Difference between revisions of "Team:Exeter/Model"

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<li ><a id="links"href="https://2016.igem.org/Team:Exeter/Model">Models</a></li>
 
<li ><a id="links"href="https://2016.igem.org/Team:Exeter/Model">Models</a></li>
 
<li ><a id="links"href="https://2016.igem.org/Team:Exeter/Collaborations">Collaborations</a></li>
 
<li ><a id="links"href="https://2016.igem.org/Team:Exeter/Collaborations">Collaborations</a></li>
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<div class="container">
 
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<h1 id="title">Models</h1>
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<h1 id="title">Collaborations</h1>
 
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<a href="#section_1" class="banner_link col-xs-6 col-sm-3"><span class="oneline">Model Overview</span></a>
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<a href="#section_1" class="banner_link col-xs-6 col-sm-3"><span class="oneline">Measurement</span></a>
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<a href="#section_2" class="banner_link col-xs-6 col-sm-3"><span class="oneline">KillerRed/Orange Model</span></a>
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<a href="#section_2" class="banner_link col-xs-6 col-sm-3"><span class="oneline">Software</span></a>
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<a href="#section_3" class="banner_link col-xs-6 col-sm-3"><span class="oneline">Enzymatic Model</span></a>
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<a href="#section_3" class="banner_link col-xs-6 col-sm-3"><span class="oneline">Parts</span></a>
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<a href="#section_4" class="banner_link col-xs-6 col-sm-3"><span class="oneline">S4</span></a>
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<a href="#section_4" class="banner_link col-xs-6 col-sm-3"><span class="twoline">Skype and<br /> Meet-ups</span></a>
 
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S1
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Measurement: Newcastle </div>
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</div>
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 +
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<p id="pp">This year we have been working alongside iGEM teams from across the globe.
 +
We have been working closely with teams from Newcastle, Glasgow and Purdue to help each other improve our projects
 +
from both in and outside the lab.</p>
 
 
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<img src="https://static.igem.org/mediawiki/2016/8/88/T--Exeter--Home_collab_cond.jpg" style="float:right; width:40vw; height:60vh;">
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<div id="contentTitle">
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KillerRed/KillerOrange model
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</div>
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<p id="pp">Given that Grenoble 2013 built a kinetic model, using OD over time, that described their experimental data very well and Carnegie Mellon 2013 had taken an intracellular approach with kinetics, we decided to consider our system from a different angle. Once again, we looked at the system on an intracellular level but varied our approach by concentrating on a more physical point of view and assessing the rates of production on a case-by-case basis rather than system-wide.</p>
+
  
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<p id="pp">At the outset, multiple assumptions were made to simplify the the system, enabling suitable definition of  appropriate variables and rates. Mutations were also ignored to eradicate this further layer of complexity. Mindful that the timeframe considered was much larger than the replication time of <i>E. coli</i>,  the amount of mRNA, Protein etc was adjusted accordingly by assuming that it splits evenly and halving the amount every time replication occurs, also affecting the amount of plasmids. However, as cited in (Nordström and Dasgupta, 2006), the frequency of replication is variable to maintain a constant amount. Therefore, in the case of our plasmid pSB1C3, which had a high copy number ~300, the amount was kept constant even after replication of the <i>E. Coli</i>.</p>    
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<p id="pp">Part of the Newcastle iGEM team’s project this year
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+
involved an experiment centred around the creation of biological electronic
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<p id="pp">We then isolated one cell and listed the major factors in the production of reactive oxygen species (ROS) starting from the transcription of mRNA.</p>
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  components. Newcastle asked our team if we could help them out by finding the  
 +
  thermal conductivity of different growth media. With the help of our biophysicist
 +
supervisor Ryan Edgington, we came up with a plan to measure the conductivity.</p>
 +
  
−
<br>
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<p id="pp">Using the apparatus we had available, we discovered that the thermal conductivity of LB and M9 broth to be roughly
−
<br>
+
the same as water. The conductivity of water at room temperature is about 598.4 $\frac{mW}{Km}\text{ }$(mili
−
 
+
watt per metre kelvin).We found the conductivity of LB and M9 to be (605 $\pm$ 20) $\frac{mW}{Km}\text{ }$ and (570 $\pm$ 30) $\frac{mW}{Km}\text{ }$ respectively
−
 
+
You can read more about our method <a href="https://2016.igem.org/Team:Exeter/Team/collab">here</a>.</p>
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                <div class="col-xs-12" style="padding:0;margin:0;">
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                    <img src="https://static.igem.org/mediawiki/2016/thumb/f/f7/T--Exeter--Modelling_Flow1.png/799px-T--Exeter--Modelling_Flow1.png" style="max-width:100%;margin:auto;display:block;">
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                </div>
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                <ul>
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<li>k1 is the rate of transcription</li>
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<li>k2 is the rate of mRNA degradation</li>
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<li>k2 is the rate of mRNA degradation</li>
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<li>k4 is the rate of degradation of the protein</li>
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                        <li>k5 is the rate of reactive oxygen species production</li>
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</ul>
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<h6>Protein production:</h6>
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<p id="pp">From this, we isolated the Transcription/Translation mechanism (k1 & k3) along with the degradation (k2 & k4) to obtain a protein production time.</p>
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<p id="pp">K1 was calculated to be 22.175s-1. This was achieved by taking the total number of base pairs of the plasmid (887) and dividing through by the transcription rate of 40 base pairs per second for the T7 promoter (García and Molineux, 1995).</p>
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<p id="pp">K3 was calculated in a similar way to be 28.690s-1. As the value found for the rate of 8.4 amino acids per seconds (Siwiak and Zielenkiewicz, 2013) is given in amino acids and not base pairs, this result was arrived at by taking the total number of base pairs of the protein coding region (723) and dividing by 3, given that a conversion of 3 base pairs = 1 amino acid is viable.</p>
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<p id="pp">K2 was found to be 3-8 minutes for 80% of mrna (Bernstein et al., 2002). 5 minutes was used as an approximation of the median time as the actual number fluctuates.</p>
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<p id="pp">K4 was initially estimated to be greater than 10 hours using protparam on expasy.org. 10 hours was used as the degradation time as this exceed the observed time taken for death of the cell.</p>
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<p id="pp">Simbiology, a modelling app for matlab, was used to model the flow diagram with the above rate parameters. Running the simulation, a rate for mrna production and protein production could be found. A few issues were initially identified, these being that the degradation times affected the transcription/translation rate directly and also that as soon as mrna production started, protein production was initialised. This suggested that even when the mrna is in the process of being transcribed, translation has started and we know this not to be the case as you can’t start transcription with a non integer amount of mRNA.</p>
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<p id="pp">To correct this mRNA needed to be a step function, limiting the amount to whole integers and meaning that production of the protein was restricted until the first mrna was produced. However, this only served to add a delay onto the overall protein production and multiply it by the mRNA amount as seen in Figure 1. This highlights another problem. As we can see in Figure 2 below, we know that it takes ~28 s to produce a protein (K3) after the mRNA has been made, suggesting that it should occur at  ~54s. However, the second mRNA is produced in advance of this, increasing the rate at which the protein are produced and making the first protein at  ~53s. This correctly describes the overall rate of the system in producing proteins but is incorrect for finding the time at which they are made, as each mRNA is independent of any other. This means that each mRNA required  individual consideration based on each having a separate protein production mechanism that contributes to a total protein quantity. </p>
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                    <img src="https://static.igem.org/mediawiki/2016/1/17/T--Exeter--Modelling_GraphKRone.png" style="max-width:100%;margin:auto;display:block;">
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                    <span class="caption">Figure 1: A graph showing the mRNA amount in blue over 100 seconds and the overall rate of Protein production in red over 100 seconds.</span>
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                    <span class="caption">Figure 2: A plot of the overall protein production in blue and the rate at which the first mrna independently produces a protein in red. Creating proteins at ~53s and ~54s respectively.</span>
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<p id="pp">To enable this we moved away from Simbiology and attempted to use Simulink. However, after careful consideration, we decided instead to write the process in C. This allowed us to handle each mRNA and its creation time separately and have it produce proteins up to the time in which the protein was induced. An array was used with each element representing a second; every time a protein was created the corresponding time element would increase by one. The total of all elements were then taken to find an overall amount of protein.</p>
+
−
 
+
−
<p id="pp">The next step was to include degradation. This was achieved by limiting the protein production of an individual mRNA to its respective degradation time. For protein degradation, the amount of time left from the time of creation until the overall run time (in our case the point at which we started shining light on the cultures) was compared to the degradation time; if the difference was greater, the protein degrades and is subtracted from the total.
+
 
</p>
 
</p>
  
−
<p id="pp">Further, we took into account multiple ribosomes on one mRNA (polyribosome) and maturation. Polyribosomes were included in the code by restricting the first protein created by each mRNA to the the calculated translation rate found above (~28s). Then for the following proteins we used a modified rate where we divided the rate by the amount of ribosomes, which was found to be 3.46 per 100 codons (Siwiak and Zielenkiewicz, 2013). In this case it was calculated to be 8 if we rounded down to the nearest integer. As the degradation and the ability to produce ROS applies to only folded proteins, we wanted to know how many mature proteins there would be. Calculating a new run time was achieved by taking away the maturation time and the time taken to make one mRNA and protein,  suggesting that the proteins made in the new run time were created and matured and ready to produce ROS if they had not degraded. </p>
 
  
−
<p id="pp">Examining the degradation time for the protein we adjusted the previously estimated value to be that of green fluorescent protein (GFP), as Killerred is a homologue to provide a more accurate degradation time. However, we could only find a half life of the protein so to calculate the degradation we used the equation below</p>
 
  
−
<p id="pp">
 
−
<span class="equation">$T = \frac{T_{\frac{1}{2}}}{ln(2)}$<span class="equation_ref">(Hyperphysics.phy-astr.gsu.edu, 2016)</span></span>
 
−
<span class="equation_key">
 
−
$T$: Degredation time [$\text{s}$]<br />
 
−
$T_{\frac{1}{2}}$: Half life[$\text{s}$]
 
−
</span>
 
−
</p>
 
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−
                <p id="pp">Running the initial model provided an overall protein quantity of under 3 million and mRNA at a maximum of 4200 (Thermofisher.com, 2016), which fits within the expected amount. This suggests that the rates and process are a good, basic approximation of protein production.</p>
 
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<a id="Section_link" href="#section_2" style="display:block;margin:20px auto 0 auto;width:14px;"><span style="color:#47BCC2;font-size: 25px;" class="glyphicon glyphicon-menu-down" aria-hidden="true"></span></a>
 
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Enzymatic Models
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Software </div>
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</div>
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<div>
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<h5>mRNA production in Enzymatic kill switches</h5>
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<h3>Purdue</h3>
 +
 
 +
<p id="pp">Our team helped Purdue with this by logging data for the 260
 +
iGEM teams of 2015 and critiquing ease of use and effectiveness of the database. For each team
 +
we documented a summary of what their project was about, their track, number of team members, chassis,
 +
research benchmarks, finished parts, the presence or absence of kill switches, medals and any awards
 +
and nominations. We tagged the teams summaries with keywords to make finding a project much easier.</p>
 +
 
 +
<p id="pp">We gave Purdue feedback on the design, layout and how
 +
easy this database was to use to help them improve on what they had done so far.</p>
 +
<br />
 +
<h3>Edinburgh Collaboration</h3>
 +
<h6>Optimising methods of data mutation detection in BabbleBlocks</h6>
 
<p id="pp">
 
<p id="pp">
−
The change from software such as Simbiology and Simulink called for a more fundamental method of modelling cell death. Preliminary research
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Storing information on DNA offers many advantages over current methods, however mutations
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showed kill switches producing the proteins “Lysozyme c” and “DNase 1” both had very similar mechanisms; as both are enzymes. Therefore, it
+
need to be carefully monitored to ensure incorrect data is not read as a false positive.
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was decided that the two models would use the same code to simulate mRNA and protein production.
+
Currently for information stored on a BabbleBrick a ‘CheckSum’ is calculated by taking the
 +
sum of the values on each base of DNA. If the checksum of a BabbleBlock has changed between
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the time of writing and reading, the data is considered to be corrupt.
 
</p>
 
</p>
 
<p id="pp">
 
<p id="pp">
−
Initially, following advice from biologists and biochemists on our team, the first code incorporated two step functions, the first of these
+
<span class="equation">$C = \sum^{bp}_{n=1} bp_n$</span><br />
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modelled mRNA production by a single <i>E. coli</i> cell. To calculate protein made by each mRNA a secondary step function triggered each time an mRNA
+
−
was produced, the sum of these functions gave the total amount of protein. This program was simple with the only input variables being the production time of
+
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mRNA and the respective protein.
+
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The initial model was presented to the rest of the team receiving plenty of feedback, the most prevalent point being that the code modelled a single
+
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cell system with a single plasmid whilst it should model a single cell system that duplicates and has multiple plasmids. It was suggested the following factors were added to the model:
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</p>
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<ul>
+
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<li>Plasmid production</li>
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<li>Degradation of mRNA and protein</li>
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<li>Maturation of protein</li>
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<li>Duplication rate of <i>E. coli</i></li>
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</ul>
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<p id="pp">
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The initial feedback called for a re-write of the code as a considerable amount of the suggestions came in stages before mRNA production.
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A second version of the code was written which incorporated all main steps between the splitting of <i>E. coli</i> to the degradation rate of protein.
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<h6>Assumptions</h6>
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<p id="pp">
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It is important to outline the factors that were overlooked due to research finding their effect on the model would be negligible. Firstly, after
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researching the production time of plasmids in <i>E. coli</i> it was found that plasmids will reproduce at a variable rate to maintain a constant population
+
−
determined by their copy number (Nordström and Dasgupta, 2006). To address this, the production rate of plasmids was overlooked, allowing for a
+
−
constant value of plasmids to be maintained throughout the simulation. In experiments a pSB1C3 strain was used - a high copy number plasmid;
+
−
therefore the copy number was set to 300.
+
−
Secondly, both enzymatic models will assume that travel time of protein to the substrate is negligible. Protein diffusing through the cytoplasm of
+
−
<i>E. coli</i> have a diffusion coefficient on the scale of $10 \mu \text{m}^2 \text{s}^{-1}$ (Elowitz et al., 1998). Considering the surface area of <i>E. coli</i> is approximately
+
−
$10 \mu \text{m}^2$, the protein will reach the cell wall in a several seconds which is several magnitudes of order smaller than the simulation time. Lastly, the models
+
−
will assume no mutations occur; the aim of the simulations is to determine whether kill switches are a plausible method of biosafety, if the models show
+
−
a kill switch is not a reliable way to terminate GMO’s then accounting for mutations will only that enforce statement.
+
−
</p>
+
−
<h6>Features</h6>
+
−
<p id="pp">
+
−
Research showed that there is an upper limit of mRNA in <i>E. coli</i>,
+
−
therefore an upper limit of $4 \times 10^3$ mRNA per <i>E. coli</i> cell (Thermofisher.com, 2016) has been included in the model. The lifetime of mRNA can be found
+
−
from the observed half life of approximately 5 minutes or $300\text{s}$ (Bernstein et al., 2002), resulting in an average lifetime of $430\text{s}$. The production rate of
+
−
mRNA along with ribosomes per coding region will be worked out for both the lysozyme and DNase models independently. In addition to this, both
+
−
enzymatic models use the well known duplication time of <i>E. coli</i> - 17 minutes, at which point both the mRNA and protein is assumed to split among the two cells equally,.
+
−
</p>
+
−
<p id="pp">
+
−
<span class="equation">$T_{(mRNA)} = \frac{T_{(mRNA) \frac{1}{2}}}{ln(2)} = \frac{300\text{s}}{ln(2)} = 430\text{s (2sf)}$<span class="equation_ref">(Hyperphysics.phy-astr.gsu.edu, 2016)</span></span>
+
 
<span class="equation_key">
 
<span class="equation_key">
−
$T_{(mRNA)}$: Lifetime of mRNA [$\text{s}$]<br />
+
$C$: Frequency of checksum<br />
−
$T_{(mRNA) \frac{1}{2}}$: Half life of mRNA [$\text{s}$]
+
$n$: The integer address of base pair<br />
 +
$bp$: Amount of base pairs (5 times the number of BabbleBricks)<br />
 +
$bp_n$: The value of the $n^{th}$ base pair
 
</span>
 
</span>
 
</p>
 
</p>
−
<h5>Lysozyme Model</h5>
+
<div class="col-xs-12" style="width:100%;position:relative;margin:auto;padding:0;">
−
<p id="pp">
+
<div class="graph_box col-xs-12">
−
In the case of the “Lysozyme c” kill switch the mechanisms beyond producing mRNA need to be modelled separately from the DNase model. There are several
+
<img src="https://static.igem.org/mediawiki/2016/4/48/T--Exeter--Collaboration_Edinb_1.png">
−
assumptions that will be made, the first of these is that enzymatic reactions can be modelled by Michaelis-Menten kinetics. Secondly, the temperature
+
<span>Fig. 1. The frequency of all checksums in a babbleBlock system containing two BabbleBricks.</span>
−
of the constants taken imply that this model is running in the range of $37-40^o\text{C}$ at an optimal pH for <i>E. coli</i> growth. The model will assume that when
+
</div>
−
<i>E. coli</i> splits, the contents of the cell and damage of the cell wall is shared equally among the two resulting <i>E. coli</i>. Lastly, it will assume that lysozyme does not degrade
+
<div class="graph_box col-xs-12">
−
throughout the simulation.
+
<img src="https://static.igem.org/mediawiki/2016/0/0b/T--Exeter--Collaboration_Edinb_2.png">
−
</p>
+
<span>Fig. 2. The frequency of all checksums in a babbleBlock system containing three BabbleBricks.</span>
 +
</div>
 +
</div>
 
<p id="pp">
 
<p id="pp">
−
The plasmid used to produce lysozyme has a PCR with a length of $507\text{bp}$ with the promoter, RBS and terminator totalling a further $164\text{bp}$. Therefore the
+
Currently a checksum utilizes only a small percentage of the values that can be stored.  
−
production rates of mRNA and lysozyme can be calculated using translation and transcription rates of $V_{translation} = 8.4\text{aas}^{-1}$  
+
A BabbleBrick contains 5 base 4 digits meaning that 4$^{\text{5}B}$ unique bits of
−
(Siwiak and Zielenkiewicz, 2013) and $V_{transcription} = 40\text{bps}^{-1}$ (García and Molineux, 1995) respectively. The translation time is for <i>E. coli</i> at $37^o\text{C}$, other values
+
information share one of 15$B$ checksums where $B$ is the amount of BabbleBricks in one
−
have been taken at $40^o\text{C}$ as this was the closest temperature that could be found. All reaction rates have been rounded to the nearest second as this
+
BabbleBlock. This data has been plotted for BabbleBlocks containing 2 and 3 BabbleBricks
−
reduces calculation times.
+
in Fig.1 and Fig.2 respectively. Assuming that between the time of writing and reading
 +
any number of mutations can occur, the maximum probability of a mutation event resulting
 +
in the same checksum can be calculated by comparing the frequency of one checksum to the  
 +
total frequency of unique bits of information.
 
</p>
 
</p>
 
<p id="pp">
 
<p id="pp">
−
<span class="equation">$t_{lysozyme} = \frac{L_{protein}}{V_{translation}} = \frac{\frac{507\text{bp}}{3}}{8.4\text{aas}^{-1}} = 20\text{s (2sf)}$</span><br />
+
<span class="equation">$P_C = \big(\frac{C_{max}}{F}) \approx \big(\frac{1.2 \times 10^5}{4^{10}}) = 11$% in a 2 BabbleBrick system</span><br />
−
<span class="equation">$t_{mRNA} = \frac{L_{plasmid}}{V_{transcription}} = \frac{507\text{bp} + 164\text{bp}}{40\text{bps}^{-1}} = 17\text{s (2sf)}$</span>
+
<span class="equation">$\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\approx \big(\frac{10^8}{4^{15}}) = 9$% in a 3 BabbleBrick system</span>
 
<span class="equation_key">
 
<span class="equation_key">
−
$t_{lysozyme}$: Time to produce one lysozyme protein [$\text{s}$]<br />
+
$P_C$: Maximum probability of the same checksum occuring after any number of mutations<br />
−
$t_{mRNA}$: Time to produce one mRNA [$\text{s}$]<br />
+
$C_{max}$: Frequency of most common checksum<br />
−
$L_{plasmid}\text{, }L_{protein}$: Length of plasmid and protein coding region [$\text{bp}$]
+
$F$: Frequency of possible unique bits of information
 
</span>
 
</span>
 
</p>
 
</p>
 
<p id="pp">
 
<p id="pp">
−
To supplement the production of lysozyme, the program will implement the affects of multiple ribosomes on the coding site of lysozyme. For <i>E. coli</i>
+
Therefore, it can be predicted that for an average sentence containing 9 words the maximum
−
it has been found there are 3.46 codons per $100\text{bp}$ (Siwiak and Zielenkiewicz, 2013). The PCR used for lysozyme production has a length of $507\text{bp}$, meaning
+
probability of the same checksum occurring will be of the magnitude of 1%. The probability
−
there are approximately 5.8 codons which will be rounded down to 5 as not to overproduce lysozyme.  
+
should decrease marginally when adding BabbleBricks due to the slightly increased range of  
 +
checksums that become available. This value can be optimized by altering the method of the
 +
checksum to utilize a greater range of values and to spread out the frequency more evenly as  
 +
to reduce the maximum probability of the same checksum occurring.
 
</p>
 
</p>
 
<p id="pp">
 
<p id="pp">
−
“Lysozyme c” is an enzyme that hydrolyses bonds holding together peptidoglycan in the cell wall, this is explained in detail on the <a href="https://2016.igem.org/Team:Exeter/Project">project page</a>. The  
+
Currently one BabbleBlock has 4 BabbleBricks dedicated to storing the checksum, giving a maximum
−
next task of the lysozyme model is to connect the amount of protein at each time to the degradation of the <i>E. coli</i> cell wall, to do this  
+
10$^4$ possible values. The first step in determining a ‘CheckMethod’ is to ensure that all checksums
−
Michaelis-Menten kinetics were applied, which gives the reaction rate of one enzyme (Berg et al., 2002).
+
for a suitable amount of BabbleBricks can be stored without going over 10$^4$. It is also important
 +
to not use operators that will result in negative numbers or decimals, therefore limiting the  
 +
possible checksum values to integers up to but not including 10$^4$, this rules out operators such
 +
as subtract and divide. For this example, a suitable number of words in a sentence and therefore
 +
BabbleBricks in a BabbleBlock shall be 20. All simulations will be carried out on 3 BabbleBrick
 +
systems due to computing limitations.
 
</p>
 
</p>
 
<p id="pp">
 
<p id="pp">
−
<span class="equation">$k_{(cat)} = \frac{[S]k_{(cat)max}}{[S] + K_M}$</span>
+
Checksums are non-directional, for example a BabbleBrick of bases [2,2,2,2,2] would have the
−
<span class="equation_key">
+
same checksum as [2,1,3,2,2].  To alter this a checkmethod will incorporate the position
−
$k_{(cat)}$: Reaction rate of one lysozyme [$\text{s}^{-1}$]<br />
+
of the base in to the calculation. At each point the digit is multiplied by its position
−
$k_{(cat)max}$: Maximum reaction rate of one lysozyme [$\text{s}^{-1}$]<br />
+
in the BabbleBlock, where the first BabbleBrick has digit positions 1 to 5 and the last
−
$[S]$: Substrate concentration [$\text{M}$]<br />
+
BabbleBrick (20$^{th}$) has positions 96 to 100. A scaler $\alpha$ has been included to
−
$K_M$: Michaelis constant [$\text{M}$]
+
increase the range of results. To ensure multiplications don’t result in a null result
−
</span>
+
the value of each base had a value of 1 added to it. The first checkemethod of one
−
</p>
+
BabbleBlock can be defined as:
−
<p id="pp">
+
−
To use this model two constants are required, the Michaelis constant ($K_M$), the concentration of the substrate when the reaction rate is exactly one half of the maximum reaction rate.
+
−
The average reaction rate assumed to be $k_{(cat)avg} = (k_{(cat)max}/2$). The logarithms of both of these
+
−
values has been calculated at $40^oC$ to be $-log(K_M) = 5.18 \pm 0.3$M and $-log(k_{cat}^{obs}) = 0.15 \pm 0.005$s$^{-1}$ (Banerjee et al., 1975). Giving values of:
+
−
</p>
+
−
<p id="pp">
+
−
<span class="equation">$K_M = 5.6\text{mM}$ (2sf)</span><br />
+
−
<span class="equation">$k_{(cat)avg} = \frac{k_{(cat)max}}{2} \approx \frac{k_{(cat)}^{obs}}{2} = \frac{0.86\text{s}^{-1}}{2} = 0.43\text{s}^{-1}$ (2sf)</span>
+
−
</p>
+
−
<p id="pp">
+
−
Lastly, the initial concentration of the substrate peptidoglycan is calculated. An assumption is made that determines that all the peptidoglycan in  
+
−
<i>E. coli</i> is spread out over the entire volume of the cell. Peptidoglycan or murein amount has been calculated to be approximately $3.5\text{x}10^6$ molecules
+
−
per cell in a strain of <i>E. coli</i> (Vollmer and Höltje, 2004). Using an approximate volume of <i>E. coli</i> of $0.7 \mu \text{m}^3$, the concentration of peptidoglycan is:
+
 
</p>
 
</p>
 
<p id="pp">
 
<p id="pp">
−
<span class="equation">$[Pep]_{int} = \frac{\frac{N_{pep}}{N_A}}{V_{E. coli}} = \frac{\frac{3.5\text{x}10^6}{6.02\text{x}10^{23}}}{0.7 \mu \text{m}^3} = 8.3\text{mM}$ (2sf)</span>
+
<span class="equation">$M_1 = \sum_{n=1}^{bp}(bp_n + 1) . \alpha . bp$</span>
 
<span class="equation_key">
 
<span class="equation_key">
−
$[Pep]_{int}$: Initial concentration of peptidoglycan [$\text{mM}$]<br />
+
$M_1$: Frequency of CheckMethod 1<br />
−
$N_{pep}$: Amount of peptidoglycan [molecules]<br />
+
$\alpha$: Scaler ($\alpha = 5$ in this example)
−
$N_A$: Avogadro's constant [molecules/mole]<br />
+
−
$V_{\textit{E. coli}}$: Volume of <i>E. coli</i> [$\mu\text{m}^3$]
+
 
</span>
 
</span>
 
</p>
 
</p>
−
<p id="pp">
 
−
This calculation gives a value in the same order of magnitude as the Michaelis constant, which represents the concentration of substrate when the
 
−
reaction rate is at half of its maximum value.
 
−
</p>
 
−
<h5>Results</h5>
 
 
<div class="col-xs-12" style="width:100%;position:relative;margin:auto;padding:0;">
 
<div class="col-xs-12" style="width:100%;position:relative;margin:auto;padding:0;">
−
<div class="graph_box_single col-xs-12">
+
<div class="graph_box col-xs-12">
−
<img src="https://static.igem.org/mediawiki/2016/2/24/T--Exeter--Modelling_Enzyme_Graph3.png">
+
<img src="https://static.igem.org/mediawiki/2016/4/4c/T--Exeter--Collaboration_Edinb_3.png">
−
<span>Fig. 1. Using Michaelis-Menten kinetics the reaction rate of each lysozyme enzyme has
+
<span>Fig. 3. The frequency of checkmethod 1 for all possible bits of information in a babbleBlock system containing two BabbleBricks.</span>
−
been plotted for each peptidoglycan substrate concentration. The
+
</div>
−
average reaction rate of $0.43\text{s}^{-1}$ occurs when the concentration is equal to $K_M = 0.0056\text{M}$.  
+
<div class="graph_box col-xs-12">
−
The maximum or initial concentration $[Pep]_{int} = 0.0083\text{M}$ of the substrate causes a reaction rate of $0.51\text{s}^{-1}$.</span>
+
<img src="https://static.igem.org/mediawiki/2016/d/d7/T--Exeter--Collaboration_Edinb_4.png">
 +
<span>Fig. 4. The frequency of checkmethod 1 for all possible bits of information in a babbleBlock system containing three BabbleBricks.</span>
 
</div>
 
</div>
 
</div>
 
</div>
 
<p id="pp">
 
<p id="pp">
−
Enzyme reaction rates have been modelled by the Michaelis-Menten kinetics model in Fig. 1, therefore the reaction
+
This method results in Fig.3 and Fig.4 for a 2 and 3 BabbleBlock system respectively,  
−
rate decreases as the substrate concentration decreases. This graph shows that the reaction
+
which shows a large improvement over the original checksum method. The maximum frequency
−
rate will be greatest at the beginning of the simulation and approach zero when the cell wall is most damaged.
+
of a single checksum has been significantly decreased whichwill lower the probability of
 +
a flase positive occuring; this is largely due to the large range of results available to
 +
the method. However, there is still room for improvement as the shaded area of the graph
 +
indicates that on a smaller scale the frequency of checkmethod 1 varies between high and low
 +
values. Eliminating this fluctuation would allow for the data to be spread out more evenly.
 +
To improve this
 +
method a second layer of multiplication will be implamented, each digit will
 +
now be multiplied by a constant depending on its relative position in the BabbleBrick.
 +
</p>
 +
<p id="pp">
 +
<span class="equation">$M_2 = \sum_{p=1}^B \sum_{q=1}^{5}(bp_{(5B_p + q)} + 1) . q . bp$</span><br />
 +
<span class="equation" style="font-size:60%;">Or using the remainder modulo '%'</span><br />
 +
<span class="equation">$M_2 = \sum_{n=1}^{bp} (bp_n + 1) . ((bp \text{ % } 5) + 1) . bp$</span>
 +
<span class="equation_key">
 +
$M_2$: Frequency of CheckMethod 2<br />
 +
$B$: Number of BabbleBricks in the BabbleBlock<br />
 +
$p$: Local integer address of BabbleBrick<br />
 +
$q$: Local integer address of base pair in BabbleBrick<br />
 +
$B_p$: The $p^{th}$ Babblebrick in the BabbleBlock
 +
</span>
 
</p>
 
</p>
 
<div class="col-xs-12" style="width:100%;position:relative;margin:auto;padding:0;">
 
<div class="col-xs-12" style="width:100%;position:relative;margin:auto;padding:0;">
 
<div class="graph_box col-xs-12">
 
<div class="graph_box col-xs-12">
−
<img src="https://static.igem.org/mediawiki/2016/8/84/T--Exeter--Modelling_Enzyme_Graph1.png">
+
<img src="https://static.igem.org/mediawiki/2016/6/6f/T--Exeter--Collaboration_Edinb_5.png">
−
<span>Fig. 2. The percentage of peptidoglycan compared to the original concentration plotted against time.</span>
+
<span>Fig. 5. The frequency of checkmethod 2 for all possible bits of information in a babbleBlock system containing two BabbleBricks.</span>
 
</div>
 
</div>
 
<div class="graph_box col-xs-12">
 
<div class="graph_box col-xs-12">
−
<img src="https://static.igem.org/mediawiki/2016/a/a1/T--Exeter--Modelling_Enzyme_Graph2.png">
+
<img src="https://static.igem.org/mediawiki/2016/0/06/T--Exeter--Collaboration_Edinb_6.png">
−
<span>Fig. 3. Plots a smaller range of times as Fig. 2. To show the rapid decrease in peptidoglycan concentration.</span>
+
<span>Fig. 6. The frequency of checkmethod 2 for all possible bits of information in a babbleBlock system containing three BabbleBricks.</span>
 
</div>
 
</div>
 
</div>
 
</div>
 
<p id="pp">
 
<p id="pp">
−
The model predicted the complete degradation of the cell wall to be within the first generation
+
<span class="equation">$P_{M_2} = \big(\frac{M_{2\:max}}{F}) \approx \big(\frac{6 \times 10^3}{4^{10}}) = 0.6$% in a 2 BabbleBrick system ($11$% for checksum)</span><br />
−
of <i>E. coli</i>, Fig. 2. The reaction rate is slow at first due to the cell having no initial lysozyme,
+
<span class="equation">$\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\approx \big(\frac{3 \times 10^6}{4^{15}}) = 0.3$% in a 3 BabbleBrick system ($9$% for checksum)</span>
−
this slowly increases, until the low concentration of the substrate casues
+
<span class="equation_key">
−
the reaction rate of lysozyme to slow considerably. The peptidoglycan concentration in the cell is
+
$P_{M_2}$: Maximum probability of the same checkmethod 2 value occuring after any number of mutations<br />
−
negligible until 17 minutes at which point the <i>E. coli</i> splits sharing the cell wall damage equally
+
$M_{2\:max}$: Frequency of most common checkmethod 2 value<br />
−
between the two child cells, hence cell damage drops from almost 100% to 50%. The immediate concern
+
$F$: Frequency of possible unique bits of information
−
is that in this model cell death would occur far before it is able to duplicate, meaning that
+
</span>
−
assuming no mutations the cell would terminate before 17 minutes.
+
 
</p>
 
</p>
 
<p id="pp">
 
<p id="pp">
−
The cell death threshold of peptidoglycan concentration in the cell wall is not well defined from
+
This has been plotted for a 2 and 3 BabbleBlock system in Fig.5 and Fig.6 respectively.
−
research. Fig. 3 demonstrates that any threshold that is chosen is likely to fall in between 2  
+
When comparing checksum to checkcethod 2 the frequency peak is approximately 20 to 30
−
and 5 minutes of the simulation which is well before the reproduction rate of <i>E. coli</i> of 17
+
times smaller in both cases whilst utilizing more values. In Fig.5 and Fig.6 the largest
−
minutes. Therefore it is a reasonable assumption that given no mutations were to occur that the  
+
improvement using the second iteration of the checkmethod is the utilization of every
−
cell would be terminated before the <i>E. coli</i> could reproduce.
+
integer value, checkmethod 1 appears shaded as the frequency varies frequently. The last
 +
step is to test checkmethod 2 when used in a babbleBlock containing 20 BabbleBricks; the
 +
largest value possible assuming a BabbleBlock containing the value ‘3’ in each digit will
 +
grant a value of 60600 which falls out of the current limit of 10$^4$ values. Therefore,
 +
it is recommended that one more BabbleBrick is added to the end of the BabbleBlock in order
 +
to store 10$^5$ values.  
 
</p>
 
</p>
−
<h5>References</h5>
+
<p id="pp">
−
<ol style="font-size:100%;">
+
To improve this method  further more complex multiplications could be added, it would be
−
<li>Nordström, K. and Dasgupta, S. (2006). Copy-number control of the <i>Escherichia coli</i> chromosome: a plasmidologist's view. EMBO Rep, [online] 7(5), pp.484-489. Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1479556/ [Accessed 6 Sep. 2016].</li>
+
a decision based on optimising efficiency of calculations and minimising false positives.
−
<li>Elowitz, M., Surette, M., Wolf, P., Stock, J. and Leibler, S. (1998) ‘Protein mobility in the cytoplasm of Escherichia coli’, Journal of bacteriology., 181(1), pp. 197–203 [Accessed 6 Sep. 2016].</li>
+
In a 2 and 3 BabbleBrick system the probability of a false positives occurring was reduced by
−
<li>Thermofisher.com. (2016). Macromolecular Components of E. coli and HeLa Cells | Thermo Fisher Scientific. [online] Available at: https://www.thermofisher.com/uk/en/home/references/ambion-tech-support/rna-tools-and-calculators/macromolecular-components-of-e.html# [Accessed 6 Sep. 2016].</li>
+
approximately 20 and 30 times respectively, although the numbers are too large to compute,  
−
<li>Bernstein, J., Khodursky, A., Lin, P., Lin-Chao, S. and Cohen, S. (2002). Global analysis of mRNA decay and abundance in Escherichia coli at single-gene resolution using two-color fluorescent DNA microarrays. Proceedings of the National Academy of Sciences, [online] 99(15), pp.9697-9702. Available at: http://www.pnas.org/content/99/15/9697.long [Accessed 6 Sep. 2016].</li>
+
this new method has the possibility of lowering the maximum false positive error of the previously
−
<li>Berg, J., Tymoczko, J., Stryer, L. and Stryer, L. (2002). Biochemistry. New York: W.H. Freeman, pp.Section 8.4 - Equation (23).</li>
+
used checksum by one or more orders of magnitude.
−
<li>Banerjee, S., Holler, E., Hess, G. and Rupley, J. (1975). Reaction of N-acetylglucosamine oligosaccharides with lysozyme. Temperature, pH, and solvent deuterium isotope effects; equilbrium, steady state, and pre-steady state measurements*. Journal of Biological Chemistry, [online] 250(11), pp.4357, Figure 2 and 4359, Table I. Available at: http://www.jbc.org/content/250/11/4355.long [Accessed 7 Sep. 2016].</li>
+
If continued further, research should also be done in to the reconstruction of data after it has been lost.
−
<li>Vollmer, W. and Höltje, J. (2004). The Architecture of the Murein (Peptidoglycan) in Gram-Negative Bacteria: Vertical Scaffold or Horizontal Layer(s)?. Journal of Bacteriology, [online] 186(18), p.5980. Available at: http://jb.asm.org/content/186/18/5978 [Accessed 7 Sep. 2016].</li>
+
</p>
−
<li>Siwiak, M. and Zielenkiewicz, P. (2013) ‘Transimulation - protein Biosynthesis web service’, PLoS ONE, 8(9), p. e73943. doi: 10.1371/journal.pone.0073943. [Accessed 7 Sep. 2016]</li>
+
<div>
−
<li>García, L. and Molineux, I. (1995) ‘Rate of translocation of bacteriophage T7 DNA across the membranes of Escherichia coli’, Journal of bacteriology., 177(14), pp. 4066–76.[Accessed 7 Sep. 2016]</li>
+
<a id="Section_link" href="#section_3" style="display:block;margin:20px auto 0 auto;width:14px;"><span style="color:#47BCC2;font-size: 25px;" class="glyphicon glyphicon-menu-down" aria-hidden="true"></span></a>
−
<li>Siwiak, M. and Zielenkiewicz, P. (2013). Transimulation - Protein Biosynthesis Web Service. PLoS ONE, [online] 8(9), p.3, left column, second paragraph. Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3764131/ [Accessed 7 Sep. 2016].</li>
+
</div>
−
<li>Hyperphysics.phy-astr.gsu.edu. (2016). Mean Lifetime for Particle Decay. [online] Available at: http://hyperphysics.phy-astr.gsu.edu/hbase/nuclear/meanlif.html [Accessed 10 Sep. 2016].</li>
+
−
</ol>
+
  
  
−
+
 
−
+
 
−
<div>
+
</div>
 +
 
 +
<div class="col-xs-12 div_content">
 +
<div id="section_3" class="link_fix"></div>
 +
<div id="contentTitle">
 +
Parts:Glasgow
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</div>
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                <p id="pp">We collaborated with Glasgow iGEM 2016 to test the efficiency of the T7 Promoter we were using to construct the KillerRed, KillerOrange and Lysozyme kill switches. We new that it was leaky and we speculated that it was reducing the efficiency of our project but we needed proof that the leakiness of the promoter could affect our project. In return they gave us a DH5α.Z1 strain in the hopes it would improve the efficiency of our promoter. After subsequent testing we were unable to express our KillerRed and KillerOrange proteins in this strain. This is the report they sent us for the test of the T7 promoter:</p>
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                <h5>Exeter and Glasgow iGEM 2016 Collaboration: <br \>
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                <br>
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                KillerRed and KillerOrange Promoter Efficiency Experiment
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                </h5>
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                <h6>Methods</h6>
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                <p id="pp">First, we transformed the plasmids for testing promoter efficiency into the E. coli strain DH5α.Z1:</p>
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                <ul>
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                    <li>J04450 (RFP with a lac-repressible promoter) in pSB1C3</li>
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                    <li>lac-repressible promoter + KillerRed in pSB1C3</li>
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                    <li>lac-repressible promoter + KillerOrange in pSB1C3</li>
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                </ul>
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                <p id="pp">Next, we set up 5ml LB broth overnight cultures in boiling tubes with loose caps at 37°C shaking at 225rpm of:</p>
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                <ol>
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                    <li>DH5α, no plasmid</li>
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                    <li>DH5α, J04450-pSB1C3</li>
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                    <li>DH5α, KillerRed-pSB1C3</li>
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                    <li>DH5α, KillerOrange-pSB1C3</li>
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                    <li>DH5α.Z1, no plasmid</li>
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                    <li>DH5α.Z1, J04450-pSB1C3</li>
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                    <li>DH5α.Z1, KillerRed-pSB1C3</li>
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                    <li>DH5α.Z1, KillerOrange-pSB1C3</li>
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                </ol>
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                <p id="pp">We set up each of these both with 1mM IPTG and without, so 16 overnights in total, with 25μg/ml chloramphenicol for the strains with plasmids.</p>
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                <p id="pp">The next day, we spun down 500μl of each overnight in 1.5ml eppendorfs, resuspended in 1ml PBS buffer (PBS gives less background fluorescence than LB broth), and transferred to cuvettes for OD600 measurements. For the fluorescence measurements, we used a Typhoon FLA 9500 with the samples in a 96-well plate. Each of the 16 samples were pipetted into 3 wells with 300μl each. The settings on the Typhoon were: 1) excitation laser at 532nm and emission filter above 575nm (so would detect any wavelength above 575nm) and 2) excitation laser at 473nm and emission filter above 575nm. Both were across the whole 96-well plate, but the second lower excitation wavelength was included in case the first was too high to excite KillerOrange.</p>
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                <h6>Results</h6>
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                <p id="pp">For each of the wells, a fluorescence value in arbitrary units (au) was calculated using ImageQuant software, where we set the 3 wells of PBS only blanks as 0% fluorescence, and the well with the highest value as 100% to convert au into percentage. Next, we normalised for any natural fluorescence in the cells by subtracting the average of the “cells only” wells (DH5α, and Dh5α.Z1, both with and without IPTG) from all wells, and then corrected for the difference in OD600 values between samples by dividing the normalised fluorescence measurements by the OD600 values. The table below shows the average across the 3 wells of each of the 16 samples.</p>
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<style>
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table{
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padding-top:20px;
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padding-right:33%px;
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padding-left:33%;
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font-size:150%;
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text-align:center;
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border-collapse: initial;
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}
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td:first-child {
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text-align:left;
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font-weight:bold;
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}
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th{
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padding-left:5px;
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padding-right:5px;
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}
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</style>
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<table>
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<tr>
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<th>Strain + Plasmid</th>
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<th>No IPTG</th>
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<th>1mM IPTG</th>
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</tr>
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<tr>
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<td>DH5α</td>
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<td>0.016009</td>
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<td>-0.00115</td>
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</tr>
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<tr>
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<td>DH5α J04450</td>
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<td>99.84583</td>
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<td>90.32377</td>
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</tr>
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<tr>
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<td>DH5α KillerRed</td>
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<td>0.003236</td>
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<td>-0.00109</td>
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</tr>
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<tr>
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<td>DH5α KillerOrange</td>
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<td>-0.02212</td>
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<td>-0.02343</td>
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</tr>
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<tr>
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<td>DH5α.Z1</td>
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<td>-0.02934</td>
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<td>0.015912</td>
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</tr>
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<tr>
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<td>DH5α.Z1 J04450</td>
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<td>14.20878</td>
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<td>80.45877</td>
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</tr>
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<tr>
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<td>DH5α.Z1 KillerRed</td>
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<td>-0.03287</td>
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<td>-0.00517</td>
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</tr>
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<tr>
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<td>DH5α.Z1 KillerOrange</td>
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<td>-0.01994</td>
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<td>-0.02095</td>
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</tr>
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</table>
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                <p id="pp">Graph of these averages. The error bars are standard deviation but are very small because the 3 replicates for each sample are technical replicates, so do not show the variation that would be seen with biological replicates (3 different colonies for each of the 16 samples).</p>
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<br>
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<br>
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                <div class="col-xs-12" style="padding:0;margin:0;">
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                    <img src="https://static.igem.org/mediawiki/2016/a/a1/T--Exeter--collab-glasgow1_png.png" style="max-width:100%;margin:auto;display:block;">
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<br>
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<br>
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                </div>
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                <p id="pp">Fluorescence scan image from the Typhoon with labels for which samples are in each well.</p>
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<br>
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<br>
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                <div class="col-xs-12" style="padding:0;margin:0;">
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                    <img src="https://static.igem.org/mediawiki/2016/2/2d/T--Exeter--collab-glasgow2_png.png" style="max-width:100%;margin:auto;display:block;">
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<br>
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<br>
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                </div>
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                <p id="pp">These data indicate that there is no difference in fluorescence between either KillerRed or KillerOrange and the cells only control either with or without induction with IPTG. There could be several reasons for this, including the light was not intense enough to excite the fluorescent proteins, however no fluorescence from this type of the test with a laser for excitation would be unlikely.  It is also possible that no protein is being produced, which could be due to insufficient IPTG. However, the RFP in J04450 under the control of the lac-repressible promoter R0010 clearly shows that in the DH5α.Z1 strain, there is less fluorescence without IPTG, than with IPTG. This is not a perfect control for the concentration of IPTG used unless KillerRed and KillerOrange also have the R0010 promoter. Interestingly, in the DH5α strain, there is no significant difference between RFP fluorescence with or without IPTG – this is due to DH5α not having a functional copy of LacI, the lac repressor, therefore lac-repressible promoters are not “OFF”, so cannot be switched “ON” by IPTG induction. </p>
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                <h6>Sequencing</h6>
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                <p id="pp">Another reason there may not be any KillerRed or KillerOrange protein produced, is mutations in the promoter. This was something we encountered when attempting to clone a promoter in front of the toxin from the toxin-antitoxin system we were working with. If a protein is toxic to produce, any cell which is producing less or no protein will grow faster than a cell which is producing the toxic protein. This means a mutated, non-functional promoter will have a proliferative advantage during transformation. So, as we were sending our BioBricks for registry for submission, we decided to sequence the minipreps of KillerRed and KillerOrange as well with the registry standard pSB1C3 sequencing primer VF2, to check for any mutations. The results are shown below in screenshots of a plasmid editor software called ApE.</p>
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<br>
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<br>
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<div class="col-xs-12" style="padding:0;margin:0;">
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                    <img src="https://static.igem.org/mediawiki/2016/4/44/T--Exeter--collab-glasgow3_png.png" style="max-width:100%;margin:auto;display:block;">
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<br>
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<br>
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                </div>
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<div class="col-xs-12" style="padding:0;margin:0;">
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                    <img src="https://static.igem.org/mediawiki/2016/5/53/T--Exeter--collab-glasgow4_png.png" style="max-width:100%;margin:auto;display:block;">
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<br>
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<br>
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                </div>
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                <p id="pp">Both plasmids had the BioBrick prefix, and the correct sequence for both KillerRed and KillerOrange open reading frame, according to the papers cited on the Exeter 2016 iGEM wiki. The sequence between the prefix and the ATG start codon, we checked against lac-repressible promoters in the iGEM registry. We found a match to R0184, which is a T7 lac-repressible promoter. T7 promoters require T7 polymerase to be transcribed, as they are not recognised by E. coli polymerases. This results confirms the result of the fluorescence measurements. No KillerRed or KillerOrange protein was observed by fluorescence, as neither gene was being transcribed by either DH5α or DH5α.Z1 as neither strain produces the required T7 polymerase. A protein overexpression E. coli strain such as BL21<DE3> which has the T7 polymerase gene inserted into its genome is designed to use T7 promoters would have been able to express these KillerRed and KillerOrange constructs.</p>
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                <p id="pp">Sequencing result for KillerRed:</p>
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                <p id="pp"> ATAAAATAGG CGTATCACGA GGCAGAATTT CAGATAAAAA AAATCCTTAG CTTTCGCTAA GGATGATTTC TGGAATTCGC GGCCGCTTCT AGAGTACTTA ATACGACTCA CTATAGGGGA ATTGTGAGCG GATAACAATT CCCCTCAAGA AATAATTTTG TTTAACTTTA AACCTAAAGA GGAGAAAAAT GGGCAGTGAA GGTGGTCCTG CGCTTTTCCA GTCAGACATG ACCTTCAAAA TTTTCATTGA CGGTGAAGTT AATGGACAGA AATTTACGAT CGTAGCCGAT GGCTCAAGCA AATTCCCACA TGGGGACTTC AATGTCCACG CCGTGTGCGA AACAGGCAAA TTACCCATGA GCTGGAAGCC GATTTGTCAT TTGATTCAGT ACGGGGAGCC TTTTTTCGCT CGTTACCCAG ATGGAATTTC TCACTTTGCC CAGGAGTGTT TTCCCGAAGG ACTGTCTATC GATCGTACCG TGCGCTTTGA AAACGACGGT ACTATGACCT CGCATCATAC CTATGAATTA GACGATACAT GCGTGGTAAG TCGTATCACG GTAAACTGCG ACGGTTTTCA ACCTGATGGC CCAATCATGC GTGACCAGTT GGTCGATATC CTGCCTAATG AAACCCATAT GTTCCCGCAT GGGCCAAATG CGGTCCGCCA ATTAGCATTC ATCGGGTTCA CGACTGCGGA CGGCGGACTT ATGATGGGGC ATTTTGACTC TAAGATGACC TTTAACGGTT CGCGCGCGAT TGAAATTCCT GGGCCGCACT TTGTGACGAT TATTACAAAG CAAATGCGTG ATACATCTGA CAAACGCGAC CACGTCTGTC AACGTGAAGT CGCTTACGCA CATTCAGTGC CTCGCATTAC CAGTGCGATC GGTTCAGATG AGGACTGATA ACTGCCCAGG CATCAAATAA AACGAAAGGG TCAGTCGAAA ACT
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</p>
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                <p id="pp">Sequencing result for KillerOrange:</p>
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                <p id="pp"> TATAAAATAG GCGTATCACG AGGCAGAATT TCAGATAAAA AAAATCCTTA GCTTTCGCTA AGGATGATTT CTGGAATTCG CGGCCGCTTC TAGAGTACTT AATACGACTC ACTATAGGGG AATTGTGAGC GGATAACAAT TCCCCTCAAG AAATAATTTT GTTTAACTTT AAACCTAAAG AGGAGAAAAA TGATGGAATG CGGCCCGGCG CTGTTTCAGA GCGATATGAC CTTTAAAATT TTTATTGATG GCGAAGTGAA CGGCCAGAAA TTTACCATTG TGGCGGATGG CAGCAGCAAA TTTCCGCATG GCGATTTTAA CGTGCATGCG GTGTGCGAAA CCGGCAAACT GCCGATGAGC TGGAAACCGA TTTGCCATCT GATTCAGTGG GGCGAACCGT TTTTTGCGCG CTATCCGGAT GGCATTAGCC ATTTTGCGCA GGAATGCTTT CCGGAAGGCC TGAGCATTGA TCGCACCGTG CGCTTTGAAA ACGATGGCAC CATGACCAGC CATCATACCT ATGAACTGAG CGATACCTGC GTGGTGAGCC GCATTACCGT GAACTGCGAT GGCTTTCAGC CGGATGGCCC GATTATGCGC GATCAGCTGG TGGATATTCT GCCGAGCGAA ACCCATATGT TTCCGCATGG CCCGAACGCG GTGCGCCAGC TGGCGTTTAT TGGCTTTACC ACCGCGGATG GCGGCCTGAT GATGGGCCAT CTGGATAGCA AAATGACCTT TAACGGCAGC CGCGCGATTG AAATTCCGGG CCCGCATTTT GTGACCATTA TTACCAAACA GATGCGCGAT ACCAGCGATA AACGCGATCA TGTGTGCCAG CGCGAAGTGG CGCATGCGCA TAGCGTGCCG CGCATTACCA GCGCGATTGG CAGCGATCAG GATTGATGAC TGCCCAGGCA TCAATTAAAA CGAAAGGCTC AGTCGAAAAC
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</p>
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                <h6>Conclusion:</h6>
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                <p id="pp">Glasgow iGEM did fantastic work for us, providing us with detailed analysis of the T7 promoter and suggestions for improving the efficiency of our project. Whilst their data on the DH5alpha Z1 strain is accurate and in accordance with subsequent research and advice, we have since noted there is expression of KillerRed and KillerOrange in DH5alpha in lab tests.  </p>
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Revision as of 19:26, 11 October 2016