP16 may not have successfully recruited the polymerases to the DNA as desired.

## Kinetic modeling with Virtual Cell

---

### Construct 2: Conjugated human liver fatty acid binding protein and GFP (hlFAB-GFP)

We modeled the dynamics of part BBa\_K5114228.

![](https://static.igem.wiki/teams/5114/result-graphs/vcell1-1.png)

Simulations were carried out for 100 minutes (6000) seconds at various concentrations of initial PFAS. The environment was made to be 1000 um^3 in volume because that is the expected amount of water “available” to each individual cell (max density of E. coli is around 10^9 cells/ml). Cell volume was set to 1 um^3 based on established cell sizes of E. coli at stationary phase. Simulations were carried out at non-steady-state and steady-state (concentration of FAB\_GFP and FAB\_GFP\_mRNA are stable) conditions, found by deterministic ODE modeling.

Regulations for PFAS are on the parts per trillion (ppt) level, so we used 1 ppt as our minimum concentration. Assuming 1 ppt=1 ng/L and the molar weight of PFOA, a common model PFAS, is 414 g/mol, that means 1 ppt is approximately 2E-6 uM of PFOA. Therefore, we simulated from 1E-6 uM initial PFAS in the environment up to 1E-2 uM, which would correlate to roughly 5 ppb.

![](https://static.igem.wiki/teams/5114/vcelleng/fabgfp-plots.jpg)

Interestingly, steady state modeling does not yield significant differences in maximal fluorescence. This is likely because the fluorescence is actually limited by the amount of PFAS available to bind to hlFAB-GFP. Since the environment is set to be 1000 times larger in volume than the cell, the final concentration of bound hlFAB-GFP is 1000 times the initial concentration of PFAS, indicating nearly every molecule of PFAS is bound to a hlFAB-GFP. From 1E-6 to 1E-4, the stochastic nature of the simulation is evident: each step up on the graph represents one PFAS molecule diffusing into the cell from the environment and binding to one molecule of hlFAB-GFP.

Thus, our model indicates that biosensors that directly fluoresce upon binding to PFAS are limited by the amount of PFAS available to them, and not necessarily by the binding affinity of the protein. Future work should focus on increasing the effective fluorescence of the molecule when bound to PFAS.

### Construct 3: Synthetic estradiol transcription factor

![](https://static.igem.wiki/teams/5114/result-graphs/vcell3-3.png)

Cell and environment volumes were kept at 1 and 1000 um^3, respectively. Initial PFAS concentrations were also kept the same. Simulations were carried out in ideal settings that were not at steady state and had no promoter leakage (expression of GFP without the STF binding to the DNA), as well as in more realistic conditions where there was promoter leakage and the concentrations of the STF and its mRNA were constant.

![](https://static.igem.wiki/teams/5114/vcelleng/stf-plots.jpg)

Compared to hlFAB-GFP, this construct appears to be much more sensitive to small quantities of PFAS. Each initial PFAS concentration resulted in much more GFP produced compared to the hlFAB-GFP. The increase in GFP production as initial PFAS concentration increases does not appear to increase logarithmically as did the hlFAB-GFP construct. However, the graphs imply the amount of GFP produced has yet to reach a steady state, indicating that longer exposure times to PFAS may produce more differentiated levels of GFP.

When considering GFP production in the presence of leaking promoters, it appears that it is more difficult to distinguish concentrations below 1E-4, as the shapes of the graph are very similar. It is possible that longer incubation time will make for more differentiated graphs, although more testing is required. The differentiability of the graphs will also depend on the sensitivity of the fluorometer used.

Steady state conditions appear to slightly increase the rate of GFP at any given time, likely because there is more free STF available to bind to PFAS as soon as PFAS diffuses into the cell.

In summary, a synthetic transcription factor inducible by PFAS will likely produce much more fluorescence than something similar to hlFAB-GFP, with the fluorescence more limited by time than by the amount of PFAS available. However, promoter leakage must be kept to a minimum to better separate very low concentrations of PFAS, or be incubated for longer periods of time.

Our modeling demonstrates that steady state conditions do not significantly affect the fluorescence time or maximal fluorescence for hlFAB-GFP, and affects the STF by simply increasing the rate at which PFAS can be bound and GFP can be produced.