iGEM 2019 — Team Austin_UTexas

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# Team:Austin UTexas/Notebook

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        + [Judging Form↗](https://igem.org/2019_Judging_Form?id=3174)

# Notebook

Week 1 (May 27 - June 2)

Wet lab: Collectively completed ~40 transformations, ran 3 burden assay plates (exp007, 008, 009)
People: Noor, Jordan, Diya, Shireen

Dry Lab: Python script written to create a record of transformation DNA sequences for later comparison (reference file generation)
 People: Ginny, Angela

Week 2 (June 3 - 9)

Wet Lab: Completed ~62 transformations total, ran 7 burden assay plates (exp007-013)
People: Noor, Jordan, Diya, Ginny, Angela, Shireen

Dry Lab: Wrapping up reference file for all biobrick DNA sequences, wiki graphics are being made, starting interactive simulation of burden<
 People: Ginny, Angela

Week 3 (June 10 - 16)

Wet Lab: Transformations, burden assays
 People: Noor, Jordan, Diya, Shireen

Dry Lab: Looked at data for burden analysis
 People: Noor, Jordan, Diya, Ginny, Angela, Shireen

![](https://static.igem.org/mediawiki/2019/8/8f/T--Austin_Utexas--BurdenGraphC.png)
 Exp017

Week 4 (June 17 - 23)

Wet Lab: Completed ~77 transformations total, ran 4 burden assay plates
 People: Noor, Jordan, Diya, Shireen

Dry Lab: Previous burden assay experiments were analyzed in RStudio
People: Noor, Jordan, Diya, Ginny, Angela, Shireen

Week 5 (June 24 - 30)

Wet Lab: Transformations, burden assays
 People: Noor, Jordan, Diya, Shireen

Dry Lab: Burden analysis
 People: Noor, Jordan, Diya, Ginny, Angela, Shireen

![](https://static.igem.org/mediawiki/2019/9/95/T--Austin_Utexas--BurdenGraphD.png)

Week 6 (July 1 - 7)

Wet Lab: Decided to update transformation plan and have a goal of 1000 transformations total, ~100 from commonly used parts and ~900 from the 2018 kit plate.
 People: Noor, Jordan, Diya, Shireen

Dry Lab: Planned University of Texas at Arlington, Texas Tech, and Rice University meet up and voice chat, tested out a few parts in breseq (sequence analysis program) with reference file, refined burden scripts for analyzing data
 People: Ginny, Angela

![](https://static.igem.org/mediawiki/2019/2/2c/T--Austin_Utexas--AndersonSeries.png)

Week 7 (July 8 - 14)

Wet Lab: Completed ~80 transformations, ran 2 burden assay plates
 People: Noor, Jordan, Diya, Shireen

Dry Lab: Created a script to programmatically run breseq
 People: Ginny, Angela
![](https://static.igem.org/mediawiki/2019/4/49/T--Austin_Utexas--BurdenGraphA.png)
 Exp023

bla. bla.

Week 8 (July 15 - 21)

Wet Lab: Completed ~15 transformations, ran 4 burden assay plates
 People: Noor, Jordan, Diya, Shireen, Alex, Michelle, Raul

Dry Lab: Gathered distribution of DNA parts by type and sub-part number
 People: Ginny, Angela

![](https://static.igem.org/mediawiki/2019/d/d6/T--Austin_Utexas--PartDistribution.png)

![](https://static.igem.org/mediawiki/2019/9/9a/T--Austin_Utexas--SubPartDistribution.png)

Week 9 (July 22 - 28)

Wet Lab: Made new batch of competent cells for transformations because of contamination of old cells, transformations, ran 2 burden assays
People: Noor, Jordan, Diya, Ginny, Angela, Shireen, Alex, Michelle, Raul

Dry Lab: Worked on sequence analysis, simulation script, and burden analysis
 People: Ginny, Angela

![](https://static.igem.org/mediawiki/2019/1/1b/T--Austin_Utexas--BurdenGraph2.png)

Week 10 (July 29 - August 4)

Wet Lab: Completed ~30 transformations
 People: Noor, Jordan, Diya, Ginny, Angela, Shireen, Mina, Raul

Dry Lab: Fixing burden analysis --> refined quantitative interpretation of burden, figured out how to calculate percentage of each type of burden
 People: Ginny, Angela

![](https://static.igem.org/mediawiki/2019/0/0a/T--Austin_Utexas--BurdenGraphB.png)
 Exp028

Week 11 (August 5 - 11)

Wet Lab: 8 burden assays done
 People: Noor, Jordan, Diya, Ginny, Angela, Shireen, Mina, Alex, Raul

Dry Lab: Wiki, burden analysis
 People: Noor, Jordan, Diya, Ginny, Angela, Shireen

![](https://static.igem.org/mediawiki/2019/2/2f/T--Austin_Utexas--BurdenGraph3.png)

Week 12 (August 12 - 18)

Wet Lab: Burden assays
People: Noor, Jordan, Diya, Ginny, Angela, Shireen, Mina, Alex, Raul

Dry Lab: Made graphs, worked on statistical correction of data, wiki
 People: Noor, Jordan, Diya, Ginny, Angela, Shireen

Week 13 (August 19 - 25)

Wet Lab: Redid some burden assays
People: Noor, Jordan, Diya, Ginny, Angela, Shireen, Mina, Alex, Raul

Dry Lab: Collaboration write up, got data from Texas Tech University about calibration strains, wiki
 People: Noor, Jordan, Diya, Ginny, Angela, Shireen

Week 14 (August 26 - September 1)

Wet Lab: Burden assay of interesting parts
 People: Noor, Jordan, Diya, Ginny, Angela, Shireen, Mina, Raul

Dry Lab: Wiki
 People: Noor, Jordan, Diya, Ginny, Shireen, Alex, Mina

Week 15 (September 1 - 7)

Wet Lab: Burden assays
 People: Noor, Jordan, Diya, Ginny, Angela, Shireen, Mina, Raul

Dry Lab: Wiki, burden analysis
 People: Noor, Jordan, Diya, Ginny, Angela, Shireen, Michelle, Alex, Raul, Mina

Week 16 (Septmeber 7 - 13)

Wet Lab: Redid some burden assays
 People: Jordan, Diya, Raul

Dry Lab: Wiki, statistics
 People: Noor, Jordan, Diya, Ginny, Angela, Shireen, Alex, Raul, Mina

Week 17 (Septmeber 14 - 20)

Wet Lab: Redid some burden assays
 People: Jordan, Diya, Raul

Dry Lab: Statistics, wiki, burden analysis
 People: Noor, Jordan, Diya, Ginny, Angela, Shireen, Alex, Raul, Mina

![](https://static.igem.org/mediawiki/2019/9/9b/T--Austin_Utexas--BurdenGraphG.png)

Week 18 (Septmeber 21 - 27)

Wet Lab: N/A

Dry Lab: Statistics, wiki
 People: Noor, Jordan, Diya, Ginny, Angela, Shireen, Michelle, Alex, Raul, Mina

Week 19 (Septmeber 28 - October 3)

Wet Lab: N/A

Dry Lab: Wiki, collaborations
 People: Noor, Jordan, Diya, Ginny, Angela, Shireen, Alex, Raul, Mina

Week 20 (October 4 - October 10)

Wet Lab: N/A

Dry Lab: Wiki, getting data from collaborations
 People: Noor, Jordan, Diya, Ginny, Angela, Shireen, Alex, Raul, Mina

Week 21 (October 11 - October 17)

Wet Lab: N/A

Dry Lab: Wiki, getting data from collaborations
 People: Noor, Jordan, Diya, Ginny, Angela, Shireen, Michelle, Alex, Raul, Mina


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# Team:Austin UTexas/Experiments

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      + [Team](https://2019.igem.org/Team%3AAustin_UTexas/Team)
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        + [Medal Criteria](https://2019.igem.org/Team%3AAustin_UTexas/Medals)
        + [Demonstrate](https://2019.igem.org/Team%3AAustin_UTexas/Demonstrate)
        + [Collaborations](https://2019.igem.org/Team%3AAustin_UTexas/Collaborations)
        + [Judging Form↗](https://igem.org/2019_Judging_Form?id=3174)

## Contents

* [1 Experiments](#Experiments)
  + [1.1 Transformation](#Transformation)
    - [1.1.1 Materials](#Materials)
    - [1.1.2 Procedure](#Procedure)
  + [1.2 Burden Assay](#Burden_Assay)
    - [1.2.1 Materials](#Materials_2)
    - [1.2.2 Procedure](#Procedure_2)
    - [1.2.3 Culture Preparation](#Culture_Preparation)
    - [1.2.4 Plate Preparation](#Plate_Preparation)
    - [1.2.5 Plate Reading](#Plate_Reading)
    - [1.2.6 Data Analysis](#Data_Analysis)

# Experiments

From start to finish, a central goal of our project was burden quantification. Two experiments required for our progress were transformations and burden assays; select BioBrick parts in the iGEM registry were transformed into our burden monitor E. coli, and these strains were then moved to a 96 well plate to measure optical density and GFP fluorescence. This, along with our burden R script, then gave us the burden graphs seen in our Results. These two protocols are shown below.

### Transformation

Though our parts and *E. coli* strain are specific to our project, these can be substituted with other plasmids and DNA relevant to any experiment.

##### Materials

* Competent cells
* Transforming DNA
* SOC medium
* Heat block or water bath
* Antibiotic plates
* Ice bucket with ice

##### Procedure

1. Take out a frozen tube of cells for each reaction, and appropriate controls.

2. Thaw tubes by holding them in your hand.

3. Add 0.5-1μL of the miniprepped plasmid (the transforming DNA) and incubate on ice for 30 minutes.

4. Transfer the tubes to a 42°C heat block or water bath for exactly 90 seconds.

5. Immediately transfer the tubes to ice for 1-2 minutes.

6. Add 450μL of SOC medium to each tube, then shake at 200-250RPM for 45 minutes.

7. Plate two different volumes (10μL and 50μL as an example) for each reaction on antibiotic plates.

### Burden Assay

The iGEM registry's BioBricks were used as our plasmids-of-interest, but this protocol is generalized to allow for alternative plasmids.

##### Materials

* Transformed plasmids
* LB antibiotic plates
* LB medium
* 96 well plate
* Calibration strains
* Relevant antibiotics
* Plate reader

##### Procedure

##### - Culture Preparation

1. With frozen stocks of plasmids-of-interest transformed into the burden monitor strain, streak LB plates with appropriate antibiotic. The maximum number of unique strains that can be run per assay is 23.

2. Streak the five calibration strains (JEB1204-1208) from frozen stocks onto LB-Chloramphenicol + Kanamycin plates.

3. Leave plates at 37°C overnight

4. Pick colonies and prepare overnight cultures of calibration strains into 5 mL LB-Chloramphenicol + Kanamycin as well as overnight cultures of each plasmid-of-interest into 5mL LB with appropriate antibiotic.

5. Place the bottle of LB to be used the next day into a 37°C incubator as well.

##### - Plate Preparation

6. Begin prewarming the plate reader to 37°C before preparing the plate.

7. Vortex overnight cultures and place 5 µL into a 96-well plate in triplicate. Split the plate into 3 columns of equal width, so the triplicates will be in the same well position for each column. With 23 unique strains, leave wells A1, A4, A5, A8, A9, and A12 in addition to wells H1, H4, H5, H8, H9, and H12 for blanks. If less than 23 strains are being used, incorporate blanks in triplicate as needed. Fill blanks with 5 µL of room temperature LB. Make a 96-well-plate template representative of the experiment’s layout.

![](https://static.igem.org/mediawiki/2019/9/94/T--Austin_Utexas--baLayout.png)

Figure 1. Well template of triplicates.

8. Take pre-warmed LB out of the incubator and use a multichannel pipette to load 195 µL of the warmed LB into each column. Gently pipette up and down 5 times to mix, being careful not to introduce bubbles, and place the lid.

##### - Plate Reading

9. Load the burden assay method and start the plate reader. The method must run for at least 360 minutes.

10. Transform the 96-well-plate template into an electronic metadata layout as pictured here:

![](https://static.igem.org/mediawiki/2019/7/72/T--Austin_Utexas--examplemetadata.png)

Figure 2. Example metadata in Excel.

11. Save the measurements generated as a csv.

##### - Data Analysis

12. Use the metadata layout as well as the measurement files as inputs to the burden scripts in the [Barrick Lab Github](https://github.com/barricklab/burden/tree/master/scripts).

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# Team:Austin UTexas/Results

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      + [Team](https://2019.igem.org/Team%3AAustin_UTexas/Team)
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        + [Medal Criteria](https://2019.igem.org/Team%3AAustin_UTexas/Medals)
        + [Demonstrate](https://2019.igem.org/Team%3AAustin_UTexas/Demonstrate)
        + [Collaborations](https://2019.igem.org/Team%3AAustin_UTexas/Collaborations)
        + [Judging Form↗](https://igem.org/2019_Judging_Form?id=3174)

## Contents

* [1 Results](#Results)
  + [1.1 Overview](#Overview)
  + [1.2 BioBricks have burden associated with them.](#BioBricks_have_burden_associated_with_them.)
  + [1.3 There can be more than one type of burden imposed by a BioBrick.](#There_can_be_more_than_one_type_of_burden_imposed_by_a_BioBrick.)
  + [1.4 Discussion](#Discussion)

# Results

### Overview

We transformed 497 BioBricks from iGEM distribution kits into our burden monitor strain. We performed over 49 burden assays and analyzed the measurements through a pipeline of scripts to yield the following results. In the end, we obtained 392 distinct observations of the relevant measurements for quantifying burden for 330 different parts.

**Our data is meant to provide evidence for the following:**

* BioBricks have burden associated with them.
* There can be more than one type of burden imposed by a BioBrick.

**We define burden as percent reduction in growth rate.**

The peak mean specific growth rate and GFP rate for each experiment within the following data is normalized to a value of 1 for both variables. This multivariate normalization was conducted under the assumption that most BioBricks are not significantly burdensome. Therefore, statistically significant burdensome parts can be reported as such with surety.

### BioBricks have burden associated with them.

Figure 1 shows shows that growth rate reduction can be associated with different constructs. It displays the descending order of normalized mean specific growth rate for each strain isolate that was tested. The progressively darker blue color correlates to the specific growth rate for each strain isolate shown. The more burdensome a construct is, the more reduced the growth rate will be for a host cell harboring the BioBrick. This directly provides evidence that BioBricks have a burden associated with them.

![](https://static.igem.org/mediawiki/2019/2/2a/T--Austin_Utexas--results_growthratespdf.png)

Figure 1. Graph of normalized specific growth rates for each strain isolate.

Again referencing our frequently aforementioned definition of burden as a percent reduction in growth rate, the **burden value** is simply the decimal value representing this percentage. The burden value is equivalent to a construct’s normalized growth rate subtracted from 1, with 1 being defined as the mean value of “no burden” constructs for the total distribution. **For example,** a part with a normalized growth rate of 0.75 would have a burden value of 0.25 and therefore contributes to a 25% reduction in growth rate.

Figure 2 is a density plot that illustrates the burden values (percent reduction in growth rate) that our tested constructs exhibited, under the assumption that most BioBricks aren’t burdensome. **A construct with a negative or zero burden value** means that the construct displayed a growth rate reduction of 0% and is therefore not burdensome. However, if a construct has a measured burden value of 0.4, then that indicates a 40% reduction in growth rate. Although excluded from the data, the grand means of our control strains are plotted to serve as a reference point from which one may assess the burden of other constructs. While most parts do show some minute burden value, most parts are not burdensome because they do not significantly contribute to a reduction in growth rate relative to the expected growth rate for that construct.

![](https://static.igem.org/mediawiki/2019/8/8c/T--Austin_Utexas--results_densityplot.png)

Figure 2. Density plot of the burden distribution of BioBricks.

### There can be more than one type of burden imposed by a BioBrick.

To differentiate sources of burden, we also measured the GFP expression rate concurrently with growth rate in our “burden assays”. The linear regression is built from the measurements of our control strains. Our control strains provide a reasonable model to construct a threshold to describe translational burden (caused by ribosomal reallocation) because each of the five control strains have a different promoter and ribosome binding site combination. Therefore, we would expect to see a linear curve where stronger promoters with weaker ribosome binding sites exhibit a lower growth rate and a lower GFP expression rate as resources are allocated towards its plasmid, starving the genome and simulating an outcome of translational stress. As all combinations are used, we observed a linear regression model with a slope that is not significantly different from 1 as the threshold at which translational burden can be distinguished from other types of burden not associated with the mere stress of a construct’s translational elements.

The positive linear slope shows that constructs with a higher growth rate and concurrently higher GFP rate are less burdensome than those with a lower growth rate and lower GFP rate, and that **100% of the growth rate reduction associated with those parts is sourced from translational burden.** Simply put, points lower on the regression line are more burdensome than points higher on the regression line, and the burden imposed on those parts is a result of translational stress.

However, if a construct shows a higher GFP rate than what would be linearly coordinated with its measured growth rate, the growth rate reduction reported must be attributed to **something in addition to translational stress.** In other words, a BioBrick of this type would have a growth rate less than what it should be as dictated by the linear model. Thus, in addition to translational burden, such a BioBrick also has another type of burden which further reduces its growth rate.

Figure 3 provides evidence that there can be more than one type of burden imposed by a construct. Significantly burdensome parts are distinguished from parts showing no significant burden and from parts exhibiting additional, “other” burden.

![](https://static.igem.org/mediawiki/2019/5/5d/T--Austin_Utexas--results_scatterplotGFP.png)

Figure 3. Scatter plot of BioBricks' GFP expression rates as a function of growth rates.

### Discussion

These results are important because they can help to better characterize constructs in the iGEM registry such that reliable parts may be distinguished from unreliable, burdensome ones. These results, when used in context with our model, may help a person decide which parts are better suited for their purposes, whether it be simply cloning a part in the lab or propagating the part for long-term, large-volume industrial use.