Difference between revisions of "Team:UNOTT/Model"

 
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  <h3 class="box_header">Overview</h3>
 
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<p>When developing <i> Key. coli</i>, we found it was important to mathematically model possible situations in order to investigate the effects of different situations that we might encounter throughout different stages of development as well as during implementation. </p>
 
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<p> Software was developed to compare the fluorescence levels of the key colony with the mother colony to check if there was a high enough degree of similarity.</p>
 
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<p> This information was used by the wet lab to assist them by informing them in what to expect. This was done through the use of programming to create visual graphs and simulations, as well as development of tools to allow for comparison between fluorescence levels without needing to actually create more synthetic organisms. Another advantage is that this is far quicker than creating these results in the lab. </p>
 
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<p> <a href="https://github.com/BurgundyIsAPublicEnemy/iGEMNotts2017/blob/master/Tellurium%20-%20Python%20Models%20for%20IGEM%20NOTTS%202017">The source code for these models can be accessed from our GitHub page</a> </p>
 
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<p> The models were not perfect at first: refinement from lab results helped to optimize and correct the models.</p>
 
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  <h3 class="box_header">Modelling Aims </h3>
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<h3 class="box_header">Overview</h3>
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<li> To assist the processes within the wet lab by informing them and allowing for simulations. This would be especially useful when predicting the required fluorescence </li>
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<div class="box_content">
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<li> Test our biological systems with conditions that might not be possible to replicate in a lab environment. This allows us to future proof our methods as well as identify any vulnerabilities </li> 
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&nbsp;
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<p>In order to achieve these aims, we created a simulation for measuring fluorescence intensity when given parameters such as protein concentrations and wavelengths of lasers. </p>
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<p>When developing <i> Key. coli</i>, we found it was important to mathematically model possible situations in order to investigate the effects of different situations that we might encounter throughout different stages of development as well as during implementation. </p>
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<p><a href="https://2017.igem.org/Team:UNOTT/Modelling">Find out more about our modeling</a> </p>
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<p><a href="https://github.com/BurgundyIsAPublicEnemy/iGEMNotts2017/blob/master/LuciferA.c">This simulation can be downloaded from our GitHub page.</a> </p>
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<p>A major problem the project faced is that the comparison process of the fluorescence proteins wouldn't be possible to be investigated with all combinations as it would take too long. </p>
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<p> To answer this problem, the team attempted to model the fluorescence spectra over time expressed by each different protein. First, the type of gene expression was identified and then, the model was refined to take into account gene inhibition (whether the gene is expressed or not) and finally, applied over time to see how much expression would occur at a certain time period. The team used mathematical modelling such as Ordinary Differential Equations because they were easy to convert into programming in order to build components for the simulation.</p>
  
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<div class="box">
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<p> This information was used by the wet lab to assist them by informing them in what to expect. This was done through the use of programming to create visual graphs and simulations, as well as development of tools to allow for comparison between fluorescence levels without needing to actually create more synthetic organisms. One advantage of this was it allowed for data to be easily read and understood by the team, rather than reading a wall of numbers. Another advantage is that this is far quicker than creating these results in the lab. </p>
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  <h3 class="box_header">Software Aims</h3>
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  <div class="box_content">
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<li> To check between fluorescence levels during implementation of Key.Coli between the mother colony and the Key.Coli capsules. </li>
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<li> Develop an internal development environment to help next year's iGEM team quickly develop models as well as software.</li> 
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&nbsp;
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<p>In order to achieve these aims, we created an image comparing software as well as an internal development environment where members can easily add their own code as well as access other code made by others and other files. </p>
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<p> <a href="https://2017.igem.org/Team:UNOTT/CultureModelling">Find out more about our software </a> </p>
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<p> <a href="https://github.com/BurgundyIsAPublicEnemy/iGEMNotts2017/blob/master/LuciferA.c">This environment can be downloaded from our GitHub page</a> </p>
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<p> One limitation of models the team found out that they were too high level to accurately predict and represent all the processes that would be undertaken during the random constructions of the fluorescent proteins. This is an issue because this means the models weren't perfect to describe the real life, which however, suggests, they could undergo more refining and improving. </p>
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  <h3 class="box_header">WEEK 3</h3>
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  <div class="box_content">
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<p><span style="color: #000000;">This week we started lab work! 5 of us (biotechnologists and biochemists) were in the lab learning the techniques needed for iGEM.</span></p>
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<p><span style="color: #000000;"><span style="text-decoration: underline;">Monday</span> - Primers and enzymes were ordered for one of our two constructs today so we started on the InterLab study until they arrive! We transformed the controls and devices into DH5a competent cells that were provided for us. We set up overnight cultures of TOP10 cells for making electrocompetent cells, and we also made O/N cultures of E.coli carrying the backbone we need for our pReporter which was donated by a lab member. This is called pSTLS.&nbsp; Vikram released a teaser trailer for the game we have made (INSERT LINK HERE). Vik's computer then died</span></p>
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<p><span style="color: #000000;"><span style="text-decoration: underline;">Tuesday</span> - Vik's computer is alive again. Unfortunately our transformations only gave very small colonies on some plates, and none on others. We re-streaked those colonies that we did have. We extracted plasmid DNA from the O/N cultures of pSTLS and others for the lab to get some practice. We learnt to use the NanoDrop to quantify DNA. Outside of the lab, Georgette was busy creating our very first vlog which will be shared on the social media pages. Vik is looking at building hardware. Today we also skyped Edinburgh UG team to discuss possible collaborations</span></p>
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−
<p><span style="color: #000000;"><span style="text-decoration: underline;">Wednesday</span> - Waiting for primers to arrive. NEB sent us some enzymes and competent cells so thanks to NEB!</span></p>
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<p><span style="color: #000000;"><span style="text-decoration: underline;">Thursday</span> - Today we learnt to electroporate DH5a and TOP10 cells. We transformed them with either a plasmid containing dCas9 or our low copy backbone, pSTLS. We also set up overnight cultures for the InterLab Study.</span></p>
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−
<p><span style="color: #000000;"><span style="text-decoration: underline;">Friday</span> - DISASTER! Two of our overnight colonies didn't grow so we have to restart the overnights on Monday. Ah well, we are learning what science is like in reality. We went for a social today - Laser quest! Georgette won by far, Ellie lost miserably (-8300 points). Georgette was appointed new team leader due to Ellie's shocking performance... just kidding!</span></p>
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<p> Software was developed to compare the fluorescence levels of the key colony with the mother colony to check if there was a high enough degree of similarity. The mother colony is defined as the colony of bacteria that is securely kept within the facility and whose fluorescence acts as a verification for the key colony, which is defined as the bacteria which is taken from the mother colony and given to a person who own's a Key.Coli container. </p>
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  <h3 class="box_header">WEEK 4</h3>
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<p><span style="color: #000000;">This week we carried on with the InterLab study and waited for our primers to be delivered.</span></p>
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<p><span style="color: #000000;"><span style="text-decoration: underline;">Monday</span> - we re-set up overnight cultures for the InterLab study today.</span></p>
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<p><span style="color: #000000;"><span style="text-decoration: underline;">Tuesday</span> - all our cultures grew so we proceeded with InterLab GFP measurements... they weren't what we were expecting! Jake and Vik skyped Bristol iGEM to see whether we can help them improve the wiki design process.</span></p>
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<p><span style="color: #000000;"><span style="text-decoration: underline;">Wednesday</span> - we analysed the data from the InterLab study today and e-mailed some important people for outreach purposes.</span></p>
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<p><span style="color: #000000;"><span style="text-decoration: underline;">Thursday</span> - Jake changed the nav bar today. Chris made one of our many games.</span></p>
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<p><span style="color: #000000;"><span style="text-decoration: underline;">Friday</span> - we took team photos, ate fish and chips and OUR PRIMERS ARRIVED! This means we can properly start in the lab on Monday!!</span></p>
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<p> As a side project, the team investigated into whether our method is random and unique by investigating how many combinations we could make and whether we could accurately predict which combination will occur. </p>
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  <h3 class="box_header">WEEK 5</h3>
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<p><span style="color: #000000;">The primers arrived so we had a long day in the lab doing 46 PCR reactions! We learnt how to set up PCR and load agarose gels, and how interesting waiting around for PCR is. We got lots of interesting results.&nbsp; Our original PCRs were using 1ng template and touchdown from 70-60oC for 10 cycles followed by 25 at 65oC. These conditions were great for getting most of the components to amplify, however we had to optimise a few. For example the dCas9 and low copy backbone didn't amplify first time around, so we used a new strategy - using various amounts of template with duplicates placed at different annealing temperatures. One was placed in a touchdown setting as before but with annealing temperatures dropping from 60 to 50oC followed by 25 cycles at 65oC. The other was placed at 55oC for all cycles. We also didn't get good amplification of P1, P2, P3, P4, T1 or T2 so we repeated their PCR using touchdown from 70 to 60 as before but with 5 more cycles.<br /></span></p>
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<center><img src="https://static.igem.org/mediawiki/2017/f/fd/T--UNOTT--initial7060PCRofgRNAspromotersetc.jpeg"></center>
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<center><img src="https://static.igem.org/mediawiki/2017/5/50/T--UNOTT--initial7069prepgel.jpeg"></center>
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<p><span style="color: #000000;"> In the optimised PCR, we managed to get P1, P2, P3, P4 amplified, and T1 and T2 amplified but there were far too many bands to be certain we were amplifying the right thing. So we re-amplified T1 and T2 using PCR with touchdown 60-50oC as above. T1 was successfully amplified using this approach, however we got really strange bands for T2 so we went back to the samples from the previous PCR to see whether we could use those instead for T2. Our optimised PCR showed dCas9 was successfully amplified in all conditions - yay! The low copy backbone only amplified at 55oC using 1ng template so we repeated this PCR using those settings to generate enough DNA for later. This PCR was successful.</span></p>
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<center><img src="https://static.igem.org/mediawiki/2017/6/6a/T--UNOTT--optimisedPCRdCas9lcBB.jpeg"></center>
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<center><img src="https://static.igem.org/mediawiki/2017/f/f9/T--UNOTT--repeatedPCRreporterbackbone.jpeg"></center>
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<p><span style="color: #000000;"><span style="text-decoration: underline;">Wednesday</span> - now that we had most of the components, we could digest them. P1-5 + PE, the strong and weak RBS linked GFPs, and T2 were digested with BsaI. For the gRNA plasmid, J23119, gRNA 1-5 and gRNA0 as well as Tfdx were digested with BsaI. We digested 1ug for all of the components except the GFPs. 2ug of both backbones were digested too. The gRNA backbone was digested with SalI and AscI, whereas the low copy backbone was digested with SalI and BsteII. For the dCas9 brick, we digested dCas9 with Bsa1, T1 with BsaI and SalI, and PdCas9 with BsteII and BsaI. We PCR purified all of the digested before ligating together the bricks using T4 DNA ligase. We had problems digesting gRNA 4 as the DNA concentration dropped to 2ng/ul on two separate occasions, so the whole process was repeated for gRNA 4. 50ng of each brick was used for the brick ligations whereas 100ng of the backbones were used. 13.3ng of T1 was used and 7.2ng of PdCas9. Ligations were carried out for 2.5 hours.&lt;/li&gt;</span></p>
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<p><span style="color: #000000;"><span style="text-decoration: underline;">Thursday</span> - today we checked whether the ligations had worked. We tried to amplify using our brick forward and reverse primers for all bricks, however the results showed amplification of products much smaller than expected. We suspected a few problems so took a while to look through the primers using SnapGene and identified a few tweaks that could be made to the gRNA brick primers, so we ordered new primers for that. We were very confused by the promoter-GFP-terminator bricks as the products should be over 1kb whereas they were showing up at under 500bp. We decided to troubleshoot the PCR by using the brick forward primer and the sGFP reverse primer on P1, P2, P3, PE on the corresponding ligations. Today we also decided to ligate the sgRNAs with their promoter, terminator and the backbone in a 4 fragment ligation using T4 ligase overnight at room temperature.&lt;/li&gt;</span></p>
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<p><span style="color: #000000;"><span style="text-decoration: underline;">Friday</span> - another late finish today! Today we checked whether the PCRs worked with the brick forward primers and the sGFP reverse primer for P2, P3, P4 and PE "bricks" using our ligations as templates. They did! So that gave us hope that the promoter and sGFP are attached properly. We realised there was a problem with T2 amplification so we decided to switch to using T3 instead. So we created primer dimers of T3 and amplified it before PCR purification, ready for digestion on Monday. We noticed another problem with P1 which meant that it would not amplify using the primers we currently had, we made larger quantities of PCR of P2-5 + PE bricks using the brick forward primer and either strong and weakGFP reverse primers. Unfortunately the gel showed that the wGFP bricks did not amplify but the sGFP bricks did so we are a bit confused. We cut out the bands which had worked ready for gel extraction on Monday. Today we also dialysed our ligations which had been running overnight, and transformed these into TOP10 by electroporation and plated them on chloramphenicol plates to select for transformants. We also checked the transformation plates from yesterday which hopefully had the low copy backbone plus dCas9, PdCas9 and T1 in - at the end of Friday there were 2 colonies from being left in 30 degrees for around 24 hours - good news!</span></p>
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  <h3 class="box_header">WEEK 6</h3>
 
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<center><img src="https://static.igem.org/mediawiki/2017/1/18/T--UNOTT--Q5vsKOD.jpeg"></center>
 
 
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   <h3 class="box_header">WEEK 7</h3>
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   <h3 class="box_header">Modelling Aims </h3>
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<p> The first aim is to assist the processes within the wet lab by informing them of what spectra they can expect through the use of simulations. This would be especially useful when predicting the required fluorescence </p> 
 +
<p> The second is to test our biological systems with conditions that might not be possible to replicate in a lab environment. This allows us to future proof our methods as well as identify any vulnerabilities further down the line as well as save time and money on testing these conditions, if we were to. </p>
 +
<p>In order to achieve these aims, we decided on an end goal of writing a Simulation for measuring Fluorescence Intensity when given parameters such as protein concentrations and wavelengths of lasers. </p>
 +
<br></br>
 +
<p style="text-align: center;" > <a href="https://2017.igem.org/Team:UNOTT/Modelling">Find out more about our modeling</a> </p>
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<p style="text-align: center;" > <a href="https://github.com/BurgundyIsAPublicEnemy/iGEMNotts2017/tree/master/Models">The source code for these models can be accessed from our GitHub page</a> </p>
 
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   <h3 class="box_header">WEEK 8</h3>
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   <h3 class="box_header">Software Aims</h3>
 
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<p> The main objective of software was to check between fluorescence levels during implementation of Key.Coli between the mother colony and the Key.Coli capsules. </p>
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</div>
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<p> In order to achieve this objective, the team decided to investigate into image comparison and raw data comparison with data from the fluorescence reader. These were programmed using data from the models and a threshold value was set using data from the wet lab to ensure not just any file being compared would get verified. </p>
  
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<p> A strong emphasis was placed on effectiveness of the software and efficiency; we wanted the software to run on low end hardware but still be useful without compromises. </p>
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  <h3 class="box_header">WEEK 9</h3>
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<br> </br>
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<p style="text-align: center;" > <a href="https://2017.igem.org/Team:UNOTT/Software">Find out more about our software </a> </p>
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<p style="text-align: center;" > <a href="https://github.com/BurgundyIsAPublicEnemy/iGEMNotts2017/tree/master/Software">Our software can be downloaded from our GitHub page</a> </p>
 
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   <h3 class="box_header">WEEK 10</h3>
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   <h3 class="box_header">Parameters</h3>
 
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<p> Inputs and Outputs </p>
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   <h3 class="box_header">WEEK 11</h3>
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<center><img src="https://static.igem.org/mediawiki/2017/c/cc/T--UNOTT--colonyPCRofrandomligationtest1.jpeg"></center>
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    <th>Parameter</th>
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<center><img src="https://static.igem.org/mediawiki/2017/e/e4/T--UNOTT--colonyPCRmCas9vectors.jpeg"></center>
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    <th>Unit</th>  
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<center><img src="https://static.igem.org/mediawiki/2017/c/c6/T--UNOTT--colonyPCRofredoofgRNA3and5.jpeg"></center>
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    <td>Protein Concentration</td>
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    <td>ug / mol </td>
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  </tr>
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  <tr>
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    <td>mRNA Concentration</td>
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    <td>ug / mol </td>
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  </tr>
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  <tr>
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    <td>Fluorescence</td>
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    <td>RFU (Relative Fluorescence Unit) </td>
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  </tr>
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  <tr>
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    <td>Time</td>
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    <td>Hours / Hrs</td>
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   </tr>
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<p> Rates of change </p>  
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  <h3 class="box_header">WEEK 12</h3>
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<p><span style="color: #000000;"><br />The random ligation controls generated some unexpected results - the colours didn't line up with what we were expected so we did some sequencing with M13F and p15a_F1 to troubleshoot the issue. The result of this is that we suspect some contamination somewhere along the line, so we went back to construction. Unfortunately the primers selected were not able to sequence across the whole 3 bricks so we don't know what happened to these random ligations and thought it would be safest to backtrack.</span></p>
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    <th>Parameter</th>
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<p><span style="color: #000000;"><br />We set up 5ml overnights of the new gRNA 3 & 5 vectors. We extracted the plasmid from these cultures and sent them off for sequencing using gRNAv_F_seq and ColE1_F1. We also set up 5ml of the kit plate CFP, RFP and sfGFP transformants for re-construction of the bricks. We extracted plasmid from these the next day too.</span></p>
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    <th>Unit</th>  
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<p><span style="color: #000000;"><br />We set up overnights of pMTL83211 from the SBRC culture collection and purified this in the morning before digesting 4ug with NotI and XbaI. We also amplified P<sub>fdx</sub> donated by the lab with ? and ?. dCas9 was amplified ? and ?. dCas9 was run on a gel and purified from there, and P<sub>fdx</sub> was PCR purified.</span></p>
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<p><span style="color: #000000;"><br /> P<sub>fdx</sub> was then digested with XbaI and BsaI, while dCas9 was digested with BsaI and NotI at 37oC for 2 hours. The components of this new dCas9 strategy vector were ligated in a 1:5:3 (vector:P<sub>fdx</sub>:dCas9) ratio using 100ng of vector at 4oC overnight.</span></p>
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    <td>Protein Degradation</td>
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<p><span style="color: #000000;"><br />The new dCas9 strategy vector ligations were dialysed then transformed into TOP10 before recovery and plating on LB + Em plates and growth at 30oC for 2 days. This generated only 1 colony on the negative control plates each and around 100 on each of the test ligations.</span></p>
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    <td>Nanograms per microlitre per minute</td>
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  </div>
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    <td>mRNA Degradation</td>
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<div class="box">
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    <td>Nanograms per microlitre per minute</td>
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   <h3 class="box_header">WEEK 13</h3>
+
  </tr>
−
  <div class="box_content">
+
<tr>
−
<p><span style="color: #000000;"><br /><u>gRNA tests work</u></span></p>
+
    <td>Translation </td>
−
<p><span style="color: #000000;"><br /></span></p>
+
    <td>Number of protein molecules produced per mRNA molecule, per unit of time</td>
−
<p><span style="color: #000000;"><br />8 colonies were picked from the new dCas9 strategy vector transformation and screened using cas9_scr_R1 and pCB102_R1. If present, the plasmid would give a 1kb amplicon. PCR settings: 55oC annealing temperature and 1 minute extension time, 30 cycles.</span></p>
+
  </tr>
−
<center><img src="https://static.igem.org/mediawiki/2017/d/d1/T--UNOTT--newdCas9vectorempty.jpeg"></center>
+
<tr>
−
<p><span style="color: #000000;"><br />All colonies gave a amplicon of 1kb, which shows that all 8 colonies were positive. Colonies 1 and 3 were used to innoculate overnight cultures. Plasmids were extracted from overnight cultures and sequenced. Sequencing showed they were correct.</span></p>
+
    <td>Transcription </td>
−
<p><span style="color: #000000;"><br />Ellie digested all of the RFP bricks that Chris had re-made, as well as digesting the new dCas9 strategy vector with AscI and SalI overnight at 37oC. RFP brick digests were purified using a PCR cleanup kit, whereas the vector digest was run on a gel and the band at 7656bp was separated from the 261bp insert by gel extraction.</span></p>
+
    <td>Number of mRNA molecules produced per gene, per unit of time.</td>
−
<center><img src="https://static.igem.org/mediawiki/2017/a/a3/T--UNOTT--digestnewdCas9vectorempty.jpeg"></center>
+
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−
<p><span style="color: #000000;"><br />The digested RFP bricks were ligated into 50ng of vector in a 1:3 (vector:insert) ratio using 20ng of insert. Ligations were done using T4 DNA ligase (Promega) at room temperature for 3 hours before dialysis using Millipore filters, and transformation into electrocompetent TOP10. The electroporated cells were recovered at 30oC for 2 hours in SOC before plating on LB + Cm + Em plates and grown at 30oC over a weekend.</span></p>
+
−
<p><span style="color: #000000;"><br /><u>Random ligation work</u></span></p>
+
−
<p><span style="color: #000000;"><br /></span></p>
+
−
<center><img src="https://static.igem.org/mediawiki/2017/8/82/T--UNOTT--digestedpmtl83211ascisalichris.jpeg"></center>
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<center><img src="https://static.igem.org/mediawiki/2017/6/63/T--UNOTT--CFPbrickamplification.jpeg"></center>
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<center><img src="https://static.igem.org/mediawiki/2017/7/76/T--UNOTT--RFPbrickamplification.jpeg"></center>
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<div class="box">
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   <h3 class="box_header">WEEK 14</h3>
+
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  <div class="box_content">
+
−
<p><span style="color: #000000;"><br /></span></p>
+
−
<p><span style="color: #000000;"><br />The transformations of each RFP brick inside the new dCas9 vector resulted in 100s of colonies per plate, with only 10 on the negative control (backbone only ligation). We picked and screened 5 colonies from each transformations, as well as using the empty plasmid from before digestion as a control in the first lane. Primers used were: cas9_scr_R1 and pCB102_R1 which should give a product of around 1kb if empty, and 1.6kb if the RFP brick had been successfully inserted.</span></p>
+
−
<center><img src="https://static.igem.org/mediawiki/2017/2/2e/T--UNOTT--colonyPCRofdCas9plusRFPbricks.jpeg"></center>
+
−
<p><span style="color: #000000;"><br />The positive colonies corresponding to the lanes labelled with a red arrow were used to set up overnight cultures in 10ml of LB (+ chloramphenicol and erythromycin). Plasmids were extracted from these cultures and sequenced using cas9_scr_R1 and pCB102_R1. These plasmids will be called the reporter plasmids henceforth when referring to the CRISPRi tests. Overnights were also set up of all gRNA vectors (1, 2, 4, and 0 from -80oC storage, and 3 & 5 from the plates of the new ligations done recently. Chris made competent cells ready for transformations in the next few weeks.</span></p>
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−
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−
 
+
−
<div class="box">
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−
  <h3 class="box_header">WEEK 15</h3>
+
−
  <div class="box_content">
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−
<p><span style="color: #000000;"><br />100ng of one gRNA plasmid (targeting the promoter e.g. gRNA 4 targeting P<sub>4</sub>, or non-targeting gRNA 0) was co-transformed with 100ng of the corresponding reporter plasmid into TOP10 electocompetent cells by electroporation, recovered in SOC at 30oC for 2 hours, then plated onto LB with erythromycin (500µg/ml) and chloramphenicol (25µg/ml) and grown overnight at 30oC. 5 colonies were picked per transformation and resuspended in 10µl of water. 1µl of this resuspension was used for a colony PCR to confirm the presence of gRNA plasmid, and another 1µl used for confirmation of the reporter plasmid. The primers used and expected sizes are shown in the table below. Vik, the computer scientist, was convinced to learn how to load gels finally too! Learning new skills all round.</span></p>
+
−
<table width="918">
+
−
<tbody>
+
−
<tr>
+
−
<td><strong>Plasmid to detect</strong></td>
+
−
<td><strong>Primer 1</strong></td>
+
−
<td><strong>Primer 2</strong></td>
+
−
<td><strong>Product for sRFP plasmids (bp)</strong></td>
+
−
<td><strong>Product for PE sRFP plasmid (bp)</strong></td>
+
−
<td><strong>Product for wRFP plasmids (bp)</strong></td>
+
−
<td><strong>Product for PE wRFP plasmid (bp)</strong></td>
+
−
</tr>
+
−
<tr>
+
−
<td>gRNA vector</td>
+
−
<td>gRNAv_F_seq</td>
+
−
<td>ColE1_F1</td>
+
−
<td colspan="4">495</td>
+
−
</tr>
+
−
<tr>
+
−
<td>New design reporter plasmid</td>
+
−
<td>cas9_scr_R1</td>
+
−
<td>pCB102_R1</td>
+
−
<td>&nbsp;1647</td>
+
−
<td>1612</td>
+
−
<td>1649</td>
+
−
<td>1614</td>
+
−
</tr>
+
−
</tbody>
+
 
</table>
 
</table>
−
<center><img src="https://static.igem.org/mediawiki/2017/f/f9/T--UNOTT--viklearninggelloading.jpeg"></center>
 
−
<center><img src="https://static.igem.org/mediawiki/2017/thumb/9/91/T--UNOTT--colonyPCRofdoubletransformants.jpeg/800px-T--UNOTT--colonyPCRofdoubletransformants.jpeg"></center>
 
−
<center><img src="https://static.igem.org/mediawiki/2017/thumb/8/84/T--UNOTT--colonyPCRofdoubletransformantsweak.jpeg/800px-T--UNOTT--colonyPCRofdoubletransformantsweak.jpeg"></center>
 
−
<p><span style="color: #000000;"><br />These gels show that the vast majority of colonies were positive for both the gRNA plasmid and the reporter plasmid. We chose the colonies that represent the first 3 positive lanes containing each of the strongRBS-RFP and innoculated an overnight culture of 10ml of LB with erythromycin(500ug/ml) and chloramphenicol (25ug/ml) to maintain the plasmids.</span></p>
 
−
<p><span style="color: #000000;"><br />In the morning, the OD of these cultures were measured by photospectrometer and used to innoculate 15ml of fresh LB with Cm and Em to a starting OD of 0.02. OD and RFP fluorescence were measured from 100µl of each culture at 0, 2, 4, 6 and 24 hours after innoculation using a Clariostar (?) with the following settings (?). 3 biological replicates (3 colonies from each transformation) were measured, using duplicates for each to allow for technical errors. At 6h, some of the results went out of the range capable by the microplate reader (>260,000) so at the 24 hour timepoint the readings were done at a 1 in 5 dilution (20µl of culture mixed with 80ul LB in the 96 well plate). As the results at 6h had some replicates just below 260,000 we predict that these results that exceeded 260,000 were likely close to this value anyway. Due to this, we have included them in our results, however this must be considered during interpretation of the results.</span></p>
 
−
<p><span style="color: #000000;"><br />As the overnight cultures were not visually red, 200µl of a representative colony was spun down and supernatant removed to see whether a visual result can be seen at a higher cell density. The following picture shows these pellets.</span></p>
 
−
<center><img src="https://static.igem.org/mediawiki/2017/e/e1/T--UNOTT--photosofgRNAresults.jpeg"></center>
 
−
<p><span style="color:#000000;"><br />The image below shows colony resuspensions dotted onto LB+Cm+Em plates. Each quadrant contains the 5 colonies selected per transformation (a-e refers to the order of lanes in the colony PCR gel). Labelling of quadrants is as follows: the first number is the promoter attached to RFP, s/w corresponds to the strength of the RBS, and the second number is the gRNA plasmid that is present. An example is 1s1 where P<sub>1</sub> is attached to the strong RBS RFP in the reporter plasmid, and has been co-transformed with gRNA 1 plasmid. ? on colonies were those whose results were negative or less clear than others on the gel. Circled colonies were those selected to be used for storage of the plasmid at -80oC.</span></p>
 
−
<center><img src="https://static.igem.org/mediawiki/2017/thumb/1/1e/T--UNOTT--photosofdoubletransformants.jpeg/800px-T--UNOTT--photosofdoubletransformants.jpeg"></center>
 
  
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<p>When developing <i> Key. coli</i>, we found it was important to mathematically model possible situations in order to investigate the effects of different situations that we might encounter throughout different stages of development as well as during implementation. </p>
 
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<p> Software was developed to compare the fluorescence levels of the key colony with the mother colony to check if there was a high enough degree of similarity.</p>
 
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<p> This information was used by the wet lab to assist them by informing them in what to expect. This was done through the use of programming to create visual graphs and simulations, as well as development of tools to allow for comparison between fluorescence levels without needing to actually create more synthetic organisms. Another advantage is that this is far quicker than creating these results in the lab. </p>
 
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<a href="https://github.com/BurgundyIsAPublicEnemy/iGEMNotts2017/blob/master/Tellurium%20-%20Python%20Models%20for%20IGEM%20NOTTS%202017">The source code for these models can be accessed from our GitHub page</a>
 
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The models were not perfect at first: refinement from lab results helped to optimize and correct the models.</h5>
 
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<p>In order to achieve these aims, we created a simulation for measuring fluorescence intensity when given parameters such as protein concentrations and wavelengths of lasers. </li>
 
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Latest revision as of 02:29, 2 November 2017

</DIV>



Modelling    

 

 

Overview

When developing Key. coli, we found it was important to mathematically model possible situations in order to investigate the effects of different situations that we might encounter throughout different stages of development as well as during implementation.

A major problem the project faced is that the comparison process of the fluorescence proteins wouldn't be possible to be investigated with all combinations as it would take too long.

To answer this problem, the team attempted to model the fluorescence spectra over time expressed by each different protein. First, the type of gene expression was identified and then, the model was refined to take into account gene inhibition (whether the gene is expressed or not) and finally, applied over time to see how much expression would occur at a certain time period. The team used mathematical modelling such as Ordinary Differential Equations because they were easy to convert into programming in order to build components for the simulation.

This information was used by the wet lab to assist them by informing them in what to expect. This was done through the use of programming to create visual graphs and simulations, as well as development of tools to allow for comparison between fluorescence levels without needing to actually create more synthetic organisms. One advantage of this was it allowed for data to be easily read and understood by the team, rather than reading a wall of numbers. Another advantage is that this is far quicker than creating these results in the lab.

One limitation of models the team found out that they were too high level to accurately predict and represent all the processes that would be undertaken during the random constructions of the fluorescent proteins. This is an issue because this means the models weren't perfect to describe the real life, which however, suggests, they could undergo more refining and improving.

Software was developed to compare the fluorescence levels of the key colony with the mother colony to check if there was a high enough degree of similarity. The mother colony is defined as the colony of bacteria that is securely kept within the facility and whose fluorescence acts as a verification for the key colony, which is defined as the bacteria which is taken from the mother colony and given to a person who own's a Key.Coli container.

As a side project, the team investigated into whether our method is random and unique by investigating how many combinations we could make and whether we could accurately predict which combination will occur.

Modelling Aims

The first aim is to assist the processes within the wet lab by informing them of what spectra they can expect through the use of simulations. This would be especially useful when predicting the required fluorescence

The second is to test our biological systems with conditions that might not be possible to replicate in a lab environment. This allows us to future proof our methods as well as identify any vulnerabilities further down the line as well as save time and money on testing these conditions, if we were to.

In order to achieve these aims, we decided on an end goal of writing a Simulation for measuring Fluorescence Intensity when given parameters such as protein concentrations and wavelengths of lasers.



Find out more about our modeling

The source code for these models can be accessed from our GitHub page

Software Aims

The main objective of software was to check between fluorescence levels during implementation of Key.Coli between the mother colony and the Key.Coli capsules.

In order to achieve this objective, the team decided to investigate into image comparison and raw data comparison with data from the fluorescence reader. These were programmed using data from the models and a threshold value was set using data from the wet lab to ensure not just any file being compared would get verified.

A strong emphasis was placed on effectiveness of the software and efficiency; we wanted the software to run on low end hardware but still be useful without compromises.



Find out more about our software

Our software can be downloaded from our GitHub page

Parameters

Inputs and Outputs

Parameter Unit
Protein Concentration ug / mol
mRNA Concentration ug / mol
Fluorescence RFU (Relative Fluorescence Unit)
Time Hours / Hrs

Rates of change

Parameter Unit
Protein Degradation Nanograms per microlitre per minute
mRNA Degradation Nanograms per microlitre per minute
Translation Number of protein molecules produced per mRNA molecule, per unit of time
Transcription Number of mRNA molecules produced per gene, per unit of time.