iGEM 2020 — Team Edinburgh

[![](https://static.igem.org/mediawiki/2020/8/8b/HQ_page_logo.jpg)](https://2020.igem.org)

# Team:Edinburgh/Notebook

# Notebook

Document the dates you worked on your project. This should be a detailed account of the work done each day for your project.

### What should this page have?

* Chronological notes of what your team is doing.
* Brief descriptions of daily important events.
* Pictures of your progress.
* Mention who participated in what task.

### Inspiration

You can see what others teams have done to organize their notes:

* [2018 Munich](https://2018.igem.org/Team%3AMunich/Notebook)
* [2014 ATOMS-Turkiye](https://2014.igem.org/Team%3AATOMS-Turkiye/Notebook)
* [2014 Tec Monterrey](https://2014.igem.org/Team%3ATec-Monterrey/ITESM14_project.html#tab_notebook)
* [2014 Kyoto](https://2014.igem.org/Team%3AKyoto/Notebook/Magnetosome_Formation#title)
* [2014 Cornell](https://2014.igem.org/Team%3ACornell/notebook)

[![](https://static.igem.org/mediawiki/2020/8/8b/HQ_page_logo.jpg)](https://2020.igem.org)

# Team:Edinburgh/Engineering

Team Edinburgh Finding NEMO

[![](https://static.igem.org/mediawiki/2020/0/07/T--Edinburgh--Edinburgh_logo_transparent.png)](https://2020.igem.org/Team%3AEdinburgh)

Menu

* TEAM

  [Team](https://2020.igem.org/Team%3AEdinburgh/Team/Team)
  [Collaborations](https://2020.igem.org/Team%3AEdinburgh/Collaborations)
  [Partnerships](https://2020.igem.org/Team%3AEdinburgh/Partnerships)
  [Sponsors & Supporters](https://2020.igem.org/Team%3AEdinburgh/Team/SponsorSupporters)
  [Attributions](https://2020.igem.org/Team%3AEdinburgh/Attributions)

  * Project

    [Description](https://2020.igem.org/Team%3AEdinburgh/Description)
    [Contribution](https://2020.igem.org/Team%3AEdinburgh/Contribution)
    [Design](https://2020.igem.org/Team%3AEdinburgh/Project/Design)
    [Experiment](https://2020.igem.org/Team%3AEdinburgh/Project/Experiment)
    [Modeling](https://2020.igem.org/Team%3AEdinburgh/Model)
    [Engineering](https://2020.igem.org/Team%3AEdinburgh/Engineering)
    [Notebook](https://2020.igem.org/Team%3AEdinburgh/Project/Notebook)
    [Proof of Concept](https://2020.igem.org/Team%3AEdinburgh/Proof_of_Concept)
    [Results](https://2020.igem.org/Team%3AEdinburgh/Project/Results)

    * Parts

      [Parts Collection](https://2020.igem.org/Team%3AEdinburgh/Parts/PartsCollection)
      [Basic Parts](https://2020.igem.org/Team%3AEdinburgh/Parts/BasicParts)
      [Composite Parts](https://2020.igem.org/Team%3AEdinburgh/Parts/CompositeParts)
      [Improvements](https://2020.igem.org/Team%3AEdinburgh/Parts/Improvements)

      * Safety

        [Design](https://2020.igem.org/Team%3AEdinburgh/Safety/Design)
        [Lab](https://2020.igem.org/Team%3AEdinburgh/Safety/Lab)
        [People](https://2020.igem.org/Team%3AEdinburgh/Safety/People)

        * Human Practices

          [Human\_Practices](https://2020.igem.org/Team%3AEdinburgh/Human_Practices)
          [Values](https://2020.igem.org/Team%3AEdinburgh/HumanPractices/Values)
          [Culture](https://2020.igem.org/Team%3AEdinburgh/HumanPractices/Culture)
          [Frameworks](https://2020.igem.org/Team%3AEdinburgh/HumanPractices/Frameworks)
          [Stakeholder Engagement](https://2020.igem.org/Team%3AEdinburgh/HumanPractices/StakeholderEngagement)
          [Applications](https://2020.igem.org/Team%3AEdinburgh/Implementation)
          [Science Communication](https://2020.igem.org/Team%3AEdinburgh/HumanPractices/ScienceCommunication)
          [Education](https://2020.igem.org/Team%3AEdinburgh/Education)
          [Initiatives](https://2020.igem.org/Team%3AEdinburgh/HumanPractices/Initiatives)

          * Other

            [SDGs](https://2020.igem.org/Team%3AEdinburgh/Other/SDGs)

## Engineering Success

E. coli Cell-Free Transcription-Only Model .

Transcription and translation (TX/TL) are the core elements of classic cell-free protein synthesis system. In our project, the TX/TL system was simplified as transcription-only (TXO) when output the final outcome as RNA, thus avoiding all the restriction involved in the translation process. Therefore, the performance of transcription is of great importance for our overall cell-free sensing system.

Based on the sophisticated cell-free protein synthesis model built up by Horvath et al.[1] where all metabolic reactions are described using Michaelis Menten kinetics, we successfully generated our dynamic mathematical model of E. coli transcription-only cell-free model, with functions as below:

* Simulate reactions of a transcription-only cell-free system, especially the central carbon metabolism
* Simulate the dynamic flux of metabolites, enzymes, NTPs, etc.
* Evaluate the performance, e.g., the yield and rates of RNA production
* Simulate measurements of productivity and energy efficiency
* Assists the conceptual designs
* Can be adapted to wide range of product mRNA

Taking advantage of our well-defined TXO cell-free model, we explored how would the changes in the concentration of metabolites, NTPs, and RNA polymerase (RNAP) influence mRNA production, thus to developed corresponding methods to optimize the productivity of transcription. With glucose as the energy source, and mRNA of chloramphenicol acetyltransferaseas (CAT) as the expected final product, we screened the effects of NTPs, 36 enzymes (including T7 RNAP) and 148 metabolites which participating 204 reactions in the system, while most parameters were taken from literature and assembled as the best-fit set. The simulations were consistent with those experimentally constrained cell-free reactions, verifying that this model is reliable and accurate.

We observed that the productivity of transcription is most sensitive to the level of T7 RNAP rather than other metabolites, and much more mRNA would be generated when only increasing the initial concentration of T7 RNAP. Besides, ATP and GTP also showed significant effects on the productivity of transcription. The increase of GTP would improve the overall volume of mRNA generated, while loading more ATP would only improve the peak value of mRNA presenting in the system (Fig.1). Furthermore, the degradation of mRNA considerably hindered the level of mRNA in the working system.

![](https://static.igem.org/mediawiki/2020/5/55/T--Edinburgh--Fig.1_Ying.jpg)

**Fig.1 Effect of increasing initial concentrations of GTP and ATP on mRNA CAT production.** The higher initial concentrations of ATP (A) or GTP (B) were labelled with darker colours: 10 times, pink; 20 times, orange; 30 times, red. The unmodulated model was used as control (blue line).

Towards these results from simulations, a proof of concept for the optimization of productivity was verified when modulating 10 times initial concentrations of ATP, GTP, and T7 RNAP of their original value, half mRNA degradation rate of its original value (Fig.2).

**Fig.2 Proof of concept.** Unmodulated model was used as control (blue); half-degradation simulation was performed with half degradation rate of mRNA (green), 10x ATP simulation was performed with modulating the initial concentration of ATP to 10 times of its original value (yellow); 10x GTP simulation was performed with modulating the initial concentration of GTP to 10 times of its original value (purple); the pattern of 10x RNAP was performed with modulating the initial concentration of RNAP to 10 times of its original value (orange), which was separately displayed as it would interfere the patterns of other four simulations; the ‘optimal model’ in this case as proof of concept (red) was an assembly of the four modulations: 10x ATP, 10x GTP, 10x RNAP, half-degradation.

![](https://static.igem.org/mediawiki/2020/9/96/T--Edinburgh--Fig.2_Ying.jpg.png)

Besides the peak and final concentrations of mRNA CAT, the productivity of transcription was assessed with rate of mRNA production, including Rmax: the max rate of mRNA production; Rd>p: the rate of mRNA production at the quite early stage of transcription when degradation of mRNA was slower than the production of mRNA CAT; Rave: the average rate of mRNA production until reached the peak concentration of mRNA. These rates were obtained as the slope of fit lines for mRNA CAT patterns responding to different modulations. The optimal model showed both the highest Rmax and Rd>p, while the simulation simply modulated 10x RNAP had the highest Rave (Fig.3). This result shows that systems with the setting as optimal model are more suitable for those ask for high peak value of mRNA, while the systems simply with high RNAP loading are more fit those tend to have longer incubation time.

![](https://static.igem.org/mediawiki/2020/6/68/T--Edinburgh--Fig.3_Ying.jpg.png)

 **Fig.3 Rates of mRNA CAT production.**  Calculation of production rate of mRNA CAT using slopes of fit lines (a). The max rate of mRNA production (red); the rate of mRNA production when degradation of mRNA was slower than the production of mRNA CAT (blue); the average rate of mRNA production until reached the peak concentration of mRNA (yellow). The values of production rates of mRNA CAT with the unmodulated model as control (b). A: Control, B: Half-degradation, C: 10x ATP, D: 10x GTP, E: 10x RNAP, F: Optimal.

Energy efficiency of ATP (EATP) was calculated by dividing the peak concentration of mRNA CAT with the initial concentration of ATP; Energy efficiency of GTP (EGTP) was calculated by dividing the peak concentration of mRNA CAT with the initial concentration of GTP. The optimal model didn’t show advantages in energy efficiency of ATP or GTP (Fig.4), which reflecting that the methods of accelerating energy efficiency need to be further accessed.

![](https://static.igem.org/mediawiki/2020/8/8e/T--Edinburgh--Equation.png)

![](https://static.igem.org/mediawiki/2020/a/ab/T--Edinburgh--Fig.4_Ying.jpg.png)

**Fig.4 Energy efficiency of ATP and GTP. Unmodulated TXO model was used as control. A: Control, B: Half-degradation, C: 10x ATP, D: 10x GTP, E: 10x RNAP, F: Optimal.**
To sum up, this model can effectively predict the behaviour of transcription, productivity, energy efficiency, and the optimal metabolic flux. In the future work, we plan to further explore the performance limits of our system in the assistance of modelling. Besides improving the productivity, we could also modify this model to investigate strategies about reducing the cost and incubation time. For instance, we could investigate the most dominant metabolic factors impacting transcription, and then work on metabolic engineering for optimization. We believe that with the assistance of this model, we could design and generate a theoretically optimal cell-free system to make our sensing network cheaper, faster, and more sensitive.

Details of this model

Built from the iAF1260 reconstruction of K-12 MG1655 E. coli, the cell-free TX/TL model built up by Horvath et al.[1], modified by Nadanai Laohakunakorn was adapted in this project. The TXO model were implemented in the Julia programming language. Model parameters were largely taken from literature. This TXO model is sequence-specific, that parameters for transcription were specific to the mRNA sequence of target. Specifically, the transcription rate is linked with the length and the composition of nucleotide sequence of target. The parameters for transcription such as elongation rate and promoter activity level are specific to the particular target via its nucleotide sequence. The cDNA sequence of CAT is available in the Appendices. All simulations were operated simulating reaction time as 3 hours, while glucose is the only energy source.

The code and parameter ensemble of TX/TL cell-free model (Nadanai) is available in GitHub [2], all code used in this project is available in the Research DataStore of the University of Edinburgh under Laohakunakorn Group.

References

1. Horvath N, Vilkhovoy M, Wayman JA, Calhoun K, Swartz J, Varner JD (2020) Toward a genome scale sequence specific dynamic model of cell-free protein synthesis in Escherichia coli. Metabolic Engineering Communications 10: e00113.
2. Available from https://github.com/nadanai263/MSc2020\_resources

Copyright © iGEM Edinburgh 2020

*edigem2020@gmail.com*

[Privacy Policy](#!)
[Terms of Use](#!)

[![](https://static.igem.org/mediawiki/2020/8/8b/HQ_page_logo.jpg)](https://2020.igem.org)

# Team:Edinburgh/Experiments

# Experiments

Describe the research, experiments, and protocols you used in your iGEM project. These should be detailed enough for another team to repeat your experiments.

If you made Parts this year, please remember to put all information, characterization, and measurement data on the Part's Main Page on the [Registry](http://parts.igem.org/Main_Page).

### What should this page contain?

* Protocols
* Experiments

### Inspiration

* [2014 Colombia](https://2014.igem.org/Team%3AColombia/Protocols)
* [2014 Imperial](https://2014.igem.org/Team%3AImperial/Protocols)
* [2014 Caltech](https://2014.igem.org/Team%3ACaltech/Project/Experiments)

[![](https://static.igem.org/mediawiki/2020/8/8b/HQ_page_logo.jpg)](https://2020.igem.org)

# Team:Edinburgh/Results

# Results

You can describe the results of your project and your future plans here.

### What should this page contain?

* Clearly and objectively describe the results of your work.
* Future plans for the project.
* Considerations for replicating the experiments.

### Describe what your results mean

* Interpretation of the results obtained during your project. Don't just show a plot/figure/graph/other, tell us what you think the data means. This is an important part of your project that the judges will look for.
* Show data, but remember **all measurement and characterization data must also be on the Part's Main Page on the [Registry](http://parts.igem.org/Main_Page).** Otherwise these data will not be in consideration for any medals or part awards!
* Consider including an analysis summary section to discuss what your results mean. Judges like to read what you think your data means, beyond all the data you have acquired during your project.

### Project Achievements

You can also include a list of bullet points (and links) of the successes and failures you have had over your summer. It is a quick reference page for the judges to see what you achieved during your summer.

* A list of linked bullet points of the successful results during your project
* A list of linked bullet points of the unsuccessful results during your project. This is about being scientifically honest. If you worked on an area for a long time with no success, tell us so we know where you put your effort.

### Inspiration

See how other teams presented their results.

* [2014 TU Darmstadt](https://2014.igem.org/Team%3ATU_Darmstadt/Results/Pathway)
* [2014 Imperial](https://2014.igem.org/Team%3AImperial/Results)
* [2014 Paris Bettencourt](https://2014.igem.org/Team%3AParis_Bettencourt/Results)