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| − | <p>The success in the commercialisation and democratisation of engineering, has often been touted to be attributed to the use of standardization (Endy 2005; Brazma 2001). Modular parts that were clearly defined can be implemented into complementary systems to produce perfectly reproducible results. This allowed for reliable, predictable and complex engineered systems to be developed with mathematical precision. Then, the amalgamation of standardization and automation brought forth a whole new era of engineering success, with the automation of both production and assembly lines revolutionising industry. </p> | + | <p><font size="3">Alternative Roots participated in the 2018 InterLab study. Three calibration steps were carried out prior to any experimental measurements being taken. Absorbance and fluorescence values were measured in 96-well plates using a Thermofisher Varioskan Lux plate reader (Thermofisher Scientific). Absorbance was measured at 600 nm and converted to a comparable OD<sub>600</sub>. Fluorescence was measured at 525 nm with excitation at 485 nm with a 12 nm bandpass width. All readings took place at 25 °C and pathlength correction was disabled. The calibrations were:</p> |
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| − | <p> There have already been some efforts to implement standardisation in biology that have been successful. Annotated DNA sequence data has been categorised by the International Nucleotide Sequence Database Collaboration (INSDC), combining the databases of DDBJ, EMBL-EBI and NCBI. Brazma and colleagues ( 2001) proposed a minimum information standard for microarray data that would allow for easy interpretation and analysable results. The Protein Data Bank ( PDB) stores protein crystallographic data from published articles and independent researchers, while the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology (NC- IUBMB) specifies enzyme nomenclature. Each of these examples have advanced their given fields by allowing increased reproducibility and understanding of data, allowing collaborations to easily build from previous results. Yet, even though reproducibility is a defining feature of science, reproducibility of experimental research is often taken as an assumption instead of fact (Collins 1992; Errington et al. 2014; Schmidt 2009). </p> | + | <p><font size="3">1. A LUDOX CL-X 45 % colloidal silica suspension was used to calculate a conversion factor for the Abs<sub>600</sub> value measured by the plate reader to a comparable OD<sub>600</sub> value, considering path length and well volume (Table 1). Abs<sub>600</sub> of 1:2 dilutions of LUDOX silica suspension were taken in triplicate and a reference OD<sub>600</sub> of 0.063 (the reference value for 100 µL of LUDOX CL-X in a well of a standard 96-well flat-bottom black with clear bottom plate) divided by the mean measured value to give a conversion factor. </font></p> |
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| | + | <p><font size="2"><center>Table 1. Optical density readings for LUDOX CL-X 45% colloidal silica suspension and water used to calculate the conversion factor for absorbance readings to OD<sub>600</sub> readings for plate reader measurements. |
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| − | <p> This is shown not just in synthetic biology, but in other life sciences. Currently there is an investigation into experimental cancer biology research called the ‘Reproducibility Project’, which aims to assess studies and evaluate the reproducibility of their results (Morrison 2014). As of 2018, the project has assessed 10 high-profile studies with only four of these studies being able to be fully reproduced. However, there are conflicting opinions on the need for experimental reproducibility. Some believe that irreproducible studies will eventually be removed naturally from the scientific community through failed verification and evolution of newer, more efficient protocols that evolve naturally from exploratory research (Bissell 2013). There is also some concern among researchers that early standardisation may limit future scientific research and development (Gaisser et al. 2009). To combat this, Gaisser and colleagues (2009) suggested a stepwise standard introduction that spans 10 years, starting with reporting, before moving onto methods and components. Gaisser et al. (2009) proceeds to suggest that the standardisation process should be developed by the researchers themselves. </p>
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| + | <p><font size="3">2. A standard curve was prepared by measuring the OD<sub>600</sub> of serial dilutions of monodisperse silica microspheres, with similar light scattering properties to <i>E. coli</i> cells. This was used to standardise OD readings across labs (Figure 1A). </p> |
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| | + | <p><font size="3">3. A fluorescence standard curve was created by measuring the fluorescence of serial dilutions of the small molecule fluorescein. This has similar excitation and emission characteristics to GFP allowing conversion of fluorescence readings to an equivalent fluorescein concentration. Calibrations allowed expression measurement in units of fluorescence per OD and molecules of equivalent fluorescein (MEFL) per cell (Figure 1B). </p> |
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| + | <p><font size="2"><center>Figure 1. Curves used to calibrate A; Fluorescein per OD using dilutions of fluorescein and B; molecules of equivalent fluorescein per particle using dilutions of solutions of monodisperse silica microspheres.</font></center></p> |
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| − | <h3 class="subhead">Biodesign Automation</h3>
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| − | <p>Bio-design automation (BDA) is a new discipline that aims to automate the synthetic biology pipeline using workflows adapted from electronic design automation (EDA) (Rodrigo and Jaramillo 2013; Chen 2009). Through the adoption of BDA, the ‘Design-Build-Test’ cycle can be fully automated, with only the initial ‘Specification’ and final ‘Learn’ stages requiring human input. In the future, this could even be automated with the use of AI and machine learning. BDA can be split into two different categories; the automated hardware such as the integration of robotics, and the software that allows for in silico optimisation through computational modelling (Densmore and Bhatia 2014).</p>
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| − | <p> Automated hardware such as liquid handling robots and microfluidic systems allow for significantly reproducible, standardised workflows, while removing human error as a potential source of variation (Beal et al. 2016). Integrated software for synthetic biology allows for in silico modelling enabling the use of the available databanks. From this, complex genetic circuits can be built, designed and tested through computer simulation, without ever having to enter the laboratory for wet-lab experimentation. For an exhaustive review on the range of software available see Appleton and colleagues (2017) work. These synthetic biology workflows require data standards for BDA to be successfully implemented. Part standards must encode more than just the sequence, requiring the inclusion of additional data such as environmental and experimental information whilst also providing computational models of construct behaviour and measurements of performance (Galdzicki et al. 2014). Synthetic Biology Open Language (SBOL) is a data standard aiming to tackle this challenge. Developed by the synthetic biology community, it allows the exchange of biological circuit design in a format that can be understood by software and repositories (Galdzicki et al. 2014). Successful integration of SBOL into the wider scientific community would allow for a more directly integrated design workflow and increase the reproducibility of experimental results (Beal et al. 2012; Peccoud et al. 2011). </p>
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| − | <p> Laboratory robotics and smart lab equipment provides the platform for high reproducibility in experiments, however due to their current price bracket are unavailable to the majority of labs. This has driven recent developments in making affordable technologies for lab equipment, notably in the release of the Opentrons OT-2 liquid handling robot and the smart incubator by Incuvers Incorporated. With these developments and the ever-growing library of software, the likelihood that BDA will be integrated into most modern labs is high. </p>
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| − | <p>Minimum information standards (MIEO) provide explicit information on what information needs to be reported out of the experimental metadata that could influence the reproducibility of the result (Decoene et al. 2018). The following factors, based around Hecht and colleagues (2018) work (full MIEO in appendix), focused on experimental factors deemed most necessary in the growth and productivity of engineered organisms. This provides specific details for use of microtiter 96 well plates and shake flasks during culturing. </p>
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| − | <b style="font-size:30px"> Media Components </b>
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| − | <b> Effects on Growth </b>
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| − | <p> Growth media is essential to any form of microbial culture, providing the nutrition required for optimal growth. There are a number of different options available, with Lysogeny Broth (LB) (Bertani 2004), Super Optimal Broth (SOB) (Hanahan 1983) and Terrific Broth (TBr) (Tartof 1987) being the most commonly used. However most are rich and undefined media, containing extracts such as yeast or beef that have an unquantifiable and highly variable composition. These extracts are also generally more expensive, complicate recovery and, due to their variable composition, result in significant batch-to-batch variation (Lee 1996; Moser et al. 2012). In the literature researched, TBr contributed to the highest amount of culture growth with Escherichia coli, with Losen et al. (2004) stating that TBr lead to an increase of 5x biomass when compared to LB. Islam (2007) produced similar results, with a significantly higher soluble protein yield in TBr than LB. This was put down to having glycerol as a defined carbon source. Furthermore, it is suggested that glucose is a poor choice due to E. coli excreting acetic acid as a by-product of glucose consumption, lowering pH and reducing growth (Islam et al. 2007; Losen et al. 2004; Marini et al. 2014). Glucose however is not the only issue. Singh et al. (2017) suggests that the carbon and nitrogen source are the most important components of the media as they can affect the type and amount of product produced. Other studies have concluded that E. coli develops a media history, adapting to different medias over time, showing variations in ribosome and RNA polymerase efficacy due to the medias amino acid makeup (Ehrenberg et al. 2013; Paliy and Gunasekera 2007). </p>
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| − | <p> Inorganic ions can also play an important role in the growth of cultures. Studier (2005) carried out an exhaustive study on inducer effects in media, investigating a number of variables as well as the presence of inorganic ions. The data collected showed that phosphate promoted kanamycin resistance, while sulphate supported optimum growth. However, on the contrary limiting magnesium concentrations allowed the cell culture to grow to a higher OD600. </p>
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| − | <b> Effects on Protein Yield </b>
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| − | <p> It is known that with an increased amount of cell growth, there is generally a higher yield of recombinant protein (Khan et al. 2009). If E. coli are made to produce protein at too high a rate however, inclusion bodies will form which is deemed inefficient due to the complex process of refolding them into functional proteins (Marini et al. 2014). Marini and colleagues (2014) showed that even with higher cell density, functional protein expression had no change. Therefore, there must be a point of optimal cell growth that provides the highest amount of functional protein whilst causing the least amount of inclusion body formation or incorrect protein folding. Antibiotic selection can also impact on the protein yield. Using kanamycin in higher concentrations has been shown to increase plasmid stability and allow maintenance of higher plasmid copy numbers due to selection pressures (Kelly et al. 2009). </p>
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| − | <p> Control of pH is essential for growth mediums. All forms of bacterium have optimum pH’s, even at the extremes (acidophiles and alkaliphiles). However, in current synthetic biology many of the used chassis are generally classified as neutrophiles, so maintaining a pH of between 6-8 is essential. Presser et al. (1997) carried out an in-depth study of E. coli growth rates modelling the growth as a function of pH and lactic acid concentration. From this, E. coli was determined to have a pH boundary of 4.0, beyond which resulted in no growth. Lactic acid was found to be inhibitory in high concentrations, something required to consider during scale-up. This links back to aforementioned studies that showed the importance of carbon source, with glucose instigating a drop of pH and inhibition of E. coli growth (Islam et al. 2007; Losen et al. 2004; Marini et al. 2014). However, for very niche experimentation, suboptimal pH may play an important role in experimental design. Maurer et al. (2005) discussed how pH regulates a number of genes, including flagellar motility, catabolism and oxidative stress in E. coli. High pH 8.7 was found to repress membrane proteins, chemotaxis and flagellar motility. Low, acidic conditions of pH 5.0 were found to increase metabolic rates. In conclusion, experimentation entailing genetic circuits and protein production can be said to be drastically affected by pH. pH therefore must be defined in the experimental method for reproducible results. </p>
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| − | <p> If synthetic biology is going to follow a BDA approach, robotics will need to be implemented into microbial growth workflows. For high-throughput, this requires the use of multi-well plates and much smaller volumes than standard batch microbial growth techniques. This opens up an entirely new area of irreproducibility and standardising these experiments is therefore crucial to further understanding. Additionally, in a study of 49 papers in the field of synthetic biology it was estimated that upwards of 80% of papers did not provide complete information to fully reproduce their data (Chavez et al. 2017). </p>
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| − | <p> In microtiter plates, there are two main variables that need to be optimised; the oxygen transfer rate (OTR) and in turn, the overall mass transfer (KLa). Both of these variables have a significant inhibiting effect on microbial growth if not optimised. Fortunately in microtiter plates, both variables can be optimised in tandem by employing the same methods. OTR has been found to increase with increasing well size and a decrease in fill volume, as would be expected due to the reliance of surface aeration (Running and Bansal 2016; Hermann et al. 2003; Schiefelbein et al. 2013). Hermann and colleagues (2003) presented a clear correlation that a decreasing fill volume results in an increased OTRmax, that is only accentuated with increasing shake speeds. However, increasing the media viscosity can also decrease the OTR (Giese et al. 2014; Klöckner et al. 2013; Running and Bansal 2016), so shaking and baffling are essential to the optimisation of growth. If multiple wells have different viscosities a compromise must be made. </p>
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| − | <p> Shaking in microtitre plates needs to surpass the critical shaking frequency, whereby the centrifugal force exceeds that of the interfacial surface tension (Hermann et al. 2003; Kensy et al. 2005). Funke and colleagues (2009) states that below 500 rpm, this is not reached and no significant increase in OTR is seen. Unfortunately the data is not shown, but due to their significant OTR increases from 500-1000 rpm, this suggests it is reliable. The shaking diameter for their results only covers 3 mm, but previous work has conflicting results, using a larger shaking diameter and lower rpm OTR. In two papers, a 300 rpm and shaking diameter of 50 mm was shown to increase OTR significantly, with a shaking diameter of 25 mm showing a 3x decrease when compared with 50 mm (Duetz et al. 2000). However there was some splashing in larger wells (Duetz and Witholt 2004). On the contrary, Hermann et al. (2003) found that a shaking at 300 rpm at 25 mm produced no significant difference in OTR than if not shaken but also confirmed that any higher than 400 rpm at 25 mm would cause liquid spillage. </p>
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| − | <p> Baffling changes the flow characteristics of wells, increasing the turbulence and mixing. In microtitre plates, baffling is not standardised as in shake flasks, so the amount of laboratory’s with access to intentionally baffled microtiter plates is limited. Baffling in microtitre plates can also increase the chance of ‘out-of-phase phenomena’, where the flow of liquid creates an unmixed space at the bottom of the well (Büchs et al. 2001). Funke et al. (2009) designed 30 different well shapes, with a gradually increasing number of edges/baffles. From this, standard spherical wells were found to have the worst OTRmax and KLa, whilst the novel 6 edged petal shape allowed maximal OTR. Realistically, the 6 edged petal shape used is not commercially available so it would be unacceptable to suggest this use is common place. Despite this, other research groups have found that square wells have a baffle-like effect (Duetz 2007; Duetz and Witholt 2004; Hermann et al. 2003). </p>
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| − | <p> Wells are usually covered to prevent evaporation throughout experimentation. The most common are oils, lids, stickers and seals. The type of cover used can significantly affect the growth rates and overall experimental data (Chavez et al. 2017). Oil has been shown to prevent evaporation entirely, however it significantly lowers the OTR and reduces protein expression, making it sub-optimal for most synthetic biology uses (Chavez et al. 2017). Chavez and colleagues (2017) found that lid and sticker covers allowed for the greatest OTR and protein expression and that lid covering also caused the highest evaporation rate, specifically in the four corner wells. Sealing the plate is another option, however this method has only been to shown to reduce the OTR with minimal evaporation prevention (Zimmermann et al. 2003; Sieben et al. 2016). </p>
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| − | <p> The overarching aim for the 2018 Interlab focuses on the weakness in the measurement of fluorescence relative to optical density (OD), as with previous IGEM interlab protocols there is potential discrepancy between optical density and actual cell concentration. This year the iGEM study aims to reduce lab-to-lab variability further by measuring GFP fluorescence relative to absolute cell counts or colony forming units. Normalisation of fluorescence to colony forming units also allows measurement of fluorescence relative only to viable cells, and thus a more accurate measurement of promoter strength, whereas OD600 and absolute cell count measures cannot differentiate between viable and non-viable cells. </p>
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| − | <p> However, in this case the GeneMachine team further investigated the core reproducibility and standardisation aspect of the Interlab. What were its flaws and weaknesses? How could variation be minimised? How could it be standardised? Using a statistically driven Design of Experiments (DoE) methodology to aid in optimisation and a BDA workflow for enhanced reproducibility and standardisation, three main aims were investigated: </p>
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| − | <li> The use of an internal standard to allow comparative results through all test devices indicating sources of variation in protein production and expression. This should highlight the efficacy of promoter strength and resulting protein production. </li>
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| − | <li> The development of a E. coli Dh5a growth model to investigate how media effects the expression of the Interlab test devices and to determine the optimal media for reproducible results. </li>
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| − | <li> The automation and optimisation of competent cell preparation and transformation workflows. To create an automated and most importantly robust protocol to allow the reproducible generation of competent cells for consistent transformation of E.coli Dh5a. </li>
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| − | <p>Following calibrations, two transformed colonies for each test device and both controls were used to inoculate LB medium containing chloramphenicol (CAM) and incubated overnight at 37 °C with shaking at 220 rpm. Overnight cultures were diluted 1:10 and the OD600 adjusted to 0.02 with LB with CAM to a final volume of 12 ml. Fluorescence and Abs600 were taken at 0h and 6 hours of incubation at 37 °C with 220 rpm shaking. Test devices, plasmid backbone and protocol workflow are shown in figure 2.</p>
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| − | <img src="https://static.igem.org/mediawiki/2018/4/4e/T--Newcastle--IntProt.PNG"> | + | <p><font size="3">Following these calibration steps, two transformed colonies for each test device and both control plasmids were used to inoculate lysogeny broth (LB) medium containing 25 µg/L of chloramphenicol (CAM) and incubated overnight at 37 °C with shaking at 220 rpm. Overnight cultures were diluted 1:10 and the OD<sub>600</sub> adjusted to 0.02 with LB with CAM to a final volume of 12 mL. Fluorescence and Abs<sub>600</sub> were taken at 0h and 6 hours of incubation at 37 °C with 220 rpm shaking. Test devices and plasmid backbone are shown in Figure 2.</font></p> |
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| − | <p>All test devices produced growth to OD600 reading in excess of 0.3, except test device (TD) 4. Despite lower growth than other transformants, TD4 produced the highest mean fluorescence reading of 79.1 a.u., as was expected as the strongest promoter of the Anderson collection (parts.igem.org/Promoters/Catalog/Anderson). Figure 3A and 3B show the colony 1 and colony 2 Abs600 values for the controls and each test device at 0 hours and 6 hours respectively. </p>
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| − | <img src="https://static.igem.org/mediawiki/2018/8/85/T--Newcastle--ABS600.PNG"> | + | <p><font size="2"><center>Figure 2. A) test devices transform into DH5α for the iGEM InterLab study. A negative control device consisting of a tetracycline resistance gene (BBa_R0040) with no associated promoter, RBS or terminator sequences was also transformed into DH5α. B) All test devices were transformed into DH5α using the pSB1C3 plasmid backbone with chloramphenicol for selection (adapted from LabGenius plasmid viewer).</font></center></p> |
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| − | <h1 class="display-2">Fluorescein/OD<sub>600</sub> and MEFL/particle analysis</h1> | + | <h1 class="display-2">Results</h1> |
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| − | <p>The relatively poor growth and high fluorescence levels effectively cancelled each other out when readings were converted to Fluorescence per OD600 and MEFL per OD600 measurements, resulting in TD4 transformants producing the highest expression levels (figure 3.1). The high fluorescence and MEFL per OD600 reading for TD4 despite lowest growth suggests expression of TD4 is not fully representative of the relative promoter strength; expression levels are interdependent with growth rate, with higher growth rates expected to produce higher expression levels (Scott et al. 2010). Expected fluorescence levels based on relative promoter strength reported for the Anderson collection of promoters did not match entirely the results produced here. In particular, TD5 utilising the promoter J23104 was expected to be the second strongest but yielded only the fourth highest fluorescence reading of 22.14 and 21.42 for colonies 1 and 2 respectively. Similarly, the highest fluorescence reading was recorded by TD1 (expected strength: third), though this test device produced the widest range in fluorescence reading between the two colonies (r = 36.2), despite both colonies having the closest OD600 reading of any of the test devices (r = 0.007). In addition to the iGEM repository documentation for relative strengths of the Anderson promoter collection, previous literature has also demonstrated that the J23101 and J23104 promoters should have almost equal strength (He et al. 2017).</p>
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| − | <p>While TD5 appeared to underperform compared to promoter activity previously reported in the literature, subsequent sequencing of test devices revealed that colonies labelled as TD5 had in fact been transformed with the positive control device. This may have simply been the result of human error when pipetting or labelling over the process of the study. The consistence of TD5 underperformance across multiple replications of the study suggests that this occurred early on. The variation in expression visible is particularly alarming for the J23101 promoter, which has been proposed and utilised in the literature as a reference promoter to characterise relative strengths of other promoters as relative promoter units (Kelly et al. 2009).</p> | + | <p> <font size="3">Results for OD<sub>600</sub> and fluorescence measurements after 6 hours are shown in Figure 3. </font></p> |
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| + | <p><font size="2"><center>Figure 3. OD<sub>600</sub> (A) and fluorescence (B) InterLab study results for positive and negative controls and all test devices after 6 hours. Tests were performed in duplicate using two colonies for each test device, results for which are shown separately to account for colony-to-colony variation. </center></font></p> |
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| + | <p><font size="3">Results for both 0 and 6 hours were converted into fluorescence per OD<sub>600</sub> and fluorescence per cell using the previously described calibrations (Figure 4). While the InterLab study is an opportunity for crowdsourcing a large amount of data on the inherent variability of a given biological system, it does not account for variations within data sets. Our team attempted to seek out and address such sources of variation, investigating biodesign automation of protocols, use of internal standards and effects of media composition. Further detail and results are available on the <a href="https://2018.igem.org/Team:Newcastle/Measurement"class="black">measurements page</a>.</font></p> |
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| − | <h1 class="display-2">Sources of variation in the InterLab study design</h1>
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| − | <p>While the InterLab study is an effective way of gathering large sets of data surrounding the parts and protocols, it does not consider all sources of variation within datasets or the variability between data sets. As there are many factors which cause variability in microbial protein expression and productivity, some of these factors may be more favoured than others, leading to an overrepresentation of these in the metadata. </p>
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| | + | <p><font size="2"><center>Figure 4. iGEM InterLab study results for positive and negative controls and all test devices. Fluorescence was measured at 525 nm and readings were taken at 0 hours and 6 hours in a 96 well plate. A: Fluorescence per OD<sub>600</sub>, B: Fluorescence per <i>Escherichia coli</i> cell, calibrated to a standard curve of LUDOX silica beads. </center></font></p> |
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| − | <p>Even prior to the main cell measurement protocol, irreproducibility had a significant effect in the preparation of competent cells and successful transformation. While the recommended CCMB80 and transformation protocols were followed exactly, successful expression of transformants were not guaranteed. It took 3 weeks of constant run-throughs within our lab to gain a transformation efficiency (TrE) of 5.05 x 106 before we could even start the Interlab. As such, the competent cell and transformation process was further investigated through a Bio-design Automation platform that used a design of experiments methodology to optimise transformation buffer (TB) composition. Utilising our recently acquired OT-2 liquid handling robot (Opentrons, USA), a robust automated competent cell preparation protocol was developed.</p>
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| − | <p>One factor that has been overlooked in the literature is the recovery period. Anecdotal evidence during all transformation protocols has indicated that the antibiotic resistance gene has a significant impact on how long the recovery time needs to be. For chloramphenicol, the widespread suggestion of a 1-hour recovery incubation for optimal TrE is quite simply incorrect, with a recovery incubation time of upwards of 2 hours being required for optimal TrE in our study. This is irrespective of volume, be it in 2 mL microcentrifuge tubes or 96 well plates. However, if using a 96 well plate format, this recovery period requires full optimisation due to its suboptimal OTR and KLa characteristics. With the additional growth inhibition of the majority of TB compositions, this recovery step needs to be fully optimised for optimal TrE. Super optimal broth with catabolite repression (SOC) is regularly used instead of SOB to enhance cells recovery, however as it includes glucose, this was not considered due to the potential inhibitory effects of increasing pH due to glucose metabolism (Islam et al. 2007; Losen et al. 2004; Marini et al. 2014). </p>
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| − | <p>Experimental observation found ampicillin (AMP) did not require longer incubation. Chloramphenicol’s requirement for increased recovery time can be explained through its mechanism of action, inhibiting protein synthesis via the inhibition of peptidyl transferase (Schifano et al. 2013; Wolfe and Hahn 1965). AMP however inhibits cell wall synthesis via inhibition of transpeptidase. Unlike AMP resistance, which involves the synthesis and excretion of either β-lactamase or penicillinase (Drawz and Bonomo 2010), chloramphenicol resistance is acquired by the synthesis of chloramphenicol acetyltransferase which is not readily excreted (Shaw 1983). This is beneficial for generating libraries as it decreases risk of satellite colonies, but the resistance mechanism may take longer to form and confer sufficient antibiotic resistance. As a result, for a further optimised protocol, using a plasmid that confers AMP resistance would be beneficial to minimise the protocol time requirement. This would also decrease the safety risk as while CAM is a known carcinogen, AMP is not. </p>
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| − | <p>Even prior to the main cell measurement protocol, irreproducibility had a significant effect in the preparation of competent cells and successful transformation. While the recommended CCMB80 and transformation protocols were followed exactly, successful expression of transformants were not guaranteed. It took 3 weeks of constant run-throughs within our lab to gain a transformation efficiency (TrE) of 5.05 x 106 before we could even start the Interlab. As such, the competent cell and transformation process was further investigated through a Bio-design Automation platform that used a design of experiments methodology to optimise transformation buffer (TB) composition. Utilising our recently acquired OT-2 liquid handling robot (Opentrons, USA), a robust automated competent cell preparation protocol was developed.</p>
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| − | <p>Even prior to the main cell measurement protocol, irreproducibility had a significant effect in the preparation of competent cells and successful transformation. While the recommended CCMB80 and transformation protocols were followed exactly, successful expression of transformants were not guaranteed. It took 3 weeks of constant run-throughs within our lab to gain a transformation efficiency (TrE) of 5.05 x 106 before we could even start the Interlab. As such, the competent cell and transformation process was further investigated through a Bio-design Automation platform that used a design of experiments methodology to optimise transformation buffer (TB) composition. Utilising our recently acquired OT-2 liquid handling robot (Opentrons, USA), a robust automated competent cell preparation protocol was developed.</p>
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| − | <p>Even prior to the main cell measurement protocol, irreproducibility had a significant effect in the preparation of competent cells and successful transformation. While the recommended CCMB80 and transformation protocols were followed exactly, successful expression of transformants were not guaranteed. It took 3 weeks of constant run-throughs within our lab to gain a transformation efficiency (TrE) of 5.05 x 106 before we could even start the Interlab. As such, the competent cell and transformation process was further investigated through a Bio-design Automation platform that used a design of experiments methodology to optimise transformation buffer (TB) composition. Utilising our recently acquired OT-2 liquid handling robot (Opentrons, USA), a robust automated competent cell preparation protocol was developed.</p>
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| | + | <p class="about-para"><strong>Attributions: Matthew Burridge, Kyle Stanforth, Sam Went |
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| − | <p class="about-para"><font size="2">1. Jousset, A., et al. (2009). "Predators promote defence of rhizosphere bacterial populations by selective feeding on non-toxic cheaters." The Isme Journal 3: 666<font></p>
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| − | <p class="about-para"><font size="2">2. Jousset, A., et al. (2009). "Predators promote defence of rhizosphere bacterial populations by selective feeding on non-toxic cheaters." The Isme Journal 3: 666<font></p>
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| − | <p class="about-para"><font size="2">3. Vanitha SC & Umesha S (2011) Pseudomonas fluorescens mediated systemic resistance in tomato is driven through an elevated synthesis of defense enzymes. Biologia Plantarum 55(2):317-322.<font></p>
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| − | <p class="about-para"><font size="2">4. United Nations, Department of Economic and Social Affairs, Population Division (2017) World Population Prospects: The 2017 Revision, Key Findings and Advance Tables. https://population.un.org/wpp/Publications/Files/WPP2017_KeyFindings.pdf<font></p>
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| − | <p class="about-para"><font size="2">5. Food and Agriculture Organization of the United Nations (2015) World Fertilizer Trends and Outlook to 2018. http://www.fao.org/3/a-i4324e.pdf<font></p>
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| − | <p class="about-para"><font size="2">6. Usman MN, MG; Musa, I (2015) Effect of Three Levels of NPK Fertilizer on Growth Parameters and Yield of Maize-Soybean Intercrop. International Journal of Scientific and Research Publications 5(9).<font></p>
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| − | <p class="about-para"><font size="2">7. Pfromm PH (2017) Towards sustainable agriculture: Fossil-free ammonia. Journal of Renewable and Sustainable Energy 9(3):034702.<font></p>
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| − | <p class="about-para"><font size="2">8. Bitew YA, M (2017) Impact of Crop Production Inputs on Soil Health: A Review. Asian Journal of Plant Sciences 16(3):109-131.<font></p>
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| − | <p class="about-para"><font size="2">9. Yang X-e, Wu X, Hao H-l, & He Z-l (2008) Mechanisms and assessment of water eutrophication. Journal of Zhejiang University. Science. B 9(3):197-209.<font></p>
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| − | <p class="about-para"><font size="2">10. Carmichael WW (2001) Health Effects of Toxin-Producing Cyanobacteria: “The CyanoHABs”. Human and Ecological Risk Assessment: An International Journal 7(5):1393-1407.<font></p>
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| − | <p class="about-para"><font size="2">11. New Partnership for Africa's Development (2013) Agriculture in Africa - Transformation and Outlook. http://www.un.org/en/africa/osaa/pdf/pubs/2013africanagricultures.pdf<font></p>
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| − | <p class="about-para"><font size="2">12. Food and Agriculture Organization of the United Nations (2017) World Fertilizer Trends and Outlook to 2020. http://www.fao.org/3/a-i6895e.pdf<font></p>
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| − | <p class="about-para"><font size="2">13. Bergey, D. H., et al. (1984). Bergey's manual of systematic bacteriology. Baltimore, MD, Williams & Wilkins.<font></p>
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| − | <p class="about-para"><font size="2">14. Gómez-Lama Cabanás C, Schilirò E, Valverde-Corredor A, & Mercado-Blanco J (2014) The biocontrol endophytic bacterium Pseudomonas fluorescens PICF7 induces systemic defense responses in aerial tissues upon colonization of olive roots. Frontiers in Microbiology 5:427.<font></p>
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| − | <p class="about-para"><font size="2">15. Gross, H. and J. Loper (2009). Genomics of secondary metabolite production by Pseudomonas spp.<font></p>
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| − | <p class="about-para"><font size="2">16. Sharma SB, Sayyed RZ, Trivedi MH, & Gobi TA (2013) Phosphate solubilizing microbes: sustainable approach for managing phosphorus deficiency in agricultural soils. SpringerPlus 2:587.<font></p>
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