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Team:Newcastle University/Mark's Lab Journal
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MarkWappett (Talk | contribs) (New page: '''16/05/2008''' Following Thursdays meeting I have been researching into Quorum sensing peptides that are unique to Staphylococcus aureus. After consulting the literature I have noted th...) |
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At this point I went on to retrieve the sequences using the NCBI and EBI databases. | At this point I went on to retrieve the sequences using the NCBI and EBI databases. | ||
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| + | Carried out the Blast analysis of the different agrC groups. There are very small differences between the agrC protein sequences in Staphylococcus aureus, however the differences are much greater between Staphylococcus aureus species and other Staph species, namely epidermis and haemolyticus. As a result the agrC protein could well be used as a receptor for the agrD receptor proteins. | ||
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| + | The alignment for agrD contains group I, III and IV agrD peptides. The alignment for the sensory protein in agrC contains three peptides from agrC and two from Staphylococcus haemolyticus and Staphylococcus epidermis. The differences mentioned above are documented. | ||
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| + | Have started looking at Listeria monocytogenes. It is a gram-positive human pathogenic bacterium and the cause of listeriosis a serious infection characterized by high mortality rates in immunocompromised individuals and pregnant women. It is often involved in food poisoning and although rare results in a mortality rate of over 25% compared to Salmonella which only has a mortality rate of 1%. The bacteria uses a similar two-component system to Staphylococcus aureus for its quorum sensing signalling system, the agr system. | ||
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| + | I am now going to blast the genomic sequence data I have found on the agr system for Listeria. The results for the Listeria agrC protein are promising as it is distincly different to any agrC present in any other species (including importantly Staphylococcus aureus). The result is similar for the agrD protein. The results are listed below: | ||
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| + | I am now moving on to look at the neural network training data, and to incorporate some noise into the network. I want to use multiple instances and inputs to map to a single output, and see how the network trains the data. Using the Emergent software I created 8 inputs, 25 hidden layer nodes and mapped it all to a single output. As specified the data was extremely noisy as the graphical output showed. I specified first 10 batches, and then 50 batches each to run through until the error reached zero. This enables the demonstration of network learning. As the network learns, the length of time and the number of epoch required to run through the data to reach zero is significantly reduced, even in a network filled with noisy data. | ||
Revision as of 15:20, 11 July 2008
16/05/2008
Following Thursdays meeting I have been researching into Quorum sensing peptides that are unique to Staphylococcus aureus. After consulting the literature I have noted the presence of four Quorum sensing systems. They are agr, sae, artRS and SrrAB. The relevant papers are linked to below. Previously I discovered that there were four different autoinducing peptides in Staphylococcus aureus. However it has now become clear that these are indeed unique to S. aureus, and are not present (in the same sequence) in any other member of the Staph species. The agrC receptors also have different corresponding conformational groups. It may be possible to use all four AIP's and agrC's as a S. aureus biosensor.
At this point I went on to retrieve the sequences using the NCBI and EBI databases.
19/05/2008
Carried out the Blast analysis of the different agrC groups. There are very small differences between the agrC protein sequences in Staphylococcus aureus, however the differences are much greater between Staphylococcus aureus species and other Staph species, namely epidermis and haemolyticus. As a result the agrC protein could well be used as a receptor for the agrD receptor proteins.
The alignment for agrD contains group I, III and IV agrD peptides. The alignment for the sensory protein in agrC contains three peptides from agrC and two from Staphylococcus haemolyticus and Staphylococcus epidermis. The differences mentioned above are documented.
Have started looking at Listeria monocytogenes. It is a gram-positive human pathogenic bacterium and the cause of listeriosis a serious infection characterized by high mortality rates in immunocompromised individuals and pregnant women. It is often involved in food poisoning and although rare results in a mortality rate of over 25% compared to Salmonella which only has a mortality rate of 1%. The bacteria uses a similar two-component system to Staphylococcus aureus for its quorum sensing signalling system, the agr system.
I am now going to blast the genomic sequence data I have found on the agr system for Listeria. The results for the Listeria agrC protein are promising as it is distincly different to any agrC present in any other species (including importantly Staphylococcus aureus). The result is similar for the agrD protein. The results are listed below:
I am now moving on to look at the neural network training data, and to incorporate some noise into the network. I want to use multiple instances and inputs to map to a single output, and see how the network trains the data. Using the Emergent software I created 8 inputs, 25 hidden layer nodes and mapped it all to a single output. As specified the data was extremely noisy as the graphical output showed. I specified first 10 batches, and then 50 batches each to run through until the error reached zero. This enables the demonstration of network learning. As the network learns, the length of time and the number of epoch required to run through the data to reach zero is significantly reduced, even in a network filled with noisy data.
