Team:Newcastle University/Conclusions

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== Results ==
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===Conclusions===
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[[Image:Montage.jpg|thumb|right|300px|Microscopy results of iGEMgfp upon 1% induction by subtilin]]
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The major outcome of the project is our demonstration that the basic biological function on which the whole project depends - moving two-component quorum-sensing systems from one strain of ''Bacillus'' to another strain can be achieved, and the two-component system can continue to function as it does in the original strain. We have decoupled the production and detection of quorum sensing peptides. Although we didn't achieve our overall goal of evolving an ANN and implementing it in ''B. subtilis'', this a major step towards the achievement of out original goals.
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[[Image:Montage_pc.jpg|thumb|right|300px|Microscopy results of iGEMcherry upon 1% induction by subtilin]]
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[[Image:Flow0%.jpg|thumb|right|300px|Flow cytometry results for 0% induction by subtilin.]]
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[[Image:Flow1%.jpg|thumb|right|300px|Flow cytometry results for 1% induction by subtilin.]]
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[[Image:Flow10%.jpg|thumb|right|300px|Flow cytometry results for 10% induction by subtilin.]]
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To analyse the results of the wet lab transformations of the inserts into ''B. subtilis'', we used two methods: microscopy and flow cytometry.
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The preliminary work involved identifying suitable target pathogens, the quorum communication peptides produced by them, and the amount of overlap between peptides produced by different species. This took much longer than anticipated, and involved a lot of searching the literature and online databases, but resulted in a database of species and peptides which is a good training set for the system.
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===Microscopy===
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Software development also took up a lot of our time, since all of the code had to be developed from scratch, and this could not be done without learning a lot of skills, including the principles of neural networks and evolutionary algorithms, advanced Java, CellML and JSim, databases and JDBC and Web services. The fact that the software works and the different parts communicate with each other is very rewarding, and the software may be a valuable resource for future iGEM teams, if they choose to work in this area.
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Microscopy work from 08.09.08 showed a difference in the level of flourescence of the iGEMgfp fluorescent cells (higher in 10% subtilin-induced cells compared to 0% subtilin-induced cells). However, there was little difference in the ''number'' of cells that fluoresced between the two cultures.  
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The lab work was also a positive experience, as we managed to clone the proof of concept BioBrick into an integration vector, and get it to integrate into the chromosome of the ''Bacillus''. Once again, this required the acquisition of a host of new skills, both at the bench and microscopy, flow cytometry and, for some of us, computing and the use of Wikis.
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There was no difference in the number of fluorecent cells ''or'' the level of flourescence between the 10% subtilin-induced and the 0% subtilin-induced iGEMcherry cells.
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Overall, the 2008 Newcastle iGEM team had a lot of fun, learned a lot of skills, and made (hopefully useful) contributions to the BioBricks Repository, as well as local databases and software tools. Jamboree, here we come!
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===Future Work===
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===Flow cytometry===
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* The ''Bacillus'' chromosomal integration vector we used is not BioBrick compatible. It may be possible to modify it so that it is, or provide simple and reliable protocols to transfer a BioBrick part from compatible vectors into this one.
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* Need to further characterize the subtilin Brick part. We started to do this with the flow cytometry and microscopy, but more data-points would make simulations more robust.
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Flow cytometry allows us to quantify our results and present them in graphical form.  A sample of cells our engineered Bacillus subtilis cells were injected into the machine which hydro-dynamically focusses the fluid. Lasers of a particular wavelength are directed onto the stream of fluid, and each particle which passes through the light beam will cause the laser to scatter in a different way. Cells which absorb and then re-emit the light, result in fluorescence (a higher energy state).
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* Extend to the full original plan. We have demonstrated that extra-cellular quorum peptides can be sensed and that they can control the expression of reporter genes. The next step would be to implement a stripped-down bacterial neural network, and finally to try out one of the full solutions produced by the evolutionary computation.
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The detectors in the machine measure the scattering of light and any flourescence which occurs, and the data is displayed as histograms and dot plots.
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'''0% induction by subtilin''' (i.e in the absence of subtilin): mean flourescence = 7.70
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'''1% induction by subtilin:''' mean flourescence = 14.77
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'''10% induction by subtilin:''' the mean flourescence = 21.95
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These results show that the higher the concentraion of subtilin, the more GFP is expressed.
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At 10% induction, the subtilin began to cause some of the cells to lyse- this can be seen by the two distinct populations of cells- one being the cells which have lysed, and the other being the remainder of intact cells. For us, this was a good indication that the subtilin we had collected that day was good quality.
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Latest revision as of 21:37, 29 October 2008

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Newcastle University

GOLD MEDAL WINNER 2008

Home Team Original Aims Software Modelling Proof of Concept Brick Wet Lab Conclusions


Home >> Conclusions

Conclusions

The major outcome of the project is our demonstration that the basic biological function on which the whole project depends - moving two-component quorum-sensing systems from one strain of Bacillus to another strain can be achieved, and the two-component system can continue to function as it does in the original strain. We have decoupled the production and detection of quorum sensing peptides. Although we didn't achieve our overall goal of evolving an ANN and implementing it in B. subtilis, this a major step towards the achievement of out original goals.

The preliminary work involved identifying suitable target pathogens, the quorum communication peptides produced by them, and the amount of overlap between peptides produced by different species. This took much longer than anticipated, and involved a lot of searching the literature and online databases, but resulted in a database of species and peptides which is a good training set for the system.

Software development also took up a lot of our time, since all of the code had to be developed from scratch, and this could not be done without learning a lot of skills, including the principles of neural networks and evolutionary algorithms, advanced Java, CellML and JSim, databases and JDBC and Web services. The fact that the software works and the different parts communicate with each other is very rewarding, and the software may be a valuable resource for future iGEM teams, if they choose to work in this area.

The lab work was also a positive experience, as we managed to clone the proof of concept BioBrick into an integration vector, and get it to integrate into the chromosome of the Bacillus. Once again, this required the acquisition of a host of new skills, both at the bench and microscopy, flow cytometry and, for some of us, computing and the use of Wikis.

Overall, the 2008 Newcastle iGEM team had a lot of fun, learned a lot of skills, and made (hopefully useful) contributions to the BioBricks Repository, as well as local databases and software tools. Jamboree, here we come!

Future Work

  • The Bacillus chromosomal integration vector we used is not BioBrick compatible. It may be possible to modify it so that it is, or provide simple and reliable protocols to transfer a BioBrick part from compatible vectors into this one.
  • Need to further characterize the subtilin Brick part. We started to do this with the flow cytometry and microscopy, but more data-points would make simulations more robust.
  • Extend to the full original plan. We have demonstrated that extra-cellular quorum peptides can be sensed and that they can control the expression of reporter genes. The next step would be to implement a stripped-down bacterial neural network, and finally to try out one of the full solutions produced by the evolutionary computation.