Team:Manchester/Experimental Design

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Experimental design


  • We define redesigned our project to fit an unusual year.
  • We successfully mapped our project to the DBTL cycle.
  • We demonstrated engineering success throughout the project.
  • We applied the DBTL philosophy to each of the areas of the project individually.

Experimental design is and has always been a key component of both iGEM and Synthetic Biology. In the approximation to engineering Synthetic Biology adopted the Design-Build-Test-Learn (DBTL) cycle, and iGEM projects are generally carried out in accordance to it. This year the COVID-19 pandemic has disrupted the traditional view of the cycle, and has forced many teams to reconsider their approach.

Our measure for engineering success has been the application of the DBTL cycle both at a whole project scale, and individually to all the different aspects of our project.

The DBTL cycle can be found throughout our project in our approach to almost every page and activity, some notable ones are:



Our host institution SYNBIOCHEM has its own adaptation of DBLT and we mapped our project to it, and adapted it to fit our current and future work, as we are establishing this year as thePhase I of a two phase project.

Flowchart 1

We will not be able to complete a full iteration of the cycle for the overall project, but we approach each of the individual nodes with the DBTL philosophy in mind.

DESIGN

The DESIGN platform of the DBTL cycle is often overlooked by iGEM teams, when it is the platform that has the most potential for standardization in Synthetic Biology.

Flowchart 2

The DESIGN platform is for the modelling and design of pathways, and DNA constructs, it includes several iterations for the optimization of the process.

Pathway Design: We accomplished a full pathway design by combining computational retrosynthetic and assembly methods with chemical analysis.

Enzyme selection: We used a self-hosted version of selenzyme, an online enzyme selection to find enzymes to catalyze the reactions in our designed pathway.

Modelling: Informs the design itself, and it is used to further characterize the designed pathway as well as validate it. In addition it helps with design for the

DNA Parts design: When designing our DNA parts we had to consider and optimise each of the following variables to produce a viable plasmid that would successfully transform our chassis selection,


  • Codon optimisation
  • RBS design
  • Promoter design
  • Assembly Method
  • Chassis

We designed two parts for Escherichia coli strain DH5alpha, the two parts we designed were among the Top 10 enzymes outputted by Selenzyeme (link pathway design), two variants 4-hydroxyphenylpyruvate dioxygenase one from Pseudomonas aeruginosa (paHPPD), which can be seen Figure 3, and another one from Streptomyces avermitilis (saHPPD), which can be seen in Figure 4. A pET28a backbone was selected and we used the restriction enzymes NcoI and HindIII to remove the enzyme selection marker SacB and its promoter. In this newly vacant space we inserted our CDS.

Figure 3

Figure 3. paHPPD construct, IPTG regulated.

Figure 4

Figure 4. saHPPD construct, IPTG regulated.

BUILD

The BUILD platform from the DBTL cycle is responsible for the production of parts for the subsequent assembly and transformation of the chassis. There is a growing trend for automation in this platform of the cycle.

Flowchart 3

The Build section for us this year is quite simple, as we are constructing single assembly mongene parts. The process involves the following steps:


  • Commercial DNA synthesis
  • PCR for part preparation
  • Setup for pathway assembly
  • Purification for quality checking
  • Transformation of chassis

Plans for the future include assembling complex parts for overexpression of Tyrosine as well as overexpression of HPPD to increase the yield of the chassis.

TEST

The TEST platform of the “Design-Build-Test-Learn Cycle” is for tracking and assaying the products emanating from the BUILD platform. This process involves using different functional targeted analytical chemistry techniques and untargeted metabolomics to determine how the technology can be improved (SYNBIOCHEM, n/a). In this sense the TEST platform acts as a debugging tool for the rest of the cycle. The results from the TEST platform feed back into the cycle to inform researchers how to extend both the laboratory scale and scale-up and scale-down processes.

Flowchart 4

After chassis transformation with our designed plasmids if the characteristic red colouration of Hipposudoric Acid appears, we will grow E.coli on a standard growth substratum and begin our product extraction and purification.

We have a choice to extract either the precursor HGA or our desired product Hipposudoric Acid. HGA is a known phenolic acid and can be easily identified by mass spectrometry (MS). The results of a MS analysis of HGA is a molecular weight of 168mu and relevant fragments at 66, 94, 122 and 150 mu (Cabras et al, 1999). This is what we would expect to see, if this was not the case it implies there is a mistake within the DESIGN and BUILD platforms and we would have to go back to modify these earlier stages.

In contrast Hipposudoric Acid is a relatively uncharacterised compound and there is currently no MS standard. Hashimoto et al attempted to characterise Hipposudoric Acid using 1HNMR, UV and mass spectral data however, due to the unstable nature of the acid it had to first be converted into a more stable derivative. This was achieved through a reduction of the pigment and subsequent methylation and silylation to produce a stable derivative that could be crystallised from methanol (Hashimoto et al., 2007). It is likely that next year's team would have to follow this protocol in order to analyse Hipposudoric Acid. It has been deduced that the molecular weight of Hipposudoric Acid is 328.3, this is the first test that shall be performed to confirm that we have achieved our desired compound (Hashimoto et al., 2007).

According to Royal Society Publishing when characterising a new organic compound one must “provide unequivocal support for the purity and assigned structure of all compounds” and this is achieved through a range of different analytical techniques. These are the protocols that will be performed in the laboratory next year.

Analytical Analysis:


  • Elemental analysis within +/-0.4% of the calculated value is required to confirm 95% sample purity and corroborate isomeric purity.
  • NMR spectra (1H, 13C).
  • HPLC traces - determination of enantiomeric excess of non-racemic, chiral substances.
  • Retention times.
  • Mosher Ester/Chiral Shift Reagent analysis.
  • Gel electrophoresis.

Physical:


  • Boiling/melting point.
  • Specific rotation.
  • Refractive index - compared to known literature.
  • Crystalline compounds - document method of recrystallisation.

Spectroscopic:


  • Mass spectra.
  • Infrared spectra - support functional group modifications.
  • Diagnostic assignments.

Hipposudoric Acid is a small molecule that can theoretically polymerise indefinitely producing a long chain, this means that the NMR data should be tabulated. Furthermore, it is possible that not all of these protocols will be relevant, however because of the novelty of Hipposudoric Acid we cannot determine at this stage which protocols are useful for characterisation of our compound.

After performing these experiments the data received for HGA and Hipposudoric Acid will be processed and analysed in preparation for the LEARN platform of the “Design-Build-Test-Learn Cycle”.

LEARN

Flowchart 5

In the final stage of the cycle the results collected in the TEST platform are used to determine if any changes are needed in the DESIGN and BUILD platforms; the LEARN platform identifies how the project can be improved. Mechanistic modeling and artificial intelligence approaches such as machine learning can be applied to extract value from data sets. Learning is used to generate testable hypotheses that are incorporated into the next cycle resulting in a gradual development and improvement of our biotechnology over time. Once the process is optimised the final section of our project involves the moving from lab scale experiments towards commercial manufacturing, this is called Scale-Up. This process is usually accompanied by its own “Design-Build-Test-Learn Cycle” and involves up-scaling the fermentation capacity including further bioengineering to suit the bioprocess. This stage my induce unexpected costs and to align with our project values we may have to adopt our method accordingly to reduce retail prices.

References

Literature

Cabras, P., Angioni, A., Tuberoso, C., Floris, I., Reniero, F., Guillou, C., Ghelli, S., (1999) Homogentisic Acid: A Phenolic Acid as a Marker of Strawberry-Tree (Arbutus unedo) Honey, Journal of Agricultural and Food Chemistry, 47, 4064-4067
Hasimoto, K., Saikawa, Y., Nakata, M., (2009) Studies on the red sweat of the Hippopotamus amphibious, Pure and Applied Chemistry, 79, 507-517
The Royal Society Publishing, Characterising new chemical compounds & measuring results, 2020, Available at: https://royalsocietypublishing.org/rsos/chemical-compounds Accessed: 20/09/2020
ACS Publications, Guidelines for Characterisation of Organic Compounds, 2020, Available at: https://pubs.acs.org/page/jacsat/submission/org_character.html Accessed: 20/09/2020
Christopher Jonson,The Bio-Engineering Cycle, (N/A) Available at: https://biofoundries.org/design-build-test-learn Accessed: 09/10/2020
SYNBIOCHEM, SYNBIOCHEM pipeline, (N/A) Available at: http://synbiochem.co.uk/synbiochem-pipeline/ Accessed: 09/10/202

Figures

Pictures: flow chart made by Madeleine Webster-Harris 2020 in Lucidchart.com
Cycle: from Eriko Takano Lecture in Prokaryotic Microbiology, it is a modified version of the cycle found on SYNBIOCHEM.com
igem2020manchester@gmail.com